1
|
Li D, Chu B, Li B, Wang X, Chen X, Gu Q. The difference analysis of physicochemical indexes and volatile flavor compounds of chili oil prepared from different varieties of chili pepper. Food Res Int 2024; 190:114657. [PMID: 38945630 DOI: 10.1016/j.foodres.2024.114657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/15/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Because of its peculiar flavor, chili oil is widely used in all kinds of food and is welcomed by people. Chili pepper is an important raw material affecting its quality, and commercial chili oil needs to meet various production needs, so it needs to be made with different chili peppers. However, the current compounding method mainly relies on the experience of professionals and lacks the basis of objective numerical analysis. In this study, the chroma and capsaicinoids of different chili oils were analyzed, and then the volatile components were determined by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-ion migration spectrometer (GC-IMS) and electronic nose (E-nose). The results showed that Zidantou chili oil had the highest L*, b*, and color intensity (ΔE) (52.76 ± 0.52, 88.72 ± 0.89, and 118.84 ± 1.14), but the color was tended to be greenyellow. Xinyidai chili oil had the highest a* (65.04 ± 0.2). But its b* and L* were relatively low (76.17 ± 0.29 and 45.41 ± 0.16), and the oil was dark red. For capsaicinoids, Xiaomila chili oil had the highest content of capsaicinoids was 2.68 ± 0.07 g/kg, Tianjiao chili oil had the lowest content of capsaicinoids was 0.0044 ± 0.0044 g/kg. Besides, 96 and 54 volatile flavor substances were identified by GC-MS and GC-IMS respectively. And the main volatile flavor substances of chili oil were aldehydes, alcohols, ketones, and esters. A total of 11 key flavor compounds were screened by the relative odor activity value (ROAV). Moguijiao chili oil and Zidantou chili oil had a prominent grass aroma because of hexanal, while Shizhuhong chili oil, Denglongjiao chili oil, Erjingtiao chili oil, and Zhoujiao chili oil had a prominent floral aroma because of 2, 3-butanediol. Chili oils could be well divided into 3 groups by the partial least squares discriminant analysis (PLS-DA). According to the above results, the 10 kinds of chili oil had their own characteristics in color, capsaicinoids and flavor. Based on quantitative physicochemical indicators and flavor substances, the theoretical basis for the compounding of chili oil could be provided to meet the production demand more scientifically and accurately.
Collapse
Affiliation(s)
- Dingding Li
- Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Anhui Wangxiaolu Food Technology Company Limited, Anhui 239000, PR China; Beijing Wangxiaolu Network Technology Company Limited, Beijing 100000, PR China
| | - Beibei Chu
- Anhui Wangxiaolu Food Technology Company Limited, Anhui 239000, PR China; Beijing Wangxiaolu Network Technology Company Limited, Beijing 100000, PR China
| | - Bo Li
- Langfang Customs of the People's Republic of China, PR China
| | - Xiong Wang
- Anhui Wangxiaolu Food Technology Company Limited, Anhui 239000, PR China; Beijing Wangxiaolu Network Technology Company Limited, Beijing 100000, PR China
| | - Xingguang Chen
- Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; School of Food Science and Engineering, Hefei University of Technology, Hefei 230009, PR China.
| | - Qianhui Gu
- Anhui Wangxiaolu Food Technology Company Limited, Anhui 239000, PR China; Beijing Wangxiaolu Network Technology Company Limited, Beijing 100000, PR China; School of Food Science and Engineering, Hefei University of Technology, Hefei 230009, PR China.
| |
Collapse
|
2
|
Gohel VR, Chetyrkina M, Gaev A, Simonenko NP, Simonenko TL, Gorobtsov PY, Fisenko NA, Dudorova DA, Zaytsev V, Lantsberg A, Simonenko EP, Nasibulin AG, Fedorov FS. Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses. LAB ON A CHIP 2024. [PMID: 39016307 DOI: 10.1039/d4lc00252k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.
Collapse
Affiliation(s)
- Vishalkumar Rajeshbhai Gohel
- Laboratory of Nanomaterials, Skolkovo Institute of Science and Technology, 3 Nobel Str, Moscow, 121205, Russian Federation.
| | - Margarita Chetyrkina
- Laboratory of Nanomaterials, Skolkovo Institute of Science and Technology, 3 Nobel Str, Moscow, 121205, Russian Federation.
| | - Andrey Gaev
- Bauman Moscow State Technical University, 5/1 Baumanskaya 2-ya Str, Moscow, 105005, Russian Federation
| | - Nikolay P Simonenko
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Tatiana L Simonenko
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Philipp Yu Gorobtsov
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Nikita A Fisenko
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Darya A Dudorova
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Valeriy Zaytsev
- Laboratory of Nanomaterials, Skolkovo Institute of Science and Technology, 3 Nobel Str, Moscow, 121205, Russian Federation.
| | - Anna Lantsberg
- Bauman Moscow State Technical University, 5/1 Baumanskaya 2-ya Str, Moscow, 105005, Russian Federation
| | - Elizaveta P Simonenko
- Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences, 31 Leninsky pr, Moscow, 119991, Russian Federation
| | - Albert G Nasibulin
- Laboratory of Nanomaterials, Skolkovo Institute of Science and Technology, 3 Nobel Str, Moscow, 121205, Russian Federation.
| | - Fedor S Fedorov
- Laboratory of Nanomaterials, Skolkovo Institute of Science and Technology, 3 Nobel Str, Moscow, 121205, Russian Federation.
| |
Collapse
|
3
|
Liu J, Sun R, Bao X, Yang J, Chen Y, Tang B, Liu Z. Machine Learning Driven Atom-Thin Materials for Fragrance Sensing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2401066. [PMID: 38973110 DOI: 10.1002/smll.202401066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/05/2024] [Indexed: 07/09/2024]
Abstract
Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.
Collapse
Affiliation(s)
- Juanjuan Liu
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Ruijia Sun
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Xuan Bao
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Jiefu Yang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yanling Chen
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Bijun Tang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zheng Liu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| |
Collapse
|
4
|
Shi L, Tang P, Hu J, Zhang Y. A Strategy for Multigas Identification Using Multielectrical Parameters Extracted from a Single Carbon-Based Field-Effect Transistor Sensor. ACS Sens 2024; 9:3126-3136. [PMID: 38843033 DOI: 10.1021/acssensors.4c00357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Given the widespread utilization of gas sensors across various industries, the detection of diverse and complex target gases presents a significant challenge in designing sensors with multigas detection capability. Although constructing a sensor array with widely used chemiresistive gas sensors is one solution, it is difficult for a single chemiresistive gas sensor to simultaneously detect different gases, as it can only detect a single target gas. The intrinsic reason for this bottleneck is that chemiresistive gas sensors rely entirely on the resistivity as the unique parameter to evaluate the diverse gas sensing properties of sensors, such as sensitivity, selectivity, etc. Herein, a field-effect transistor (FET) with abundant electrical parameters is employed to prepare a gas sensor for the detection of a variety of gases. Semiconducting carbon nanotubes (CNTs) are selected as the channel material, which is modified by Pd nanoparticles to enhance the gas sensing properties of the sensors. By extracting various electrical parameters such as transconductance, threshold voltage, etc. from the transfer characteristic curves of FET, a correlation between multielectrical parameters and various gas detection information is established for subsequent data analysis. Through the utilization of the principal component analysis algorithm, the identification of six gases can be finally achieved by relying solely on a single carbon-based FET-type gas sensor. We hope our work can solve the bottleneck of multigas identification by a single sensor in principle and is expected to reduce the system complexity and cost caused by the design of sensor arrays, offering a valuable guidance for multigas identification technology.
Collapse
Affiliation(s)
- Lin Shi
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, PR China
| | - Pinghua Tang
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, PR China
| | - Jinyong Hu
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, PR China
| | - Yong Zhang
- School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, PR China
- Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan 411105, PR China
| |
Collapse
|
5
|
Rocco G, Pennazza G, Tan KS, Vanstraelen S, Santonico M, Corba RJ, Park BJ, Sihag S, Bott MJ, Crucitti P, Isbell JM, Ginsberg MS, Weiss H, Incalzi RA, Finamore P, Longo F, Zompanti A, Grasso S, Solomon SB, Vincent A, McKnight A, Cirelli M, Voli C, Kelly S, Merone M, Molena D, Gray K, Huang J, Rusch VW, Bains MS, Downey RJ, Adusumilli PS, Jones DR. A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology. J Thorac Oncol 2024:S1556-0864(24)00211-9. [PMID: 38762120 DOI: 10.1016/j.jtho.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
INTRODUCTION Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.
Collapse
Affiliation(s)
- Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Giorgio Pennazza
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marco Santonico
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Robert J Corba
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Bernard J Park
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Smita Sihag
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew J Bott
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pierfilippo Crucitti
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - James M Isbell
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michelle S Ginsberg
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hallie Weiss
- Department of Anesthesiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Raffaele Antonelli Incalzi
- Department of Geriatrics, Research Unit of Internal Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Panaiotis Finamore
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Filippo Longo
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Alessandro Zompanti
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Simone Grasso
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Stephen B Solomon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alain Vincent
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexa McKnight
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael Cirelli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Carmela Voli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Susan Kelly
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mario Merone
- Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Daniela Molena
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gray
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Huang
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Valerie W Rusch
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manjit S Bains
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Robert J Downey
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
6
|
Okur S, Hashem T, Bogdanova E, Hodapp P, Heinke L, Bräse S, Wöll C. Optimized Detection of Volatile Organic Compounds Utilizing Durable and Selective Arrays of Tailored UiO-66-X SURMOF Sensors. ACS Sens 2024; 9:622-630. [PMID: 38320750 PMCID: PMC10898453 DOI: 10.1021/acssensors.3c01575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/27/2023] [Accepted: 01/05/2024] [Indexed: 02/24/2024]
Abstract
Metal-organic frameworks (MOFs), with their well-defined and highly flexible nanoporous architectures, provide a material platform ideal for fabricating sensors. We demonstrate that the efficacy and specificity of detecting and differentiating volatile organic compounds (VOCs) can be significantly enhanced using a range of slightly varied MOFs. These variations are obtained via postsynthetic modification (PSM) of a primary framework. We alter the original MOF's guest adsorption affinities by incorporating functional groups into the MOF linkers, which yields subtle changes in responses. These responses are subsequently evaluated by using machine learning (ML) techniques. Under severe conditions, such as high humidity and acidic environments, sensor stability and lifespan are of utmost importance. The UiO-66-X MOFs demonstrate the necessary durability in acidic, neutral, and basic environments with pH values ranging from 2 to 11, thus surpassing most other similar materials. The UiO-66-NH2 thin films were deposited on quartz-crystal microbalance (QCM) sensors in a high-temperature QCM liquid cell using a layer-by-layer pump method. Three different, highly stable surface-anchored MOFs (SURMOFs) of UiO-66-X obtained via the PSM approach (X: NH2, Cl, and N3) were employed to fabricate arrays suitable for electronic nose applications. These fabricated sensors were tested for their capability to distinguish between eight VOCs. Data from the sensor array were processed using three distinct ML techniques: linear discriminant (LDA), nearest neighbor (k-NN), and neural network analysis methods. The discrimination accuracies achieved were nearly 100% at high concentrations and over 95% at lower concentrations (50-100 ppm).
Collapse
Affiliation(s)
- Salih Okur
- Karlsruhe
Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Tawheed Hashem
- Karlsruhe
Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Evgenia Bogdanova
- Karlsruhe
Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Patrick Hodapp
- Karlsruhe
Institute of Technology (KIT), Institute for Biological Interfaces
3–Soft Matter Synthesis Laboratory (IBG3–SML), Kaiserstrasse 12, 76131 Karlsruhe, Germany
| | - Lars Heinke
- Karlsruhe
Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Stefan Bräse
- Karlsruhe
Institute of Technology (KIT), Institute of Organic Chemistry (IOC), Kaiserstrasse 12,, 76131 Karlsruhe, Germany
- Karlsruhe
Institute of Technology (KIT), Institute of Biological and Chemical
Systems–Functional Molecular Systems (IBCS–FMS), Kaiserstrasse 12, 76131 Karlsruhe, Germany
| | - Christof Wöll
- Karlsruhe
Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| |
Collapse
|
7
|
Liu L, Na N, Yu J, Zhao W, Wang Z, Zhu Y, Hu C. Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305639. [PMID: 38095453 PMCID: PMC10870059 DOI: 10.1002/advs.202305639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 10/24/2023] [Indexed: 02/17/2024]
Abstract
As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature-rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmental surveillance.
Collapse
Affiliation(s)
- Luzheng Liu
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| | - Na Na
- Key Laboratory of RadiopharmaceuticalsMinistry of EducationCollege of ChemistryBeijing Normal UniversityBeijing100875China
| | - Jichuan Yu
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| | - Wenxiang Zhao
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| | - Ze Wang
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| | - Yu Zhu
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| | - Chuxiong Hu
- State Key Laboratory of TribologyDepartment of Mechanical EngineeringTsinghua UniversityBeijing100084China
| |
Collapse
|
8
|
Wang J, Zhou Z, Luo Y, Xu T, Xu L, Zhang X. Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis. Anal Chem 2024; 96:381-387. [PMID: 38154078 DOI: 10.1021/acs.analchem.3c04383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.
Collapse
Affiliation(s)
- Jing Wang
- College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong 518060, P. R. China
| | - Zhongzeng Zhou
- College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong 518060, P. R. China
| | - Yong Luo
- College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong 518060, P. R. China
| | - Tailin Xu
- College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong 518060, P. R. China
| | - Long Xu
- Department of Gastroenterology and Hepatology, Shenzhen University General Hospital, Shenzhen, Guangdong 518060, P. R. China
| | - Xueji Zhang
- College of Chemistry and Environmental Engineering, The Institute for Advanced Study (IAS), Shenzhen University, Shenzhen, Guangdong 518060, P. R. China
| |
Collapse
|
9
|
Zain M, Ma H, Ur Rahman S, Nuruzzaman M, Chaudhary S, Azeem I, Mehmood F, Duan A, Sun C. Nanotechnology in precision agriculture: Advancing towards sustainable crop production. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 206:108244. [PMID: 38071802 DOI: 10.1016/j.plaphy.2023.108244] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 02/15/2024]
Abstract
Nanotechnology offers many potential solutions for sustainable agroecosystem, including improvement in nutrient use efficiency, efficacy of pest management, and minimizing the adverse environmental effects of agricultural production. Herein, we first highlighted the integrated application of nanotechnology and precision agriculture for sustainable productivity. Application of nanoparticle mediated material and advanced biosensors in precision agriculture is only possible by nanochips or nanosensors. Nanosensors offers the measurement of various stresses, soil quality parameters and detection of heavy metals along with the enhanced data collection, enabling precise decision-making and resource management in agricultural systems. Nanoencapsulation of conventional chemical fertilizers (known as nanofertilizers), and pesticides (known as nanopesticides) helps in sustained and slow release of chemicals to soils and results in precise dosage to plants. Further, nano-based disease detection kits are popular tools for early and speedy detection of viral diseases. Many other innovative approaches including biosynthesized nanoparticles have been evaluated and proposed at various scales, but in fact there are some barriers for practical application of nanotechnology in soil-plant system, including safety and regulatory concerns, efficient delivery at field levels, and consumer acceptance. Finally, we outlined the policy options and actions required for sustainable agricultural productivity, and proposed various research pathways that may help to overcome the upcoming challenges regarding practical implications of nanotechnology.
Collapse
Affiliation(s)
- Muhammad Zain
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Crop Cultivation and Physiology of Jiangsu Province, College of Agriculture, Yangzhou University, Yangzhou, 225009, China
| | - Haijiao Ma
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Crop Cultivation and Physiology of Jiangsu Province, College of Agriculture, Yangzhou University, Yangzhou, 225009, China
| | - Shafeeq Ur Rahman
- Water Science and Environmental Engineering Research Center, College of Chemical and Environmental Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Md Nuruzzaman
- Faculty of Agriculture, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh
| | - Sadaf Chaudhary
- Department of Botany, University of Agriculture Faisalabad, Faisalabad, 38000, Pakistan
| | - Imran Azeem
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation and College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China
| | - Faisal Mehmood
- Key Laboratory of Crop Water Use and Regulation, Farmland Irrigation Research Institute, Chinese Academy of Agriculture Sciences, Ministry of Agriculture and Rural Affairs, Xinxiang, 453003, China; Department of Land and Water Management, Faculty of Agricultural Engineering, Sindh Agriculture University, Tandojam, 70060, Pakistan
| | - Aiwang Duan
- Key Laboratory of Crop Water Use and Regulation, Farmland Irrigation Research Institute, Chinese Academy of Agriculture Sciences, Ministry of Agriculture and Rural Affairs, Xinxiang, 453003, China
| | - Chengming Sun
- Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Key Laboratory of Crop Cultivation and Physiology of Jiangsu Province, College of Agriculture, Yangzhou University, Yangzhou, 225009, China.
| |
Collapse
|
10
|
Sun X, Yu Y, Saleh ASM, Yang X, Ma J, Gao Z, Zhang D, Li W, Wang Z. Characterization of aroma profiles of chinese four most famous traditional red-cooked chickens using GC-MS, GC-IMS, and E-nose. Food Res Int 2023; 173:113335. [PMID: 37803645 DOI: 10.1016/j.foodres.2023.113335] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 10/08/2023]
Abstract
The aroma profile of the four most popular types of red-cooked chickens in China was analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS), and electronic nose (E-nose). Principal component analysis (PCA) demonstrated that the E-nose could successfully distinguish between the four types of red-cooked chickens. Additionally, a fingerprint was created using GC-IMS to examine the variations in volatile organic compounds (VOCs) distribution in the four chicken types. A total number of 84 and 62 VOCs were identified in the four types of red-cooked chickens using GC-MS and GC-IMS, respectively. Odor activity value (OAV) showed that 1-octen-3-ol, heptanal, hexanal, nonanal, octanal, eugenol, dimethyl trisulfide, anethole, anisaldehyde, estragole, and eucalyptol were the key volatile components in all samples. Furthermore, partial least squares-discriminant analysis (PLS-DA) demonstrated that (E, E)-2,4-decadienal, dimethyl trisulfide, octanal, eugenol, hexanal, (E)-2-nonenal, 1-octen-3-ol, butanal, ethyl acetate, ethyl acetate (D), nonanal, and heptanal could be used as markers to distinguish aroma of the four types of red-cooked chickens. Also, it is worth noting that levels of VOCs varied between chicken breast muscle and skin. The obtained results offer theoretical and technological support for flavor identification and control in red-cooked chickens to enhance their quality and encourage consumer consumption, which will be advantageous for the red-cooked chicken production chain.
Collapse
Affiliation(s)
- Xiangxiang Sun
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China; College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China
| | - Yumei Yu
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Ahmed S M Saleh
- Department of Food Science and Technology, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
| | - Xinyu Yang
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Jiale Ma
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Ziwu Gao
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Dequan Zhang
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China
| | - Wenhao Li
- College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China.
| | - Zhenyu Wang
- Key Laboratory of Agro-Products Processing, Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Beijing 100193, China.
| |
Collapse
|
11
|
Okolo CA, Kilcawley KN, O'Connor C. Recent advances in whiskey analysis for authentication, discrimination, and quality control. Compr Rev Food Sci Food Saf 2023; 22:4957-4992. [PMID: 37823807 DOI: 10.1111/1541-4337.13249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 08/29/2023] [Accepted: 09/12/2023] [Indexed: 10/13/2023]
Abstract
In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.
Collapse
Affiliation(s)
- Chioke A Okolo
- FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Food Science & Environmental Health, Technological University Dublin, Dublin, Ireland
| | - Kieran N Kilcawley
- Food Quality & Sensory Science Department, Teagasc Food Research Centre, Co Cork, Ireland
- School of Food and Nutritional Sciences, College of Science, Engineering and Food Science, University College Cork, Cork, Ireland
| | - Christine O'Connor
- School of Food Science & Environmental Health, Technological University Dublin, Dublin, Ireland
| |
Collapse
|
12
|
Poļaka I, Mežmale L, Anarkulova L, Kononova E, Vilkoite I, Veliks V, Ļeščinska AM, Stonāns I, Pčolkins A, Tolmanis I, Shani G, Haick H, Mitrovics J, Glöckler J, Mizaikoff B, Leja M. The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis. Diagnostics (Basel) 2023; 13:3355. [PMID: 37958251 PMCID: PMC10648537 DOI: 10.3390/diagnostics13213355] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.
Collapse
Affiliation(s)
- Inese Poļaka
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Department of Modelling and Simulation, Riga Technical University, LV-1048 Riga, Latvia
| | - Linda Mežmale
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1038 Riga, Latvia
- Faculty of Residency, Riga Stradins University, LV-1007 Riga, Latvia
- Health Centre 4, LV-1012 Riga, Latvia;
| | - Linda Anarkulova
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Faculty of Residency, Riga Stradins University, LV-1007 Riga, Latvia
- Health Centre 4, LV-1012 Riga, Latvia;
- Liepaja Regional Hospital, LV-3414 Liepaja, Latvia
| | - Elīna Kononova
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Faculty of Medicine, Riga Stradins University, LV-1007 Riga, Latvia;
| | - Ilona Vilkoite
- Health Centre 4, LV-1012 Riga, Latvia;
- Department of Doctoral Studies, Riga Stradins University, LV-1007 Riga, Latvia
| | - Viktors Veliks
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
| | - Anna Marija Ļeščinska
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Riga East University Hospital, LV-1038 Riga, Latvia
- Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia
| | - Ilmārs Stonāns
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
| | - Andrejs Pčolkins
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1038 Riga, Latvia
| | - Ivars Tolmanis
- Faculty of Medicine, Riga Stradins University, LV-1007 Riga, Latvia;
- Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia
| | - Gidi Shani
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Hossam Haick
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | | | - Johannes Glöckler
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany; (J.G.); (B.M.)
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany; (J.G.); (B.M.)
- Hahn-Schickard, 89077 Ulm, Germany
| | - Mārcis Leja
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia; (I.P.); (L.A.); (E.K.); (V.V.); (A.M.Ļ.); (I.S.); (A.P.); (M.L.)
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
- Riga East University Hospital, LV-1038 Riga, Latvia
- Digestive Diseases Centre GASTRO, LV-1079 Riga, Latvia
| |
Collapse
|
13
|
Jang WB, Yi D, Nguyen TM, Lee Y, Lee EJ, Choi J, Kim YH, Choi EJ, Oh JW, Kwon SM. Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications. Adv Healthc Mater 2023; 12:e2300845. [PMID: 37449876 DOI: 10.1002/adhm.202300845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.
Collapse
Affiliation(s)
- Woong Bi Jang
- Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea
- Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea
| | - Dongwon Yi
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, 50612, Republic of Korea
| | - Thanh Mien Nguyen
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan, 46241, Republic of Korea
| | - Yujin Lee
- Department of Nano Fusion Technology, Pusan National University, Busan, 46214, Republic of Korea
| | - Eun Ji Lee
- Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea
- Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea
| | - Jaewoo Choi
- Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea
- Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea
| | - You Hwan Kim
- Department of Nano Fusion Technology, Pusan National University, Busan, 46214, Republic of Korea
| | - Eun-Jung Choi
- Department of Nano Fusion Technology, Pusan National University, Busan, 46214, Republic of Korea
| | - Jin-Woo Oh
- Bio-IT Fusion Technology Research Institute, Pusan National University, Busan, 46241, Republic of Korea
- Department of Nano Fusion Technology, Pusan National University, Busan, 46214, Republic of Korea
- Korea Nanobiotechnology Center, Pusan National University, Busan, 46241, Republic of Korea
| | - Sang-Mo Kwon
- Laboratory for Vascular Medicine and Stem Cell Biology, Department of Physiology, Medical Research Institute, School of Medicine, Pusan National University, Yangsan, 50612, Republic of Korea
- Convergence Stem Cell Research Center, Pusan National University, Yangsan, 50612, Republic of Korea
| |
Collapse
|
14
|
Xu Y, Liu Z, Lin J, Zhao J, Hoa ND, Hieu NV, Ganeev AA, Chuchina V, Jouyban A, Cui D, Wang Y, Jin H. Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness. SENSORS (BASEL, SWITZERLAND) 2023; 23:8109. [PMID: 37836939 PMCID: PMC10575285 DOI: 10.3390/s23198109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.
Collapse
Affiliation(s)
- Yuli Xu
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zicheng Liu
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jingren Lin
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jintao Zhao
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Nguyen Duc Hoa
- International Training Institute for Material Science, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Nguyen Van Hieu
- Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 100000, Vietnam
| | - Alexander A Ganeev
- Department of Chemistry, St Petersburg University, 7/9 Universitetskaya Emb., St. Petersburg 199034, Russia
| | - Victoria Chuchina
- Department of Chemistry, St Petersburg University, 7/9 Universitetskaya Emb., St. Petersburg 199034, Russia
| | - Abolghasem Jouyban
- Pharmaceutical Analysis Research Center, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51368, Iran
| | - Daxiang Cui
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
- National Engineering Research Center for Nanotechnology, Shanghai 200241, China
| | - Ying Wang
- Chengdu Environmental Investment Group Co., Ltd., Building 1, Tianfushijia, No. 1000 Jincheng Street, Chengdu 610000, China
- Department of Biological Science, College of Life Sciences, Sichuan Normal University, Chengdu 610101, China
| | - Han Jin
- Institute of Micro-Nano Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
- National Engineering Research Center for Nanotechnology, Shanghai 200241, China
| |
Collapse
|
15
|
Im H, Choi J, Lee H, Al Balushi ZY, Park DH, Kim S. Colorimetric Multigas Sensor Arrays and an Artificial Olfactory Platform for Volatile Organic Compounds. ACS Sens 2023; 8:3370-3379. [PMID: 37642461 DOI: 10.1021/acssensors.3c00350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Herein, we develop colorimetric multigas sensor arrays assembling chemo-reactive fluorescent patch arrays and 10 × 10 indium gallium zinc oxide phototransistor arrays and apply them to an artificial olfactory platform to recognize five different volatile organic compounds (VOCs). Porous nanofibers, coupled with two organic emitters and emitting fluorescence, rapidly respond to gas-phased VOCs and offer unique fluorescent patterns associated with particular gas conditions, including gas kinds, concentrations, and exposure times by forming patch arrays with different fluorophore component ratios. These VOC-induced fluorescent patterns could be quantified and amplified by indium gallium zinc oxide (IGZO) phototransistor arrays functioning as a signal-generating component, resulting in gas-fingerprint patterns regarding electrical signals. Thus, the pattern library associated with VOCs and their concentration enables us to determine each airborne analyte as the artificial olfactory platform. Therefore, this system could achieve rapid, early quantitative recognition of hazardous gases and be applied as a preventative, portable, and wearable multigas identifier in various fields.
Collapse
Affiliation(s)
- Healin Im
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-do 16419, Republic of Korea
| | - Jinho Choi
- Department of Chemical Engineering, Inha University, Incheon 22212, Republic of Korea
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Hyeyun Lee
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-do 16419, Republic of Korea
| | - Zakaria Y Al Balushi
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
| | - Dong-Hyuk Park
- Department of Chemical Engineering, Inha University, Incheon 22212, Republic of Korea
- Program in Biomedical Science and Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Sunkook Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-do 16419, Republic of Korea
| |
Collapse
|
16
|
Gonçalves WB, Teixeira WSR, Sampaio ANDCE, Martins OA, Cervantes EP, Mioni MDSR, Gruber J, Pereira JG. Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella. J Microbiol Methods 2023; 212:106805. [PMID: 37558057 DOI: 10.1016/j.mimet.2023.106805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/26/2023] [Accepted: 08/05/2023] [Indexed: 08/11/2023]
Abstract
Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.
Collapse
Affiliation(s)
- Wellington Belarmino Gonçalves
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Wanderson Sirley Reis Teixeira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Aryele Nunes da Cruz Encide Sampaio
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Otávio Augusto Martins
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| | - Evelyn Perez Cervantes
- Instituto de Matemática e Estatística, Universidade de São Paulo, 05508-090, São Paulo, SP, Brazil.
| | - Mateus de Souza Ribeiro Mioni
- Departamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 14884-900, Jaboticabal, SP, Brazil.
| | - Jonas Gruber
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Av. Prof Lineu Prestes, 748, 05508-000, São Paulo, SP, Brazil.
| | - Juliano Gonçalves Pereira
- Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP), 18618-681, Botucatu, SP, Brazil.
| |
Collapse
|
17
|
Romano A, Fehervari M, Boshier PR. Influence of ventilatory parameters on the concentration of exhaled volatile organic compounds in mechanically ventilated patients. Analyst 2023; 148:4020-4029. [PMID: 37497696 DOI: 10.1039/d3an00786c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Analysis of volatile organic compounds (VOC) within exhaled breath is subject to numerous sources of methodological and physiological variability. Whilst breathing pattern is expected to influence the concentrations of selected exhaled VOCs, it remains challenging to investigate respiratory rate and depth accurately in awake subjects. Online breath sampling was performed in 20 mechanically ventilated patients using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). The effect of variation in respiratory rate (RR) and tidal volume (TV) on the VOC release profiles was examined. A panel of nineteen VOCs were selected, including isoprene, acetone, propofol, volatile aldehydes, acids and phenols. Variation in RR had the greatest influence on exhaled isoprene levels, with maximum and average concentrations being inversely correlated with RR. Variations in RR had a statistically significant impact on acetone, C3-C7 linear aldehydes and acetic acid. In comparison, phenols (including propofol), C8-C10 aldehydes and C3-C6 carboxylic acids were not influenced by RR. Isoprene was the only compound to be influenced by variation in TV. These findings, obtained under controlled conditions, provide useful guidelines for the optimisation of breath sampling protocols to be applied on awake patients.
Collapse
Affiliation(s)
- Andrea Romano
- Department Surgery and Cancer, Imperial College, London, UK
| | | | - Piers R Boshier
- Department Surgery and Cancer, Imperial College, London, UK
- Francis Crick Institute, London, UK
| |
Collapse
|
18
|
Kim KH, Jo S, Seo SE, Kim J, Lee DS, Joo S, Lee J, Song HS, Lee HG, Kwon OS. Ultrasensitive Gas Detection Based on Electrically Enhanced Nanoplasmonic Sensor with Graphene-Encased Gold Nanorod. ACS Sens 2023; 8:2169-2178. [PMID: 37161992 DOI: 10.1021/acssensors.2c02414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Nanoplasmonic sensors are a widely known concept and have been studied with various applications. Among them, gas detection is engaging attention in many fields. However, the analysis performance of nanoplasmonic sensors based on refractive index confined to the metal nanostructure characteristics causes challenges in gas detection. In this study, we develop a graphene-encased gold nanorod (AuNR)-based nanoplasmonic sensor to detect cadaverine gas. The graphene-encased AuNR (Gr@AuNR) presents an ultrasensitive peak wavelength shift even with tiny molecules. In addition, the external potential transmitted through graphene induces an additional shift. A chemical receptor is immobilized on Gr@AuNR (CR@Gr@AuNR) for selectively capturing cadaverine. The CR@Gr@AuNR achieves ultrasensitive detection of cadaverine gas, and the detection limit is increased to 15.99 ppb by applying a voltage to graphene. Furthermore, the experimental results of measuring cadaverine generated from spoiled pork show the practicality of CR@Gr@AuNR. The strategy of external-boosted nanoplasmonics provides new insight into plasmonic sensing and applications.
Collapse
Affiliation(s)
- Kyung Ho Kim
- Infectious Disease Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Immunotherapy Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Seongjae Jo
- Infectious Disease Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Immunotherapy Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Sung Eun Seo
- Infectious Disease Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Jaemin Kim
- Department of Control and Instrumentation Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Dae-Sik Lee
- Diagnostic & Therapeutic Systems Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34141, Republic of Korea
| | - Siyeon Joo
- Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Jiwon Lee
- Center for Environment, Health and Welfare Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Hyun Seok Song
- Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Hee Gu Lee
- Immunotherapy Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Oh Seok Kwon
- Infectious Disease Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Nano Engineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
| |
Collapse
|
19
|
Khorramifar A, Karami H, Lvova L, Kolouri A, Łazuka E, Piłat-Rożek M, Łagód G, Ramos J, Lozano J, Kaveh M, Darvishi Y. Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:5716. [PMID: 37420880 PMCID: PMC10300923 DOI: 10.3390/s23125716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.
Collapse
Affiliation(s)
- Ali Khorramifar
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Hamed Karami
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Larisa Lvova
- Department of Chemical Science and Technologies, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Alireza Kolouri
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199, Iran; (A.K.); (A.K.)
| | - Ewa Łazuka
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Magdalena Piłat-Rożek
- Department of Applied Mathematics, Faculty of Technology Fundamentals, Lublin University of Technology, 20-618 Lublin, Poland; (E.Ł.); (M.P.-R.)
| | - Grzegorz Łagód
- Department of Water Supply and Wastewater Disposal, Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland;
| | - Jose Ramos
- College of Computing and Engineering, Nova Southeastern University (NSU), 3301 College Avenue, Fort Lauderdale, FL 33314-7796, USA;
| | - Jesús Lozano
- Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. De Elvas S/n, 06006 Badajoz, Spain;
| | - Mohammad Kaveh
- Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq;
| | - Yousef Darvishi
- Department of Biosystems Engineering, University of Tehran, Tehran P.O. Box 113654117, Iran;
| |
Collapse
|
20
|
Zhu LY, Ou LX, Mao LW, Wu XY, Liu YP, Lu HL. Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors: Overview. NANO-MICRO LETTERS 2023; 15:89. [PMID: 37029296 PMCID: PMC10082150 DOI: 10.1007/s40820-023-01047-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/25/2023] [Indexed: 06/19/2023]
Abstract
Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring, exhaled breath diagnosis, and food freshness analysis. Among various chemiresistive sensing materials, noble metal-decorated semiconducting metal oxides (SMOs) have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble metals. This review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures (e.g., nanoparticles, nanowires, nanorods, nanosheets, nanoflowers, and microspheres) for high-performance gas sensors with higher response, faster response/recovery speed, lower operating temperature, and ultra-low detection limits. The key topics include Pt, Pd, Au, other noble metals (e.g., Ag, Ru, and Rh.), and bimetals-decorated SMOs containing ZnO, SnO2, WO3, other SMOs (e.g., In2O3, Fe2O3, and CuO), and heterostructured SMOs. In addition to conventional devices, the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also discussed. Moreover, the relevant mechanisms for the sensing performance improvement caused by noble metal decoration, including the electronic sensitization effect and the chemical sensitization effect, have also been summarized in detail. Finally, major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.
Collapse
Affiliation(s)
- Li-Yuan Zhu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Lang-Xi Ou
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Li-Wen Mao
- School of Opto-Electronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China
| | - Xue-Yan Wu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Yi-Ping Liu
- State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China
| | - Hong-Liang Lu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China.
| |
Collapse
|
21
|
Wei H, Zhang H, Song B, Yuan K, Xiao H, Cao Y, Cao Q. Metal-Organic Framework (MOF) Derivatives as Promising Chemiresistive Gas Sensing Materials: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4388. [PMID: 36901399 PMCID: PMC10001476 DOI: 10.3390/ijerph20054388] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
The emission of harmful gases has seriously exceeded relative standards with the rapid development of modern industry, which has shown various negative impacts on human health and the natural environment. Recently, metal-organic frameworks (MOFs)-based materials have been widely used as chemiresistive gas sensing materials for the sensitive detection and monitoring of harmful gases such as NOx, H2S, and many volatile organic compounds (VOCs). In particular, the derivatives of MOFs, which are usually semiconducting metal oxides and oxide-carbon composites, hold great potential to prompt the surface reactions with analytes and thus output amplified resistance changing signals of the chemiresistors, due to their high specific surface areas, versatile structural tunability, diversified surface architectures, as well as their superior selectivity. In this review, we introduce the recent progress in applying sophisticated MOFs-derived materials for chemiresistive gas sensors, with specific emphasis placed on the synthesis and structural regulation of the MOF derivatives, and the promoted surface reaction mechanisms between MOF derivatives and gas analytes. Furthermore, the practical application of MOF derivatives for chemiresistive sensing of NO2, H2S, and typical VOCs (e.g., acetone and ethanol) has been discussed in detail.
Collapse
Affiliation(s)
- Huijie Wei
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
| | - Huiyan Zhang
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
| | - Bing Song
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
| | - Kaiping Yuan
- Frontier Institute of Chip and System, Fudan University, Shanghai 200438, China
| | - Hongbin Xiao
- Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
| | - Yunyi Cao
- Laundry Appliances Business Division of Midea Group, Wuxi 214028, China
| | - Qi Cao
- Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
| |
Collapse
|
22
|
Wu C, Li J. Portable FBAR based E-nose for cold chain real-time bananas shelf time detection. NANOTECHNOLOGY AND PRECISION ENGINEERING 2023. [DOI: 10.1063/10.0016870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.
Collapse
Affiliation(s)
- Chen Wu
- Frontier Science Center for Smart Materials, College of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Jiuyan Li
- Frontier Science Center for Smart Materials, College of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
- Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai Economic and Technological Development Zone, 300 Changjiang Road, Yantai, China
| |
Collapse
|
23
|
Zhang T, Lin S, Zhou Y, Hu J. Several ML Algorithms and Their Feature Vector Design for Gas Discrimination and Concentration Measurement with an Ultrasonically Catalyzed MOX Sensor. ACS Sens 2023; 8:665-672. [PMID: 36696118 DOI: 10.1021/acssensors.2c02159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Although gas-borne ultrasound catalysis has been developed as a new method to discriminate gas species and measure the concentration, applications of machine learning methods in gas analyses with a single metal oxide (MOX) gas sensor catalyzed by gas-borne ultrasound are still scarce. In this work, with an ultrasonically catalyzed MOX gas sensor, we explored the effectiveness of K-nearest neighbors (KNN), support vector machine (SVM), and single-hidden-layer BP-ANN (SHBP) in gas discrimination and the application of the SHBP in concentration measurement. The target gases in this work are ethanol, acetone, methanol, hydrogen, and n-butane in clean air, respectively, and the discrimination and concentration regression are implemented by two different ML models. With the properly designed feature vectors, the SHBP method has an acceptable capability of both of species discrimination and concentration regression (success rate of gas discrimination = 99.5%, relative error of concentration regression = 6.406%). The KNN and SVM methods have similar capabilities of gas discrimination as the SHBP. This work also demonstrates a method to design the feature vectors for the ultrasonically catalyzed MOX gas sensor and to choose the feature parameters.
Collapse
Affiliation(s)
- Tianyu Zhang
- State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing210000, China
| | - Shun Lin
- State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing210000, China
| | - Yuchen Zhou
- State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing210000, China
| | - Junhui Hu
- State Key Lab of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing210000, China
| |
Collapse
|
24
|
Li X, Guo J, Xu W, Cao J. Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm. ACS Sens 2023; 8:822-828. [PMID: 36701636 DOI: 10.1021/acssensors.2c02450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.
Collapse
Affiliation(s)
- Xiulei Li
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Jiayi Guo
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Wangping Xu
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| | - Juexian Cao
- Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China
| |
Collapse
|
25
|
Deng Z, Guo L, Chen X, Wu W. Smart Wearable Systems for Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052479. [PMID: 36904682 PMCID: PMC10007426 DOI: 10.3390/s23052479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration, and the number of high-performance wearable systems has been gradually increasing in recent years. However, there are still many challenges in these fields, such as balancing the trade-off between flexibility/stretchability, sensing performance, and the robustness of systems. For this reason, more evolution is required to promote the development of wearable health-monitoring systems. In this regard, this review summarizes some representative achievements and recent progress of wearable systems for health monitoring. Meanwhile, a strategy overview is presented about selecting materials, integrating systems, and monitoring biosignals. The next generation of wearable systems for accurate, portable, continuous, and long-term health monitoring will offer more opportunities for disease diagnosis and treatment.
Collapse
Affiliation(s)
- Zhiyong Deng
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
- Nuclear Power Institute of China, Huayang, Shuangliu District, Chengdu 610213, China
| | - Lihao Guo
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Ximeng Chen
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| |
Collapse
|
26
|
Anisimov D, Abramov AA, Gaidarzhi VP, Kaplun DS, Agina EV, Ponomarenko SA. Food Freshness Measurements and Product Distinguishing by a Portable Electronic Nose Based on Organic Field-Effect Transistors. ACS OMEGA 2023; 8:4649-4654. [PMID: 36777610 PMCID: PMC9909782 DOI: 10.1021/acsomega.2c06386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/11/2023] [Indexed: 05/14/2023]
Abstract
Determination of food freshness, which is the most ancient role of the human sense of smell, is still a challenge for compact and inexpensive electronic nose devices. Fast, sensitive, and reusable sensors are long-awaited in the food industry to replace slow, labor-intensive, and expensive bacteriological methods. In this work, we present microbiological verification of a novel approach to food quality monitoring and spoilage detection using an electronic nose based on organic field-effect transistors (OFETs) and its application for distinguishing products. The compact device presented is able to detect spoilage-related gases as early as at the 4 × 104 CFU g-1 bacteria count level, which is 2 orders of magnitude below the safe consumption threshold. Cross-selective sensor array based on OFETs with metalloporphyrin receptors were made on a single substrate using solution processing leading to a low production cost. Moreover, machine learning methods applied to the sensor array response allowed us to compare spoilage profiles and separate them by the type of food: pork, chicken, fish, or milk. The approach presented can be used to monitor food spoilage and distinguish different products with an affordable and portable device.
Collapse
Affiliation(s)
- Daniil
S. Anisimov
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Anton A. Abramov
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Victoria P. Gaidarzhi
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Darya S. Kaplun
- The
Federal Research Centre “Fundamentals of Biotechnology”
of the Russian Academy of Sciences, Moscow 119071, Russia
| | - Elena V. Agina
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| | - Sergey A. Ponomarenko
- Enikolopov
Institute of Synthetic Polymeric Materials of Russian Academy of Sciences, Moscow 117393, Russia
| |
Collapse
|
27
|
P H, Rangarajan M, Pandya HJ. Breath VOC analysis and machine learning approaches for disease screening: a review. J Breath Res 2023; 17. [PMID: 36634360 DOI: 10.1088/1752-7163/acb283] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023]
Abstract
Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.
Collapse
Affiliation(s)
- Haripriya P
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Madhavan Rangarajan
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Hardik J Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.,Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore 560012, India
| |
Collapse
|
28
|
Lee K, Cho I, Kang M, Jeong J, Choi M, Woo KY, Yoon KJ, Cho YH, Park I. Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning. ACS NANO 2023; 17:539-551. [PMID: 36534781 DOI: 10.1021/acsnano.2c09314] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In2O3 film coated with plasmonic metal nanoparticles (NPs) directly on the μLED. The e-nose system consists of two different μLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-μLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO2, acetone, methanol) was developed. The μLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.
Collapse
Affiliation(s)
- Kichul Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Incheol Cho
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Mingu Kang
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Jaeseok Jeong
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Minho Choi
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Kie Young Woo
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Kuk-Jin Yoon
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yong-Hoon Cho
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for the NanoCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Inkyu Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for the NanoCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| |
Collapse
|
29
|
Kim S, Yoo H. Recent Progress in Thin-Film Transistors toward Digital, Analog, and Functional Circuits. MICROMACHINES 2022; 13:2258. [PMID: 36557558 PMCID: PMC9783209 DOI: 10.3390/mi13122258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/11/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Thin-film transistors have been extensively developed due to their process merit: high compatibility with various substrates, large-area processes, and low-cost processes. Despite these advantages, most efforts for thin-film transistors still remain at the level of unit devices, so the circuit level for practical use needs to be further developed. In this regard, this review revisits digital and analog thin-film circuits using carbon nanotubes (CNTs), organic electrochemical transistors (OECTs), organic semiconductors, metal oxides, and two-dimensional materials. This review also discusses how to integrate thin-film circuits at the unit device level and some key issues such as metal routing and interconnection. Challenges and opportunities are also discussed to pave the way for developing thin-film circuits and their practical applications.
Collapse
|
30
|
Cennamo N, Arcadio F, Capasso F, Maniglio D, Zeni L, Bossi AM. Non-Specific Responsive Nanogels and Plasmonics to Design MathMaterial Sensing Interfaces: The Case of a Solvent Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:s222410006. [PMID: 36560375 PMCID: PMC9787685 DOI: 10.3390/s222410006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 06/01/2023]
Abstract
The combination of non-specific deformable nanogels and plasmonic optical probes provides an innovative solution for specific sensing using a generalistic recognition layer. Soft polyacrylamide nanogels that lack specific selectivity but are characterized by responsive behavior, i.e., shrinking and swelling dependent on the surrounding environment, were grafted to a gold plasmonic D-shaped plastic optical fiber (POF) probe. The nanogel-POF cyclically challenged with water or alcoholic solutions optically reported the reversible solvent-to-phase transitions of the nanomaterial, embodying a primary optical switch. Additionally, the non-specific nanogel-POF interface exhibited more degrees of freedom through which specific sensing was enabled. The real-time monitoring of the refractive index variations due to the time-related volume-to-phase transition effects of the nanogels enabled us to determine the environment's characteristics and broadly classify solvents. Hence the nanogel-POF interface was a descriptor of mathematical functions for substance identification and classification processes. These results epitomize the concept of responsive non-specific nanomaterials to perform a multiparametric description of the environment, offering a specific set of features for the processing stage and particularly suitable for machine and deep learning. Thus, soft MathMaterial interfaces provide the ground to devise devices suitable for the next generation of smart intelligent sensing processes.
Collapse
Affiliation(s)
- Nunzio Cennamo
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Francesco Arcadio
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Fiore Capasso
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Devid Maniglio
- Department of Industrial Engineering, BIOtech Research Center, University of Trento, Via delle Regole 101, Mattarello, 38123 Trento, Italy
| | - Luigi Zeni
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Alessandra Maria Bossi
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| |
Collapse
|
31
|
Meller S, Al Khatri MSA, Alhammadi HK, Álvarez G, Alvergnat G, Alves LC, Callewaert C, Caraguel CGB, Carancci P, Chaber AL, Charalambous M, Desquilbet L, Ebbers H, Ebbers J, Grandjean D, Guest C, Guyot H, Hielm-Björkman A, Hopkins A, Kreienbrock L, Logan JG, Lorenzo H, Maia RDCC, Mancilla-Tapia JM, Mardones FO, Mutesa L, Nsanzimana S, Otto CM, Salgado-Caxito M, de los Santos F, da Silva JES, Schalke E, Schoneberg C, Soares AF, Twele F, Vidal-Martínez VM, Zapata A, Zimin-Veselkoff N, Volk HA. Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections. Front Med (Lausanne) 2022; 9:1015620. [PMID: 36569156 PMCID: PMC9773891 DOI: 10.3389/fmed.2022.1015620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Affiliation(s)
- Sebastian Meller
- Department of Small Animal Medicine & Surgery, University of Veterinary Medicine Hannover, Hanover, Germany,*Correspondence: Sebastian Meller,
| | | | - Hamad Khatir Alhammadi
- International Operations Department, Ministry of Interior of the United Arab Emirates, Abu Dhabi, United Arab Emirates
| | - Guadalupe Álvarez
- Faculty of Veterinary Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Guillaume Alvergnat
- International Operations Department, Ministry of Interior of the United Arab Emirates, Abu Dhabi, United Arab Emirates
| | - Lêucio Câmara Alves
- Department of Veterinary Medicine, Federal Rural University of Pernambuco, Recife, Brazil
| | - Chris Callewaert
- Center for Microbial Ecology and Technology, Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Charles G. B. Caraguel
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Paula Carancci
- Faculty of Veterinary Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Anne-Lise Chaber
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Marios Charalambous
- Department of Small Animal Medicine & Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Loïc Desquilbet
- École Nationale Vétérinaire d’Alfort, IMRB, Université Paris Est, Maisons-Alfort, France
| | | | | | - Dominique Grandjean
- École Nationale Vétérinaire d’Alfort, Université Paris-Est, Maisons-Alfort, France
| | - Claire Guest
- Medical Detection Dogs, Milton Keynes, United Kingdom
| | - Hugues Guyot
- Clinical Department of Production Animals, Fundamental and Applied Research for Animals & Health Research Unit, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Anna Hielm-Björkman
- Department of Equine and Small Animal Medicine, University of Helsinki, Helsinki, Finland
| | - Amy Hopkins
- Medical Detection Dogs, Milton Keynes, United Kingdom
| | - Lothar Kreienbrock
- Department of Biometry, Epidemiology and Information Processing, University of Veterinary Medicine Hannover, Hanover, Germany
| | - James G. Logan
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom,Arctech Innovation, The Cube, Dagenham, United Kingdom
| | - Hector Lorenzo
- Faculty of Veterinary Science, University of Buenos Aires, Buenos Aires, Argentina
| | | | | | - Fernando O. Mardones
- Escuela de Medicina Veterinaria, Facultad de Agronomía e Ingeniería Forestal and Facultad de Ciencias Biológicas y Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Leon Mutesa
- Center for Human Genetics, College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda,Rwanda National Joint Task Force COVID-19, Kigali, Rwanda
| | | | - Cynthia M. Otto
- Penn Vet Working Dog Center, Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Marília Salgado-Caxito
- Escuela de Medicina Veterinaria, Facultad de Agronomía e Ingeniería Forestal and Facultad de Ciencias Biológicas y Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | | | - Esther Schalke
- Bundeswehr Medical Service Headquarters, Koblenz, Germany
| | - Clara Schoneberg
- Department of Biometry, Epidemiology and Information Processing, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Anísio Francisco Soares
- Department of Animal Morphology and Physiology, Federal Rural University of Pernambuco, Recife, Brazil
| | - Friederike Twele
- Department of Small Animal Medicine & Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Victor Manuel Vidal-Martínez
- Laboratorio de Parasitología y Patología Acuática, Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados del IPN Unidad Mérida, Mérida, Yucatán, Mexico
| | - Ariel Zapata
- Faculty of Veterinary Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Natalia Zimin-Veselkoff
- Escuela de Medicina Veterinaria, Facultad de Agronomía e Ingeniería Forestal and Facultad de Ciencias Biológicas y Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Holger A. Volk
- Department of Small Animal Medicine & Surgery, University of Veterinary Medicine Hannover, Hanover, Germany,Center for Systems Neuroscience Hannover, Hanover, Germany
| |
Collapse
|
32
|
Dutta P, Gupta G. Environmental gas sensors based on electroactive hybrid organic-inorganic nanocomposites using nanostructured materials. Phys Chem Chem Phys 2022; 24:28680-28699. [PMID: 36416590 DOI: 10.1039/d2cp04247a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Advanced gas sensing devices are urgently demanded in the modern scientific world to control air pollution and protect human life. For this purpose, semiconducting electroactive materials can revolutionize the idea of conventional gas sensors. Chemi-resistive gas sensors based on electroactive hybrid organic-inorganic nanocomposites are incredibly promising gas sensing materials because they possess the advantages of excellent selectivity, high sensitivity, low response time, repeatability, high stability, cost-effectiveness, and simple fabrication techniques, and they can be operated at room temperature. This review emphasizes the recent developments of organic-inorganic hybrid nanocomposite-based electroactive gas sensors, including metal oxide nanocomposites, which are potential gas sensing materials due to the presence of numerous charge carriers. The review also focuses on nanostructured materials of different dimensions, such as semiconducting quantum dots, carbon dots, nanotubes, nanowires, and nanosheets, used for developing these gas sensing compounds and their significance and challenges. Some possible fabrication techniques for developing efficient gas sensors with different morphologies are discussed, with their probable sensing mechanism behind the detection of toxic vapours. Subsequently, a summary and possible outcome of this study, along with the various achievements and prospects in this field, are also discussed.
Collapse
Affiliation(s)
- Priyanka Dutta
- CSIR-National Physical Laboratory, Dr K. S. Krishnan Road, New Delhi 110012, India.
| | - Govind Gupta
- CSIR-National Physical Laboratory, Dr K. S. Krishnan Road, New Delhi 110012, India. .,Academy of Scientific and Innovative Research, CSIR-HRDC Campus, Ghaziabad, Uttar Pradesh-201002, India
| |
Collapse
|
33
|
Zhao Z, Bao H, Zhao Q, Fu H, Zhou L, Zhang H, Li Y, Cai W. Efficient SERS Response of Porous-ZnO-Covered Gold Nanoarray Chips to Trace Benzene-Volatile Organic Compounds. ACS APPLIED MATERIALS & INTERFACES 2022; 14:47999-48010. [PMID: 36223181 DOI: 10.1021/acsami.2c11682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Fast and sensitive detection of gaseous volatile organic compounds (VOCs), based on surface-enhanced Raman spectroscopy (SERS), is still a challenge due to their weak interaction with plasmonic metals and overly small Raman scattering cross sections. Herein, we propose a simple strategy to achieve the SERS-based highly efficient detection of trace benzene-VOCs (B-VOCs) based on a composite chip. The composite chip is designed and fabricated via covering the porous zinc oxide on gold nanoarrays by a one-step solution growth method. Such composite chip shows highly selective capture of gaseous B-VOCs (benzene, toluene, nitrobenzene, xylene, and chlorobenzene, etc.), which leads to the rapid and sensitive SERS responses to them. Typically, this chip can response to gaseous toluene within 30 s, and the lowest detectable concentration is below 10 ppb. Further experiments have revealed that there exists an optimal thickness of the ZnO covering layer for the highly efficient SERS response to the B-VOCs, which is about 150 nm. Also, such a composite chip is recoverable in SERS response and hence reusable. The highly efficient SERS response of the composite chip to the B-VOCs is attributed to the porous structure-enhanced molecular adsorption and the electromagnetic-chemical dual-enhancement mechanism. This work not only presents a practical SERS chip for the efficient detection of the typical B-VOCs but also provides a deep understand the interaction between the B-VOCs and the ZnO as well as the chemical enhancement mechanism.
Collapse
Affiliation(s)
- Zhipeng Zhao
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
- University of Science and Technology of China, Hefei 230026, PR China
| | - Haoming Bao
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
| | - Qian Zhao
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
| | - Hao Fu
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
- University of Science and Technology of China, Hefei 230026, PR China
| | - Le Zhou
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
| | - Hongwen Zhang
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
| | - Yue Li
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
- University of Science and Technology of China, Hefei 230026, PR China
| | - Weiping Cai
- Key Lab of Materials Physics, Anhui Key Lab of Nanomaterials and Nanotechnology, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, PR China
- University of Science and Technology of China, Hefei 230026, PR China
| |
Collapse
|
34
|
Ou LX, Liu MY, Zhu LY, Zhang DW, Lu HL. Recent Progress on Flexible Room-Temperature Gas Sensors Based on Metal Oxide Semiconductor. NANO-MICRO LETTERS 2022; 14:206. [PMID: 36271065 PMCID: PMC9587164 DOI: 10.1007/s40820-022-00956-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/12/2022] [Indexed: 05/05/2023]
Abstract
With the rapid development of the Internet of Things, there is a great demand for portable gas sensors. Metal oxide semiconductors (MOS) are one of the most traditional and well-studied gas sensing materials and have been widely used to prepare various commercial gas sensors. However, it is limited by high operating temperature. The current research works are directed towards fabricating high-performance flexible room-temperature (FRT) gas sensors, which are effective in simplifying the structure of MOS-based sensors, reducing power consumption, and expanding the application of portable devices. This article presents the recent research progress of MOS-based FRT gas sensors in terms of sensing mechanism, performance, flexibility characteristics, and applications. This review comprehensively summarizes and discusses five types of MOS-based FRT gas sensors, including pristine MOS, noble metal nanoparticles modified MOS, organic polymers modified MOS, carbon-based materials (carbon nanotubes and graphene derivatives) modified MOS, and two-dimensional transition metal dichalcogenides materials modified MOS. The effect of light-illuminated to improve gas sensing performance is further discussed. Furthermore, the applications and future perspectives of FRT gas sensors are also discussed.
Collapse
Affiliation(s)
- Lang-Xi Ou
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics &Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Meng-Yang Liu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics &Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Li-Yuan Zhu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics &Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics &Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China
| | - Hong-Liang Lu
- State Key Laboratory of ASIC and System, Shanghai Institute of Intelligent Electronics &Systems, School of Microelectronics, Fudan University, Shanghai, 200433, People's Republic of China.
- Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, 322000, Zhejiang, People's Republic of China.
| |
Collapse
|
35
|
The Application of In Situ Methods to Monitor VOC Concentrations in Urban Areas—A Bibliometric Analysis and Measuring Solution Review. SUSTAINABILITY 2022. [DOI: 10.3390/su14148815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Urbanisation development affects urban vegetation both directly and indirectly. Since this process usually involves a dramatic change in land use, it is seen as likely to cause ecological pressure on local ecosystems. All forms of human activity, including urbanisation of areas close to residential buildings, significantly impact air quality. This study aims to identify and characterise different measurement solutions of VOCs, allowing the quantification of total and selective compounds in a direct at source (in situ) manner. Portable devices for direct testing can generally be divided into detectors, chromatographs, and electronic noses. They differ in parameters such as operating principle, sensitivity, measurement range, response time, and selectivity. Direct research allows us to obtain measurement results in a short time, which is essential from the point of view of immediate reaction in the case of high concentrations of tested compounds and the possibility of ensuring the well-being of people. The paper also attempts to compare solutions and devices available on the market and assess their application.
Collapse
|
36
|
Mansour E, Sherbo S, Saliba W, Kloper V, Haick H. Effect of the Dispersion Process and Nanoparticle Quality on Chemical Sensing Performance. ACS OMEGA 2022; 7:22484-22491. [PMID: 35811934 PMCID: PMC9260890 DOI: 10.1021/acsomega.2c01668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
On the surface of chemiresistive films, the scarce heterogeneity of a molecularly capped gold nanoparticle (MCGNP) colloidal dispersion and uneven evaporation of the MCGNP-contained drying drop applied to this surface are among the main factors that affect reproducibility, and repeatable fabrication of thin films of MCGNPs. This article shows that an increase in reproducibility and repeatability is possible using a dispersant and a surfactant during the deposition and annealing processes of the MCGNP. The results show higher sensitivity and accuracy of the sensors for the detection of volatile organic compounds in air and an increased limit of detection. These simple and practical additions might serve as a launching pad for fabrication of other types of thin-film-based sensors.
Collapse
Affiliation(s)
- Elias Mansour
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
| | - Shay Sherbo
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
| | - Walaa Saliba
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
| | - Viki Kloper
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- The
Department of Chemical Engineering, Technion
− Israel Institute of Technology, Haifa 3200003, Israel
- The
Russell Berrie Nanotechnology Institute, Technion − Israel Institute of Technology, Haifa 3200003, Israel
| |
Collapse
|
37
|
Kwon DH, Jin EH, Yoo DH, Roh JW, Suh D, Commerell W, Huh JS. Analysis of the Response Characteristics of Toluene Gas Sensors with a ZnO Nanorod Structure by a Heat Treatment Process. SENSORS 2022; 22:s22114125. [PMID: 35684745 PMCID: PMC9185228 DOI: 10.3390/s22114125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 01/27/2023]
Abstract
The sensing characteristics of toluene gas are monitored by fabricating ZnO nanorod structures. ZnO nanostructured sensor materials are produced on a Zn film via an ultrasonic process in a 0.01 M aqueous solution of C6H12N4 and Zn(NO3)2∙6H2O. The response of the sensors subjected to heat treatment in oxygen and nitrogen atmospheres is improved by 20% and 10%, respectively. The improvement is considered to be correlated with the increase in grain size. The relationship between the heat treatment and sensing characteristics is evaluated.
Collapse
Affiliation(s)
- Dae-Hwan Kwon
- Korea Gas Safety Corporation, Wonjung-ro, Maengdong-myeon, Eumseong-gun 27738, Chungcheongbuk-do, Korea;
| | - Eui-Hyun Jin
- School of Convergence and Fusion System Engineering, Kyungpook National University, 2559, Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea; (E.-H.J.); (D.S.)
| | - Dae-Hwang Yoo
- Institute for Global Climate Change and Energy, Kyungpook National University, Sankyuk-dong, Puk-Gu, Daegu 41566, Korea;
| | - Jong-Wook Roh
- School of Nano and Materials Science and Engineering, Kyungpook National University, 2559, Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea;
| | - Dongjun Suh
- School of Convergence and Fusion System Engineering, Kyungpook National University, 2559, Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea; (E.-H.J.); (D.S.)
| | - Walter Commerell
- Technische Hochschule Ulm, Eberhard-Finckh-Strasse 11, 89075 Ulm, Germany;
| | - Jeung-Soo Huh
- School of Convergence and Fusion System Engineering, Kyungpook National University, 2559, Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea; (E.-H.J.); (D.S.)
- Institute for Global Climate Change and Energy, Kyungpook National University, Sankyuk-dong, Puk-Gu, Daegu 41566, Korea;
- Correspondence: ; Tel.: +82-53-950-5562
| |
Collapse
|
38
|
Jian Y, Zhang N, Liu T, Zhu Y, Wang D, Dong H, Guo L, Qu D, Jiang X, Du T, Zheng Y, Yuan M, Fu X, Liu J, Dou W, Niu F, Ning R, Zhang G, Fan J, Haick H, Wu W. Artificially Intelligent Olfaction for Fast and Noninvasive Diagnosis of Bladder Cancer from Urine. ACS Sens 2022; 7:1720-1731. [PMID: 35613367 DOI: 10.1021/acssensors.2c00467] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC at an early stage and maintain postoperative surveillance through ″smelling″ clinical urine samples at room temperature. In clinical trials, 18 healthy controls and 76 BLC patients (60 and 16 at early and advanced stages, respectively) are assessed by the artificial olfactory system. With the assistance of a support vector machine (SVM), very high sensitivity and accuracy from healthy controls are achieved, exceeding those obtained by the current techniques in practice. In addition, the recurrences of both early and advanced stages are diagnosed well, with the effect of confounding factors on the performance of the artificial olfactory system found to have a negligible influence on the diagnostic performance. Overall, this study contributes a novel, noninvasive, easy-to-use, inexpensive, real-time, accurate method for urine disease diagnosis, which can be useful for personalized care/diagnosis and postoperative surveillance, resulting in saving more lives.
Collapse
Affiliation(s)
- Yingying Jian
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Nan Zhang
- Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Taoping Liu
- Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710126, China
| | - Yujin Zhu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Di Wang
- Intelligent Perception Research Institute, Zhejiang Lab, Hangzhou 311100, China
| | - Hao Dong
- Intelligent Perception Research Institute, Zhejiang Lab, Hangzhou 311100, China
| | - Lihao Guo
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Danyao Qu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Xue Jiang
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Tao Du
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Youbin Zheng
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Miaomiao Yuan
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Xuemei Fu
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Jinmei Liu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
| | - Wei Dou
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, China
| | - Fang Niu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, China
| | - Ruizhi Ning
- Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710126, China
| | - Guangjian Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Jinhai Fan
- Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi’an 710126, China
- Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710126, China
| |
Collapse
|
39
|
Sha J, Xu C, Xu K. Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food. MICROMACHINES 2022; 13:mi13050789. [PMID: 35630255 PMCID: PMC9145094 DOI: 10.3390/mi13050789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/12/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022]
Abstract
In the past 20 years, the development of an artificial olfactory system has made great progress and improvements. In recent years, as a new type of sensor, nanoelectronic smelling has been widely used in the food and drug industry because of its advantages of accurate sensitivity and good selectivity. This paper reviews the latest applications and progress of nanoelectronic smelling in animal-, plant-, and microbial-based foods. This includes an analysis of the status of nanoelectronic smelling in animal-based foods, an analysis of its harmful composition in plant-based foods, and an analysis of the microorganism quantity in microbial-based foods. We also conduct a flavor component analysis and an assessment of the advantages of nanoelectronic smelling. On this basis, the principles and structures of nanoelectronic smelling are also analyzed. Finally, the limitations and challenges of nanoelectronic smelling are summarized, and the future development of nanoelectronic smelling is proposed.
Collapse
Affiliation(s)
| | - Chong Xu
- Correspondence: ; Tel.: +86-024-2469-2899
| | | |
Collapse
|
40
|
Guo Q, Adelina NM, Hu J, Zhang L, Zhao Y. Comparative analysis of volatile profiles in four pine-mushrooms using HS-SPME/GC-MS and E-nose. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108711] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
41
|
Weerakkody JS, El Kazzy M, Jacquier E, Elchinger PH, Mathey R, Ling WL, Herrier C, Livache T, Buhot A, Hou Y. Surfactant-like Peptide Self-Assembled into Hybrid Nanostructures for Electronic Nose Applications. ACS NANO 2022; 16:4444-4457. [PMID: 35174710 DOI: 10.1021/acsnano.1c10734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An electronic nose (e-nose) utilizes a multisensor array, which relies on the vector contrast of combinatorial responses, to effectively discriminate between volatile organic compounds (VOCs). In recent years, hierarchical structures made of nonbiological materials have been used to achieve the required sensor diversity. With the advent of self-assembling peptides, the ability to tune nanostructuration, surprisingly, has not been exploited for sensor array diversification. In this work, a designer surfactant-like peptide sequence, CG7-NH2, is used to fabricate morphologically and physicochemically heterogeneous "biohybrid" surfaces on Au-covered chips. These multistructural sensing surfaces, containing immobilized hierarchical nanostructures surrounded by self-assembled monolayers, are used for the detection and discrimination of VOCs. Through a simple and judicious design process, involving changes in pH and water content of peptide solutions, a five-element biohybrid sensor array coupled with a gas-phase surface plasmon resonance imaging system is shown to achieve sufficient discriminatory capabilities for four VOCs. Moreover, the limit of detection of the multiarray system is bench-marked at <1 and 6 ppbv for hexanoic acid and phenol (esophago-gastric biomarkers), respectively. Finally, the humidity effects are characterized, identifying the dissociation rate constant as a robust descriptor for classification, further exemplifying their efficacy as biomaterials in the field of artificial olfaction.
Collapse
Affiliation(s)
- Jonathan S Weerakkody
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Marielle El Kazzy
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Elise Jacquier
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Pierre-Henri Elchinger
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Raphael Mathey
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Wai Li Ling
- Université Grenoble Alpes, CEA, CNRS, IRIG, IBS, 71 Avenue des Martyrs, Grenoble 38000, France
| | - Cyril Herrier
- Aryballe, 7 Rue des Arts et Métiers, Grenoble 38000, France
| | | | - Arnaud Buhot
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| | - Yanxia Hou
- Université Grenoble Alpes, CEA, CNRS, IRIG, SyMMES, 17 Avenue des Martyrs, Grenoble 38000, France
| |
Collapse
|
42
|
Rapid odor recognition based on reliefF algorithm using electronic nose and its application in fruit identification and classification. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
43
|
Esteves C, Palma SICJ, Costa HMA, Alves C, Santos GMC, Ramou E, Carvalho AL, Alves V, Roque ACA. Tackling Humidity with Designer Ionic Liquid-Based Gas Sensing Soft Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107205. [PMID: 34873762 PMCID: PMC7613046 DOI: 10.1002/adma.202107205] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/12/2021] [Indexed: 05/13/2023]
Abstract
Relative humidity is simultaneously a sensing target and a contaminant in gas and volatile organic compound (VOC) sensing systems, where strategies to control humidity interference are required. An unmet challenge is the creation of gas-sensitive materials where the response to humidity is controlled by the material itself. Here, humidity effects are controlled through the design of gelatin formulations in ionic liquids without and with liquid crystals as electrical and optical sensors, respectively. In this design, the anions [DCA]- and [Cl]- of room temperature ionic liquids from the 1-butyl-3-methylimidazolium family tailor the response to humidity and, subsequently, sensing of VOCs in dry and humid conditions. Due to the combined effect of the materials formulations and sensing mechanisms, changing the anion from [DCA]- to the much more hygroscopic [Cl]- , leads to stronger electrical responses and much weaker optical responses to humidity. Thus, either humidity sensors or humidity-tolerant VOC sensors that do not require sample preconditioning or signal processing to correct humidity impact are obtained. With the wide spread of 3D- and 4D-printing and intelligent devices, the monitoring and tuning of humidity in sustainable biobased materials offers excellent opportunities in e-nose sensing arrays and wearable devices compatible with operation at room conditions.
Collapse
Affiliation(s)
- Carina Esteves
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Susana I C J Palma
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Henrique M A Costa
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Cláudia Alves
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Gonçalo M C Santos
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Efthymia Ramou
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Ana Luísa Carvalho
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Vitor Alves
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
- UCIBIO - Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, Caparica, 2829-516, Portugal
| | - Ana C A Roque
- LEAF - Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, 1349-017, Portugal
| |
Collapse
|
44
|
Tang N, Zhang R, Zheng Y, Wang J, Khatib M, Jiang X, Zhou C, Omar R, Saliba W, Wu W, Yuan M, Cui D, Haick H. Highly Efficient Self-Healing Multifunctional Dressing with Antibacterial Activity for Sutureless Wound Closure and Infected Wound Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106842. [PMID: 34741350 DOI: 10.1002/adma.202106842] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/10/2021] [Indexed: 05/17/2023]
Abstract
Wound healing represents a major clinical and public healthcare problem that is frequently challenged by infection risks, detrimental consequences on the surrounding tissues, and difficulties to monitor the healing process. Here we report on a novel self-healing, antibacterial, and multifunctional wound dressing for sutureless wound closure and real-time monitoring of the healing parameters. The self-healing elastomer contains cetyltrimethylammonium bromide (CTAB) and has high mechanical toughness (35 MJ m-3 ), biocompatibility, and outstanding antibacterial activity (bactericidal rate is ≈90% in 12 h), enabling the wound dressing to effectively inhibit bacterial growth and accelerate infected wound healing. In vivo tests based on full-thickness skin incision model shows that the multifunctional wound dressing can help in contracting wound edges and facilitate wound closure and healing, as could be evidenced by notably dense and well-organized collagen deposition. The test provides an evidence that the integrated sensor array within the multifunctional wound dressing can monitor temperature, pH, and glucose level of the wound area in real-time, providing reliable and timely information of the condition of the wound. Ultimately, the reported multifunctional dressing would be of high value in managing the burden associated with wound healing via personalised monitoring and treatment approaches, digital and other people-centred solutions for health care.
Collapse
Affiliation(s)
- Ning Tang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Rongjun Zhang
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China
| | - Youbin Zheng
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
| | - Jing Wang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
| | - Muhammad Khatib
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
| | - Xue Jiang
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, P. R. China
| | - Cheng Zhou
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Rawan Omar
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
| | - Walaa Saliba
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
| | - Weiwei Wu
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, P. R. China
| | - Miaomiao Yuan
- The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China
| | - Daxiang Cui
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, 3200003, Israel
- School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, P. R. China
| |
Collapse
|
45
|
Yan X, Qu H, Chang Y, Duan X. Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection. ACTA CHIMICA SINICA 2022. [DOI: 10.6023/a22030134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
46
|
A DNA-derived phage nose using machine learning and artificial neural processing for diagnosing lung cancer. Biosens Bioelectron 2021; 194:113567. [PMID: 34481239 DOI: 10.1016/j.bios.2021.113567] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/04/2021] [Accepted: 08/12/2021] [Indexed: 12/26/2022]
Abstract
There is a growing interest in electronic nose-based diagnostic systems that are fast and portable. However, existing technologies are suitable only for operation in the laboratory, making them difficult to apply in a rapid, non-face-to-face, and field-suitable manner. Here, we demonstrate a DNA-derived phage nose (D2pNose) as a portable respiratory disease diagnosis system requiring no pretreatment. D2pNose was produced based on phage colour films implanted with DNA sequences from mammalian olfactory receptor cells, and as a result, it possesses the comprehensive reactivity of these cells. The manipulated surface chemistry of the genetically engineered phages was verified through a correlation analysis between the calculated and the experimentally measured reactivity. Breaths from 31 healthy subjects and 31 lung cancer patients were collected and exposed to D2pNose without pretreatment. With the help of deep learning and neural pattern separation, D2pNose has achieved a diagnostic success rate of over 75% and a classification success rate of over 86% for lung cancer based on raw human breath. Based on these results, D2pNose can be expected to be directly applicable to other respiratory diseases.
Collapse
|
47
|
Steiger C, Phan NV, Huang H, Sun H, Chu JN, Reker D, Gwynne D, Collins J, Tamang S, McManus R, Lopes A, Hayward A, Baron RM, Kim EY, Traverso G. Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2102861. [PMID: 34713599 PMCID: PMC8693042 DOI: 10.1002/advs.202102861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/07/2021] [Indexed: 06/13/2023]
Abstract
Continuous monitoring in the intensive care setting has transformed the capacity to rapidly respond with interventions for patients in extremis. Noninvasive monitoring has generally been limited to transdermal or intravascular systems coupled to transducers including oxygen saturation or pressure. Here it is hypothesized that gastric fluid (GF) and gases, accessible through nasogastric (NG) tubes, commonly found in intensive care settings, can provide continuous access to a broad range of biomarkers. A broad characterization of biomarkers in swine GF coupled to time-matched serum is conducted . The relationship and kinetics of GF-derived analyte level dynamics is established by correlating these to serum levels in an acute renal failure and an inducible stress model performed in swine. The ability to monitor ketone levels and an inhaled anaesthetic agent (isoflurane) in vivo is demonstrated with novel NG-compatible sensor systems in swine. Gastric access remains a main stay in the care of the critically ill patient, and here the potential is established to harness this establishes route for analyte evaluation for clinical management.
Collapse
Affiliation(s)
- Christoph Steiger
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyBrigham and Women's HospitalHarvard Medical SchoolBostonMA02115USA
| | - Nhi V. Phan
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Hen‐Wei Huang
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyBrigham and Women's HospitalHarvard Medical SchoolBostonMA02115USA
| | - Haoying Sun
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Jacqueline N. Chu
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyMassachusetts General HospitalHarvard Medical SchoolBostonMA02115USA
| | - Daniel Reker
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyBrigham and Women's HospitalHarvard Medical SchoolBostonMA02115USA
| | - Declan Gwynne
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyBrigham and Women's HospitalHarvard Medical SchoolBostonMA02115USA
| | - Joy Collins
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Siddartha Tamang
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Rebecca McManus
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Aaron Lopes
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Alison Hayward
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of Comparative MedicineMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Rebecca M. Baron
- Division of Pulmonary and Critical Care MedicineDepartment of MedicineBrigham and Women's Hospital, Harvard Medical SchoolBostonMA02115USA
| | - Edy Y. Kim
- Division of Pulmonary and Critical Care MedicineDepartment of MedicineBrigham and Women's Hospital, Harvard Medical SchoolBostonMA02115USA
| | - Giovanni Traverso
- Koch Institute for Integrative Cancer ResearchMassachusetts Institute of TechnologyCambridgeMA02139USA
- Division of GastroenterologyBrigham and Women's HospitalHarvard Medical SchoolBostonMA02115USA
- Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| |
Collapse
|
48
|
Liu H, Meng G, Deng Z, Nagashima K, Wang S, Dai T, Li L, Yanagida T, Fang X. Discriminating BTX Molecules by the Nonselective Metal Oxide Sensor-Based Smart Sensing System. ACS Sens 2021; 6:4167-4175. [PMID: 34735117 DOI: 10.1021/acssensors.1c01704] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Discriminating structurally similar volatile organic compounds (VOCs) molecules, such as benzene, toluene, and three xylene isomers (BTX), remains a significant challenge, especially, for metal oxide semiconductor (MOS) sensors, in which selectivity is a long-standing challenge. Recent progress indicates that temperature modulation of a single MOS sensor offers a powerful route in extracting the features of adsorbed gas analytes than conventional isothermal operation. Herein, a rectangular heating waveform is applied on NiO-, WO3-, and SnO2-based sensors to gradually activate the specific gas/oxide interfacial redox reaction and generate rich (electrical) features of adsorbed BTX molecules. Upon several signal preprocessing steps, the intrinsic feature of BTX molecules can be extracted by the linear discrimination analysis (LDA) or convolutional neural network (CNN) analysis. The combination of three distinct MOS sensors noticeably benefits the recognition accuracy (with a reduced number of training iterations). Finally, a prototype of a smart BTX recognition system (including sensing electronics, sensors, Wi-Fi module, UI, PC, etc.) based on temperature modulation has been explored, which enables a prompt, accurate, and stable identification of xylene isomers in the ambient air background and raises the hope of innovating the future advanced machine olfactory system.
Collapse
Affiliation(s)
- Hongyu Liu
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen 518118, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Gang Meng
- Anhui Institute of Optics and Fine Mechanics, and Key Lab of Photovoltaic and Energy Conservation Materials, Chinese Academy of Sciences, Hefei 230031, China
| | - Zanhong Deng
- Anhui Institute of Optics and Fine Mechanics, and Key Lab of Photovoltaic and Energy Conservation Materials, Chinese Academy of Sciences, Hefei 230031, China
| | - Kazuki Nagashima
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Shimao Wang
- Anhui Institute of Optics and Fine Mechanics, and Key Lab of Photovoltaic and Energy Conservation Materials, Chinese Academy of Sciences, Hefei 230031, China
| | - Tiantian Dai
- Anhui Institute of Optics and Fine Mechanics, and Key Lab of Photovoltaic and Energy Conservation Materials, Chinese Academy of Sciences, Hefei 230031, China
| | - Liang Li
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou 215006, China
| | - Takeshi Yanagida
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Xiaodong Fang
- College of New Materials and New Energies, Shenzhen Technology University, Shenzhen 518118, China
| |
Collapse
|
49
|
Ivaskovic P, Ainseba B, Nicolas Y, Toupance T, Tardy P, Thiéry D. Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge? ACS Sens 2021; 6:3824-3840. [PMID: 34704740 DOI: 10.1021/acssensors.1c00917] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.
Collapse
Affiliation(s)
- Petra Ivaskovic
- UMR 1065, Santé et Agroécologie du Vignoble, INRAE, 33140 Villenave d’Ornon, France
- UMR 5218, Laboratoire de l’Intégration du Matériau au Système, 33405 Talence, France
| | - Bedr’Eddine Ainseba
- UMR 5251, Institut de Mathématiques de Bordeaux, Université de Bordeaux, 33405 Talence, France
| | - Yohann Nicolas
- UMR 5255, Institut des Sciences Moléculaires, Université de Bordeaux, 33405 Talence, France
| | - Thierry Toupance
- UMR 5255, Institut des Sciences Moléculaires, Université de Bordeaux, 33405 Talence, France
| | - Pascal Tardy
- UMR 5218, Laboratoire de l’Intégration du Matériau au Système, 33405 Talence, France
| | - Denis Thiéry
- UMR 1065, Santé et Agroécologie du Vignoble, INRAE, 33140 Villenave d’Ornon, France
| |
Collapse
|
50
|
Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. SENSORS 2021; 21:s21227620. [PMID: 34833693 PMCID: PMC8619411 DOI: 10.3390/s21227620] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 02/07/2023]
Abstract
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
Collapse
|