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Paleczek A, Grochala J, Grochala D, Słowik J, Pihut M, Loster JE, Rydosz A. Noninvasive Total Cholesterol Level Measurement Using an E-Nose System and Machine Learning on Exhaled Breath Samples. ACS Sens 2024. [PMID: 39577863 DOI: 10.1021/acssensors.4c02198] [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: 11/24/2024]
Abstract
In this paper, the first e-nose system coupled with machine learning algorithm for noninvasive measurement of total cholesterol level based on exhaled air sample was proposed. The study was conducted with the participation of 151 people, from whom a breath sample was collected, and the level of total cholesterol was measured. The breath sample was examined using e-nose and gas sensors, such as TGS1820, TGS2620, TGS2600, MQ3, Semeatech 7e4 NO2 and 7e4 H2S, SGX_NO2, SGX_H2S, K33, AL-03P, and AL-03S. The LGBMRegressor algorithm was used to predict cholesterol level based on the breath sample. Machine learning algorithms were developed for the entire measurement range and for the norm range ≤200 mg/dL achieving MAPE 13.7% and 8%, respectively. The results show that it is possible to develop a noninvasive device to measure total cholesterol level from breath.
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Affiliation(s)
- Anna Paleczek
- AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland
| | - Justyna Grochala
- Department of Prosthodontics and Orthodontics, Dental Institute, Faculty of Medicine, Jagiellonian University Medical College, ul. św. Anny 12, Kraków 31-008, Poland
| | - Dominik Grochala
- AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland
| | - Jakub Słowik
- AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland
- University Clinical Hospital in Opole, Institute of Medical Sciences, University of Opole, aleja Wincentego Witosa 26, Opole 46-020, Poland
| | - Małgorzata Pihut
- Department of Prosthodontics and Orthodontics, Dental Institute, Faculty of Medicine, Jagiellonian University Medical College, ul. św. Anny 12, Kraków 31-008, Poland
| | - Jolanta E Loster
- Professor Loster's Orthodontics, Private practice, Faculty of Medicine, Jagiellonian University Medical College, Bartłomieja Nowodworskiego 4, Krakow 30-433, Poland
| | - Artur Rydosz
- AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland
- The University Hospital in Krakow, Laboratory of Functional and Virtual Medical 3D Imaging [3D-vFMi(maging)/3D-FM], Jakubowskiego 2 Street, Krakow 30-688, Poland
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Gudiño-Ochoa A, García-Rodríguez JA, Cuevas-Chávez JI, Ochoa-Ornelas R, Navarrete-Guzmán A, Vidrios-Serrano C, Sánchez-Arias DA. Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data. Bioengineering (Basel) 2024; 11:1065. [PMID: 39593725 PMCID: PMC11591061 DOI: 10.3390/bioengineering11111065] [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: 09/25/2024] [Revised: 10/17/2024] [Accepted: 10/23/2024] [Indexed: 11/28/2024] Open
Abstract
Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.
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Affiliation(s)
- Alberto Gudiño-Ochoa
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Julio Alberto García-Rodríguez
- Centro Universitario del Sur (CUSUR), Departamento de Ciencias Computacionales e Innovación Tecnológica, Universidad de Guadalajara, Ciudad Guzmán 49000, Mexico
| | - Jorge Ivan Cuevas-Chávez
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Raquel Ochoa-Ornelas
- Systems and Computation Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico;
| | - Antonio Navarrete-Guzmán
- Electrical and Electronic Engineering Department, Tecnológico Nacional de México/Instituto Tecnológico de Tepic, Tepic 63175, Mexico;
- Academic Unit of Basic Sciences and Engineering, Universidad Autónoma de Nayarit, Tepic 63000, Mexico;
| | - Carlos Vidrios-Serrano
- Academic Unit of Basic Sciences and Engineering, Universidad Autónoma de Nayarit, Tepic 63000, Mexico;
| | - Daniel Alejandro Sánchez-Arias
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
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Wei S, Li Z, Murugappan K, Li Z, Lysevych M, Vora K, Tan HH, Jagadish C, Karawdeniya BI, Nolan CJ, Tricoli A, Fu L. Nanowire Array Breath Acetone Sensor for Diabetes Monitoring. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309481. [PMID: 38477429 PMCID: PMC11109654 DOI: 10.1002/advs.202309481] [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: 12/05/2023] [Revised: 02/18/2024] [Indexed: 03/14/2024]
Abstract
Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000 ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800 ppb) to people at high risk of DKA (>75 ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations in simulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.
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Affiliation(s)
- Shiyu Wei
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Zhe Li
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Krishnan Murugappan
- Commonwealth Scientific and Industrial Research Organisation (CSIRO)Mineral ResourcesPrivate Bag 10Clayton SouthVIC3169Australia
- Nanotechnology Research LaboratoryResearch School of ChemistryCollege of ScienceThe Australian National UniversityCanberraACT2600Australia
| | - Ziyuan Li
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Mykhaylo Lysevych
- Australian National Fabrication FacilityThe Australian National UniversityCanberraACT2600Australia
| | - Kaushal Vora
- Australian National Fabrication FacilityThe Australian National UniversityCanberraACT2600Australia
| | - Hark Hoe Tan
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Chennupati Jagadish
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Buddini I Karawdeniya
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
| | - Christopher J Nolan
- School of Medicine and PsychologyCollege of Health and MedicineThe Australian National UniversityCanberraACT2600Australia
- Department of Diabetes and EndocrinologyThe Canberra HospitalGarranACT2605Australia
| | - Antonio Tricoli
- Nanotechnology Research LaboratoryResearch School of ChemistryCollege of ScienceThe Australian National UniversityCanberraACT2600Australia
- Nanotechnology Research LaboratoryFaculty of EngineeringThe University of SydneyCamperdown2006Australia
| | - Lan Fu
- Australian Research Council Centre of Excellence for Transformative Meta‐Optical SystemsDepartment of Electronic Materials EngineeringResearch School of PhysicsThe Australian National UniversityCanberraACT2600Australia
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Gudiño-Ochoa A, García-Rodríguez JA, Ochoa-Ornelas R, Cuevas-Chávez JI, Sánchez-Arias DA. Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose. SENSORS (BASEL, SWITZERLAND) 2024; 24:1294. [PMID: 38400451 PMCID: PMC10891698 DOI: 10.3390/s24041294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.
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Affiliation(s)
- Alberto Gudiño-Ochoa
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Julio Alberto García-Rodríguez
- Centro Universitario del Sur, Departamento de Ciencias Computacionales e Innovación Tecnológica, Universidad de Guadalajara, Ciudad Guzmán 49000, Mexico
| | - Raquel Ochoa-Ornelas
- Systems and Computation Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico;
| | - Jorge Ivan Cuevas-Chávez
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
| | - Daniel Alejandro Sánchez-Arias
- Electronics Department, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Guzmán, Ciudad Guzmán 49100, Mexico; (A.G.-O.); (J.I.C.-C.); (D.A.S.-A.)
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Nyabadza A, McCarthy É, Makhesana M, Heidarinassab S, Plouze A, Vazquez M, Brabazon D. A review of physical, chemical and biological synthesis methods of bimetallic nanoparticles and applications in sensing, water treatment, biomedicine, catalysis and hydrogen storage. Adv Colloid Interface Sci 2023; 321:103010. [PMID: 37804661 DOI: 10.1016/j.cis.2023.103010] [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: 06/27/2023] [Revised: 08/30/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023]
Abstract
This article provides an in-depth analysis of various fabrication methods of bimetallic nanoparticles (BNP), including chemical, biological, and physical techniques. The review explores BNP's diverse uses, from well-known applications such as sensing water treatment and biomedical uses to less-studied areas like breath sensing for diabetes monitoring and hydrogen storage. It cites results from over 1000 researchers worldwide and >300 peer-reviewed articles. Additionally, the article discusses current trends, actionable recommendations, and the importance of synthetic analysis for industry players looking to optimize manufacturing techniques for specific applications. The article also evaluates the pros and cons of various fabrication methods, highlighting the potential of plant extract synthesis for mass production of capped BNPs. However, it warns that this method may not be suitable for certain applications requiring ligand-free surfaces. In contrast, physical methods like laser ablation offer better control and reactivity, especially for applications where ligand-free surfaces are critical. The report underscores the environmental benefits of plant extract synthesis compared to chemical methods that use hazardous chemicals and pose risks to extraction, production, and disposal. The article emphasizes the need for life cycle assessment (LCA) articles in the literature, given the growing volume of research on nanotechnology materials. This article caters to researchers at all stages and applies to various fields applying nanomaterials.
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Affiliation(s)
- Anesu Nyabadza
- I-Form Advanced Manufacturing Centre Research, Dublin City University, Glasnevin, Dublin 9, Ireland; EPSRC & SFI Centre for Doctoral Training (CDT) in Advanced Metallic Systems, School of Mechanical & Manufacturing Engineering, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland; Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland.
| | - Éanna McCarthy
- I-Form Advanced Manufacturing Centre Research, Dublin City University, Glasnevin, Dublin 9, Ireland; Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Mayur Makhesana
- Mechanical Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat 382481, India
| | - Saeid Heidarinassab
- I-Form Advanced Manufacturing Centre Research, Dublin City University, Glasnevin, Dublin 9, Ireland; EPSRC & SFI Centre for Doctoral Training (CDT) in Advanced Metallic Systems, School of Mechanical & Manufacturing Engineering, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland; Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Anouk Plouze
- Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland; Conservatoire National des arts et Métiers (CNAM), 61 Rue du Landy, 93210 Saint-Denis, France
| | - Mercedes Vazquez
- I-Form Advanced Manufacturing Centre Research, Dublin City University, Glasnevin, Dublin 9, Ireland; EPSRC & SFI Centre for Doctoral Training (CDT) in Advanced Metallic Systems, School of Mechanical & Manufacturing Engineering, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland; Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Dermot Brabazon
- I-Form Advanced Manufacturing Centre Research, Dublin City University, Glasnevin, Dublin 9, Ireland; EPSRC & SFI Centre for Doctoral Training (CDT) in Advanced Metallic Systems, School of Mechanical & Manufacturing Engineering, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland; Advanced Processing Technology Research Centre, Dublin City University, Glasnevin, Dublin 9, Ireland
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Li Y, Wei X, Zhou Y, Wang J, You R. Research progress of electronic nose technology in exhaled breath disease analysis. MICROSYSTEMS & NANOENGINEERING 2023; 9:129. [PMID: 37829158 PMCID: PMC10564766 DOI: 10.1038/s41378-023-00594-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 10/14/2023]
Abstract
Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.
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Affiliation(s)
- Ying Li
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Xiangyang Wei
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Yumeng Zhou
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Jing Wang
- School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China
| | - Rui You
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
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Jang WB, Yi D, Nguyen TM, Lee Y, Lee EJ, Choi J, Kim YH, Choi E, Oh J, Kwon S. Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications. Adv Healthc Mater 2023; 12:e2300845. [PMID: 37449876 PMCID: PMC11469111 DOI: 10.1002/adhm.202300845] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
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Affiliation(s)
- Woong Bi Jang
- Laboratory for Vascular Medicine and Stem Cell BiologyDepartment of PhysiologyMedical Research InstituteSchool of MedicinePusan National UniversityYangsan50612Republic of Korea
- Convergence Stem Cell Research CenterPusan National UniversityYangsan50612Republic of Korea
| | - Dongwon Yi
- Division of Endocrinology and MetabolismDepartment of Internal MedicinePusan National University Yangsan HospitalPusan National University School of MedicineYangsan50612Republic of Korea
| | - Thanh Mien Nguyen
- Bio‐IT Fusion Technology Research InstitutePusan National UniversityBusan46241Republic of Korea
| | - Yujin Lee
- Department of Nano Fusion TechnologyPusan National UniversityBusan46214Republic of Korea
| | - Eun Ji Lee
- Laboratory for Vascular Medicine and Stem Cell BiologyDepartment of PhysiologyMedical Research InstituteSchool of MedicinePusan National UniversityYangsan50612Republic of Korea
- Convergence Stem Cell Research CenterPusan National UniversityYangsan50612Republic of Korea
| | - Jaewoo Choi
- Laboratory for Vascular Medicine and Stem Cell BiologyDepartment of PhysiologyMedical Research InstituteSchool of MedicinePusan National UniversityYangsan50612Republic of Korea
- Convergence Stem Cell Research CenterPusan National UniversityYangsan50612Republic of Korea
| | - You Hwan Kim
- Department of Nano Fusion TechnologyPusan National UniversityBusan46214Republic of Korea
| | - Eun‐Jung Choi
- Department of Nano Fusion TechnologyPusan National UniversityBusan46214Republic of Korea
| | - Jin‐Woo Oh
- Bio‐IT Fusion Technology Research InstitutePusan National UniversityBusan46241Republic of Korea
- Department of Nano Fusion TechnologyPusan National UniversityBusan46214Republic of Korea
- Korea Nanobiotechnology CenterPusan National UniversityBusan46241Republic of Korea
| | - Sang‐Mo Kwon
- Laboratory for Vascular Medicine and Stem Cell BiologyDepartment of PhysiologyMedical Research InstituteSchool of MedicinePusan National UniversityYangsan50612Republic of Korea
- Convergence Stem Cell Research CenterPusan National UniversityYangsan50612Republic of Korea
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Tonezzer M, Bazzanella N, Gasperi F, Biasioli F. Nanosensor Based on Thermal Gradient and Machine Learning for the Detection of Methanol Adulteration in Alcoholic Beverages and Methanol Poisoning. SENSORS 2022; 22:s22155554. [PMID: 35898057 PMCID: PMC9329758 DOI: 10.3390/s22155554] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 01/01/2023]
Abstract
Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of distinguishing methanol from ethanol would be very useful. Here, we present a resistive gas sensor, based on tin oxide nanowires, that works in a thermal gradient. By combining responses at various temperatures and using machine learning algorithms (PCA, SVM, LDA), the device can distinguish methanol from ethanol in a wide range of concentrations (1–100 ppm) in both dry air and under different humidity conditions (25–75% RH). The proposed sensor, which is small and inexpensive, demonstrates the ability to distinguish methanol from ethanol at different concentrations and could be developed both to detect the adulteration of alcoholic beverages and to quickly recognize methanol poisoning.
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Affiliation(s)
- Matteo Tonezzer
- Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all’Adige, Italy; (F.G.); (F.B.)
- Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all’Adige, Italy
- IMEM-CNR, Sede di Trent o-FBK, Via alla Cascata 56/C, Povo, 38123 Trento, Italy
- Correspondence: ; Tel.: +39-0461-314-828
| | - Nicola Bazzanella
- Department of Physics, Università degli Studi di Trento, Povo, 38123 Trento, Italy;
| | - Flavia Gasperi
- Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all’Adige, Italy; (F.G.); (F.B.)
- Center Agriculture Food Environment, University of Trento/Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all’Adige, Italy
| | - Franco Biasioli
- Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 San Michele all’Adige, Italy; (F.G.); (F.B.)
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Swinarew AS, Flak T, Jarosińska A, Garczyk Ż, Gabor J, Skoczyński S, Brożek G, Paluch J, Popczyk M, Stanula A, Stach S. Polyurethane-Based Porous Carbons Suitable for Medical Application. MATERIALS 2022; 15:ma15093313. [PMID: 35591653 PMCID: PMC9101738 DOI: 10.3390/ma15093313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/19/2022] [Accepted: 04/29/2022] [Indexed: 11/24/2022]
Abstract
The main aim of the study was to synthesize and analyze spectral data to determine the structure and stereometry of the carbon-based porous material internal structure. Samples of a porous biomaterial were synthesized through anionic polymerization following our own patent and then carbonized. The samples were investigated using MALDI ToF MS, FTIR ATR spectroscopy, optic microscopy, SEM, confocal laser scanning microscopy and CMT imaging. The analysis revealed the chemical and stereological structure of the obtained porous biomaterial. Then, the parameters characterizing the pore geometry and the porosity of the samples were calculated. The developed material can be used to collect adsorption of breathing phase samples to determine the parity composition of exhaled air.
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Affiliation(s)
- Andrzej S. Swinarew
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
- Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Mikołowska 72A, 40-065 Katowice, Poland;
- Correspondence:
| | - Tomasz Flak
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
| | - Agnieszka Jarosińska
- Department of Internal Medicine, Autoimmune and Metabolic Diseases, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyków 14, 40-572 Katowice, Poland;
| | - Żaneta Garczyk
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
| | - Jadwiga Gabor
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
| | - Szymon Skoczyński
- Department of Pneumonology, School of Medicine in Katowice, Medical University of Silesia in Katowice, 40-055 Katowice, Poland;
| | - Grzegorz Brożek
- Department of Epidemiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Medyków 18, 40-752 Katowice, Poland;
| | - Jarosław Paluch
- Department of Laryngology, Faculty of Medical Sciences in Katowice, Medical University of Silesia in Katowice, ul. Francuska 20-24, 40-027 Katowice, Poland;
| | - Magdalena Popczyk
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
| | - Arkadiusz Stanula
- Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Mikołowska 72A, 40-065 Katowice, Poland;
| | - Sebastian Stach
- Faculty of Science and Technology, University of Silesia in Katowice, 75 Pułku Piechoty 1A, 41-500 Chorzów, Poland; (T.F.); (Ż.G.); (J.G.); (M.P.); (S.S.)
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Fuśnik Ł, Szafraniak B, Paleczek A, Grochala D, Rydosz A. A Review of Gas Measurement Set-Ups. SENSORS 2022; 22:s22072557. [PMID: 35408172 PMCID: PMC9002727 DOI: 10.3390/s22072557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/16/2022] [Accepted: 03/24/2022] [Indexed: 11/21/2022]
Abstract
Measurements of the properties of gas-sensitive materials are a subject of constant research, including continuous developments and improvements of measurement methods and, consequently, measurement set-ups. Preparation of the test set-up is a key aspect of research, and it has a significant impact on the tested sensor. This paper aims to review the current state of the art in the field of gas-sensing measurement and provide overall conclusions of how the different set-ups impact the obtained results.
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