Ultrasensitive silicon nanowire for real-world gas sensing: noninvasive diagnosis of cancer from breath volatolome

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Mapping the intellectual structure and emerging trends for the application of nanomaterials in gastric cancer: A bibliometric study

BACKGROUND

Recent reviews have outlined the main nanomaterials used in relation to gastrointestinal tumors and described the basic properties of these materials. However, the research hotspots and trends in the application of nanomaterials in gastric cancer (GC) remain obscure.

AIM

To demonstrate the knowledge structure and evolutionary trends of research into the application of nanomaterials in GC.

METHODS

Publications related to the application of nanomaterials in GC were retrieved from the Web of Science Core Collection for this systematic review and bibliometric study. VOSviewer and CiteSpace were used for bibliometric and visualization analyses.

RESULTS

From 2000 to 2022, the application of nanomaterials in GC developed rapidly. The keyword co-occurrence analysis showed that the related research topics were divided into three clusters: (1) The application of nanomaterials in GC treatment; (2) The application and toxicity of nanomaterials in GC diagnosis; and (3) The effects of nanomaterials on the biological behavior of GC cells. Complexes, silver nanoparticles, and green synthesis are the latest high-frequency keywords that represent promising future research directions.

CONCLUSION

The application of nanomaterials in GC diagnosis and treatment and the mechanisms of their effects on GC cells have been major themes in this field over the past 23 years.

Calibration of a Low-Cost Methane Sensor Using Machine Learning

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Research trends on nanomaterials in gastric cancer: a bibliometric analysis from 2004 to 2023

BACKGROUND

Gastric cancer is one of the leading causes of cancer-related deaths worldwide. In recent years, an increasing number of studies aimed at designing and developing nanomaterials for use in diagnosing and treating gastric cancer have been conducted. In this study, we aimed to comprehensively assess the current status and trends of the research on the application of nanomaterials in gastric cancer through a bibliometric analysis.

METHODS

Studies focusing on nanomaterials and gastric cancer were retrieved from the Web of Science Core Collection database and relevant articles were selected for inclusion in the study according to the inclusion criteria. Bibliometric and visual analysis of the included publications was performed using VOSviewer and CiteSpace.

RESULTS

A total of 793 studies were included. An increase in annual publications was observed from 2004 to 2023. China, Iran and the USA were the dominant countries in this field, accounting for 66.1%, 11.5% and 7.2% of publications, respectively. Shanghai Jiao Tong University and Cui DX were the most influential institution and author, respectively. The International Journal of Nanomedicine was the most prolific journal; Biomaterials was the most cited and most cocited journal. Nanomaterial-related drug delivery and anticancer mechanisms were found to be the most widely researched aspects, and green synthesis and anticancer mechanisms are recent research hotspots.

CONCLUSION

In this study, we summarized the characteristics of publications and identified the most influential countries, institutions, authors, journals, hot topics and trends regarding the application of nanomaterials in gastric cancer.

A Platinum Nanowire Sensor for Ethylene in Air

A single platinum nanowire (PtNW) chemiresistive sensor for ethylene gas is reported. In this application, the PtNW performs three functions: (1) Joule self-heating to a specified temperature, (2) in situ resistance-based temperature measurement, and (3) detection of ethylene in air as a resistance change. Ethylene gas in air is detected as a reduction in nanowire resistance by up to 4.5% for concentrations ranging from 1 to 30 ppm in an optimum NW temperature range from 630 to 660 K. This response is rapid (30-100 s), reversible, and reproducible for repetitive ethylene pulses. A threefold increase in signal amplitude is observed as the NW thickness is reduced from 60 to 20 nm, commensurate with a signal transduction mechanism involving surface electron scattering.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

1D and 2D Field Effect Transistors in Gas Sensing: A Comprehensive Review

Rapid progress in the synthesis and fundamental understanding of 1D and 2D materials have solicited the incorporation of these nanomaterials into sensor architectures, especially field effect transistors (FETs), for the monitoring of gas and vapor in environmental, food quality, and healthcare applications. Yet, several challenges have remained unaddressed toward the fabrication of 1D and 2D FET gas sensors for real-field applications, which are related to properties, synthesis, and integration of 1D and 2D materials into the transistor architecture. This review paper encompasses the whole assortment of 1D-i.e., metal oxide semiconductors (MOXs), silicon nanowires (SiNWs), carbon nanotubes (CNTs)-and 2D-i.e., graphene, transition metal dichalcogenides (TMD), phosphorene-materials used in FET gas sensors, critically dissecting how the material synthesis, surface functionalization, and transistor fabrication impact on electrical versus sensing properties of these devices. Eventually, pros and cons of 1D and 2D FETs for gas and vapor sensing applications are discussed, pointing out weakness and highlighting future directions.

Advances in silicon nanowire applications in energy generation, storage, sensing, and electronics: a review

Nanowire-based technological advancements thrive in various fields, including energy generation and storage, sensors, and electronics. Among the identified nanowires, silicon nanowires (SiNWs) attract much attention as they possess unique features, including high surface-to-volume ratio, high electron mobility, bio-compatibility, anti-reflection, and elasticity. They were tested in domains of energy generation (thermoelectric, photo-voltaic, photoelectrochemical), storage (lithium-ion battery (LIB) anodes, super capacitors), and sensing (bio-molecules, gas, light, etc). These nano-structures were found to improve the performance of the system in terms of efficiency, stability, sensitivity, selectivity, cost, rapidity, and reliability. This review article scans and summarizes the significant developments that occurred in the last decade concerning the application of SiNWs in the fields of thermoelectric, photovoltaic, and photoelectrochemical power generation, storage of energy using LIB anodes, biosensing, and disease diagnostics, gas and pH sensing, photodetection, physical sensing, and electronics. The functionalization of SiNWs with various nanomaterials and the formation of heterostructures for achieving improved characteristics are discussed. This article will be helpful to researchers in the field of nanotechnology about various possible applications and improvements that can be realized using SiNW.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Application of nanotechnology in the early diagnosis and comprehensive treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) is a common malignant tumour of the digestive system that seriously threatens human health. Due to the unique organ structure of the gastrointestinal tract, endoscopic and MRI diagnoses of GIC in the clinic share the problem of low sensitivity. The ineffectiveness of drugs and high recurrence rates in surgical and drug therapies are the main factors that impact the curative effect in GIC patients. Therefore, there is an urgent need to improve diagnostic accuracies and treatment efficiencies. Nanotechnology is widely used in the diagnosis and treatment of GIC by virtue of its unique size advantages and extensive modifiability. In the diagnosis and treatment of clinical GIC, surface-enhanced Raman scattering (SERS) nanoparticles, electrochemical nanobiosensors and magnetic nanoparticles, intraoperative imaging nanoparticles, drug delivery systems and other multifunctional nanoparticles have successfully improved the diagnosis and treatment of GIC. It is important to further improve the coordinated development of nanotechnology and GIC diagnosis and treatment. Herein, starting from the clinical diagnosis and treatment of GIC, this review summarizes which nanotechnologies have been applied in clinical diagnosis and treatment of GIC in recent years, and which cannot be applied in clinical practice. We also point out which challenges must be overcome by nanotechnology in the development of the clinical diagnosis and treatment of GIC and discuss how to quickly and safely combine the latest nanotechnology developed in the laboratory with clinical applications. Finally, we hope that this review can provide valuable reference information for researchers who are conducting cross-research on GIC and nanotechnology.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Smartphone Case-Based Gas Sensing Platform for On-site Acetone Tracking

Gas sensor-embedded smartphones would offer the opportunity of on-site tracking of gas molecules for various applications, for example, harmful air pollutant alarms or noninvasive assessment of health status. Nevertheless, high power consumption and difficulty in replacing malfunctioned sensors as well as limited space in the smartphone to host the sensor restrain the relevant advancements. In this article, we create a smartphone case-based sensing platform by integrating the functional units into a smartphone case, which performs a low detection limit of 117 ppb to acetone and high specificity. Particularly, dimming glass-regulated light fidelity (Li-Fi) communication is successfully developed, allowing the sensing platform to operate with relatively low power consumption (around 217 mW). Experimental proof on harmful gas sensing and potential clinic application is implemented with the sensing platform, demonstrating satisfactory sensing performance and acceptable health risk pre-warning accuracy (87%). Thus, the developed smartphone case-based sensing platform would be a good candidate for realizing harmful gas alarms and noninvasive assessment of health status.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners

OBJECTIVES

This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored.

METHODS

398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test.

RESULTS

Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy.

CONCLUSION

Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.

Porous Pb-Doped ZnO Nanobelts with Enriched Oxygen Vacancies: Preparation and Their Chemiresistive Sensing Performance

Among various approaches to improve the sensing performance of metal oxide, the metal-doped method is perceived as effective, and has received great attention and is widely investigated. However, it is still a challenge to construct heterogeneous metal-doped metal oxide with an excellent sensing performance. In the present study, porous Pb-doped ZnO nanobelts were prepared by a simply partial cation exchange method, followed by in situ thermal oxidation. Detailed characterization confirmed that Pb was uniformly distributed on porous nanobelts. Additionally, it occupied the Zn situation, not forming its oxides. The gas-sensing measurements revealed that 0.61 at% Pb-doped ZnO porous nanobelts exhibited a selectively enhanced response with long-term stability toward n-butanol among the investigated VOCs. The relative response to 50 ppm of n-butanol was up to 47.7 at the working temperature of 300 °C. Additionally, the response time was short (about 5 s). These results were mainly ascribed to the porous nanostructure, two-dimensional belt-like morphology, enriched oxygen vacancies and the specific synergistic effect from the Pb dopant. Finally, a possible sensing mechanism of porous Pb-doped ZnO nanobelts is proposed and discussed.

Detection of trace volatile organic compounds in spiked breath samples: a leap towards breathomics

Breathomics is the future of non-invasive point-of-care devices. The field of breathomics can be split into the isolation of disease-specific volatile organic compounds (VOCs) and their detection. In the present work, an array of five quartz tuning fork (QTF)-based sensors modified by polymer with nanomaterial additive has been utilized. The array has been used to detect samples of human breath spiked with ∼0.5 ppm of known VOCs namely, acetone, acetaldehyde, octane, decane, ethanol, methanol, styrene, propylbenzene, cyclohexanone, butanediol, and isopropyl alcohol which are bio-markers for certain diseases. Polystyrene was used as the base polymer and it was functionalized with 4 different fillers namely, silver nanoparticles-reduced graphene oxide composite, titanium dioxide nanoparticles, zinc ferrite nanoparticles-reduced graphene oxide composite, and cellulose acetate. Each of these fillers enhanced the selectivity of a particular sensor towards a certain VOC compared to the pristine polystyrene-modified sensor. Their interaction with the VOCs in changing the mechanical properties of polymer giving rise to change in the resonant frequency of QTF is used as sensor response for detection. The interaction of functionalized polymers with VOCs was analyzed by FTIR and UV-vis spectroscopy. The collective sensor response of five sensors is used to identify VOCs using an ensemble classifier with 92.8% accuracy of prediction. The accuracy of prediction improved to 96% when isopropyl alcohol, ethanol, and methanol were considered as one class.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Gate-controlled gas sensor utilizing 1D-2D hybrid nanowires network

Novel gas sensors that work at room temperature are attracting attention due to their low energy consumption and stability in the presence of toxic gases. However, the development of sensing characteristics at room temperature is still a primary challenge. Diverse reaction pathways and low adsorption energy for gas molecules are required to fabricate a gas sensor that works at room temperature with high sensitivity, selectivity, and efficiency. Therefore, we enhanced the gas sensing performance at room temperature by constructing hybridized nanostructure of 1D-2D hybrid of SnSe2 layers and SnO2 nanowire networks and by controlling the back-gate bias (Vg = 1.5 V). The response time was dramatically reduced by lowering the energy barrier for the adsorption on the reactive sites, which are controlled by the back gate. Consequently, we believe that this research could contribute to improving the performance of gas sensors that work at room temperature.

Volatile organic compounds as a potential screening tool for neoplasm of the digestive system: a meta-analysis

This meta-analysis was aimed to estimate the diagnostic performance of volatile organic compounds (VOCs) as a potential novel tool to screen for the neoplasm of the digestive system. An integrated literature search was performed by two independent investigators to identify all relevant studies investigating VOCs in diagnosing neoplasm of the digestive system from inception to 7th December 2020. STATA and Revman software were used for data analysis. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate mixed model was used and meta-regression and subgroup analysis were performed to identify possible sources of heterogeneity. A total of 36 studies comprised of 1712 cases of neoplasm and 3215 controls were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.87 (95% confidence interval (CI) 0.83–0.90), specificity of 0.86 (95% CI 0.82–0.89), a positive likelihood ratio of 6.18 (95% CI 4.68–8.17), and a negative likelihood ratio of 0.15 (95% CI 0.12–0.20). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing neoplasm of the digestive system were 40.61 (95% CI 24.77–66.57) and 0.93 (95% CI 0.90–0.95), respectively. Our analyses revealed that VOCs analysis could be considered as a potential novel tool to screen for malignant diseases of the digestive system.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Breath volatile organic compound analysis: an emerging method for gastric cancer detection

Gastric cancer is a common malignancy, being the fifth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Diagnosis of gastric cancer at the early stage is critical to effectively improve the survival rate. However, a substantial proportion of patients with gastric cancer in the early stages lack specific symptoms or are asymptomatic. Moreover, the imaging techniques currently used for gastric cancer screening, such as computed tomography and barium examination, are usually radioactive and have low sensitivity and specificity. Even though endoscopy has high accuracy for gastric cancer screening, its application is limited by the invasiveness of the technique. Breath analysis is an economic, effective, easy to perform, non-invasive detection method, and has no undesirable side effects on subjects. Extensive worldwide research has been conducted on breath volatile organic compounds (VOCs), which reveals its prospect as a potential method for gastric cancer detection. Many interesting results have been obtained and innovative methods have been introduced in this subject; hence, an extensive review would be beneficial. By providing a comprehensive list of breath VOCs identified by gastric cancer would promote further research in this field. This review summarizes the commonly used technologies for exhaled breath analysis, focusing on the application of analytical instruments in the detection of breath VOCs in gastric cancers, and the alterations in the profile of breath biomarkers in gastric cancer patients are discussed as well.

Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds

Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify "patients with diabetic ketosis" from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

Recent Advances in Silicon FET Devices for Gas and Volatile Organic Compound Sensing

Highly sensitive and selective gas and volatile organic compound (VOC) sensor platforms with fast response and recovery kinetics are in high demand for environmental health monitoring, industry, and medical diagnostics. Among the various categories of gas sensors studied to date, field effect transistors (FETs) have proved to be an extremely efficient platform due to their miniaturized form factor, high sensitivity, and ultra-low power consumption. Despite the advent of various kinds of new materials, silicon (Si) still enjoys the advantages of excellent and reproducible electronic properties and compatibility with complementary metal–oxide–semiconductor (CMOS) technologies for integrated multiplexing and signal processing. This review gives an overview of the recent developments in Si FETs for gas and VOC sensing. We categorised the Si FETs into Si nanowire (NW) FETs; planar Si FETs, in which the Si channel is either a part of the silicon on insulator (SOI) or the bulk Si, as in conventional FETs; and electrostatically formed nanowire (EFN) FETs. The review begins with a brief introduction, followed by a description of the Si NW FET gas and VOC sensors. A brief description of the various fabrication strategies of Si NWs and the several functionalisation methods to improve the sensing performances of Si NWs are also provided. Although Si NW FETs have excellent sensing properties, they are far from practical realisation due to the extensive fabrication procedures involved, along with other issues that are critically assessed briefly. Then, we describe planar Si FET sensors, which are much closer to real-world implementation. Their simpler device architecture combined with excellent sensing properties enable them as an efficient platform for gas sensing. The third category, the EFN FET sensors, proved to be another potential platform for gas sensing due to their intriguing properties, which are elaborated in detail. Finally, the challenges and future opportunities for gas sensing are addressed.

An Overview of Artificial Olfaction Systems with a Focus on Surface Plasmon Resonance for the Analysis of Volatile Organic Compounds

The last three decades have witnessed an increasing demand for novel analytical tools for the analysis of gases including odorants and volatile organic compounds (VOCs) in various domains. Traditional techniques such as gas chromatography coupled with mass spectrometry, although very efficient, present several drawbacks. Such a context has incited the research and industrial communities to work on the development of alternative technologies such as artificial olfaction systems, including gas sensors, olfactory biosensors and electronic noses (eNs). A wide variety of these systems have been designed using chemiresistive, electrochemical, acoustic or optical transducers. Among optical transduction systems, surface plasmon resonance (SPR) has been extensively studied thanks to its attractive features (high sensitivity, label free, real-time measurements). In this paper, we present an overview of the advances in the development of artificial olfaction systems with a focus on their development based on propagating SPR with different coupling configurations, including prism coupler, wave guide, and grating.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Nanowire-Enabled Bioelectronics

Bioelectronics explores the use of electronic devices for applications in signal transduction at their interfaces with biological systems. The miniaturization of the bioelectronic systems has enabled seamless integration at these interfaces and is providing new scientific and technological opportunities. In particular, nanowire-based devices can yield smaller sized and unique geometry detectors that are difficult to access with standard techniques, and thereby can provide advantages in sensitivity with reduced invasiveness. In this review, we focus on nanowire-enabled bioelectronics. First, we provide an overview of synthetic studies for designed growth of semiconductor nanowires of which structure and composition are controlled to enable key elements for bioelectronic devices. Second, we review nanowire field-effect transistor sensors for highly sensitive detection of biomolecules, their applications in diagnosis and drug discovery, and methods for sensitivity enhancement. We then turn to recent progress in nanowire-enabled studies of electrogenic cells, including cardiomyocytes and neurons. Representative advances in electrical recording using nanowire electronic devices for single cell measurements, cell network mapping, and three-dimensional recordings of synthetic and natural tissues, and in vivo brain mapping are highlighted. Finally, we overview the key challenges and opportunities of nanowires for fundamental research and translational applications.

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning-A Review

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

Breath analysis for the detection of digestive tract malignancies: systematic review

Background

In recent decades there has been growing interest in the use of volatile organic compounds (VOCs) in exhaled breath as biomarkers for the diagnosis of multiple variants of cancer. This review aimed to evaluate the diagnostic accuracy and current status of VOC analysis in exhaled breath for the detection of cancer in the digestive tract.

Methods

PubMed and the Cochrane Library database were searched for VOC analysis studies, in which exhaled air was used to detect gastro-oesophageal, liver, pancreatic, and intestinal cancer in humans, Quality assessment was performed using the QUADAS-2 criteria. Data on diagnostic performance, VOCs with discriminative power, and methodological information were extracted from the included articles.

Results

Twenty-three articles were included (gastro-oesophageal cancer n = 14, liver cancer n = 1, pancreatic cancer n = 2, colorectal cancer n = 6). Methodological issues included different modalities of patient preparation and sampling and platform used. The sensitivity and specificity of VOC analysis ranged from 66.7 to 100 per cent and from 48.1 to 97.9 per cent respectively. Owing to heterogeneity of the studies, no pooling of the results could be performed. Of the VOCs found, 32 were identified in more than one study. Nineteen were reported as cancer type-specific, whereas 13 were found in different cancer types. Overall, decanal, nonanal, and acetone were the most frequently identified.

Conclusion

The literature on VOC analysis has documented a lack of standardization in study designs. Heterogeneity between the studies and insufficient validation of the results make interpretation of the outcomes challenging. To reach clinical applicability, future studies on breath analysis should provide an accurate description of the methodology and validate their findings.

Artificial Intelligence in Medical Sensors for Clinical Decisions

Due to the limited ability of conventional methods and the limited perspective of human diagnostics, patients are often diagnosed incorrectly or at a late stage as their disease condition progresses. They may then undergo unnecessary treatments due to inaccurate diagnoses. In this Perspective, we offer a brief overview on the integration of nanotechnology-based medical sensors and artificial intelligence (AI) for advanced clinical decision support systems to help decision-makers and healthcare systems improve how they approach information, insights, and the surrounding contexts, as well as to promote the uptake of personalized medicine on an individualized basis. Relying on these milestones, wearable sensing devices could enable interactive and evolving clinical decisions that could be used for evidence-based analysis and recommendations as well as for personalized monitoring of disease progress and treatment. We present and discuss the ongoing challenges and future opportunities associated with AI-enabled medical sensors in clinical decisions.

Point-of-care test system for detection of immunoglobulin-G and -M against nucleocapsid protein and spike glycoprotein of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) epidemic continues to ravage the world. In epidemic control, dealing with a large number of samples is a huge challenge. In this study, a point-of-care test (POCT) system was successfully developed and applied for rapid and accurate detection of immunoglobulin-G and -M against nucleocapsid protein (anti-N IgG/IgM) and receptor-binding domain in spike glycoprotein (anti-S-RBD IgG/IgM) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Any one of the IgG/IgM found in a sample was identified as positive. The POCT system contains colloidal gold-based lateral flow immunoassay test strips, homemade portable reader, and certified reference materials, which detected anti-N and anti-S-RBD IgG/IgM objectively in serum within 15 min. Receiver operating characteristic curve analysis was used to determine the optimal cutoff values, sensitivity, and specificity. It exhibited equal to or better performances than four approved commercial kits. Results of the system and chemiluminescence immunoassay kit detecting 108 suspicious samples had high consistency with kappa coefficient at 0.804 (P < 0.001). Besides, the levels and alterations of the IgG/IgM in an inpatient were primarily investigated by the POCT system. Those results suggested the POCT system possess the potential to contribute to rapid and accurate serological diagnosis and epidemiological survey of COVID-19.

Immobilized nanoneedle-like structures for intracellular delivery, biosensing and cellular surgery

The rapid advancements of nanotechnology over the recent years have reformed the methods used for treating human diseases. Nanostructures including nanoneedles, nanorods, nanowires, nanofibers and nanotubes have exhibited their potential roles in drug delivery, biosensing, cancer therapy, regenerative medicine and intracellular surgery. These high aspect ratio structures enhance targeted drug delivery with spatiotemporal control while also demonstrating their role as an efficient intracellular biosensor with minimal invasiveness. This review discusses the history and emergence of these nanostructures and their fabrication methods. This review also provides an overview of the different applications of nanoneedle systems, further highlighting the importance of greater investigation into these nanostructures for future medicine.

Electrically Transduced Gas Sensors Based on Semiconducting Metal Oxide Nanowires

Semiconducting metal oxide-based nanowires (SMO-NWs) for gas sensors have been extensively studied for their extraordinary surface-to-volume ratio, high chemical and thermal stabilities, high sensitivity, and unique electronic, photonic and mechanical properties. In addition to improving the sensor response, vast developments have recently focused on the fundamental sensing mechanism, low power consumption, as well as novel applications. Herein, this review provides a state-of-art overview of electrically transduced gas sensors based on SMO-NWs. We first discuss the advanced synthesis and assembly techniques for high-quality SMO-NWs, the detailed sensor architectures, as well as the important gas-sensing performance. Relationships between the NWs structure and gas sensing performance are established by understanding general sensitization models related to size and shape, crystal defect, doped and loaded additive, and contact parameters. Moreover, major strategies for low-power gas sensors are proposed, including integrating NWs into microhotplates, self-heating operation, and designing room-temperature gas sensors. Emerging application areas of SMO-NWs-based gas sensors in disease diagnosis, environmental engineering, safety and security, flexible and wearable technology have also been studied. In the end, some insights into new challenges and future prospects for commercialization are highlighted.

Dual-Core Fiber-Based Interferometer for Detection of Gas Refractive Index

We demonstrate a dual-core fiber-based Mach–Zehnder interferometer that could be used for precise detection of variations in refractive indices of gaseous samples. The fiber used here have a solid germanium-doped silica core and an air core that allows gases to flow through. Coherent laser beams are coupled to the two cores, respectively, and thus excite guiding modes thereby. Interferogram would be produced as the light transmitted from the dual cores interferes. Variations in refractive index of the hollow core lead to variations in phase difference between the modes in the two cores, thus shifting the interference fringes. The fringe shifts can be then interrogated by a photodiode together with a narrow slit in front. The resolution of the sensor was found to be ~1 × 10−8 RIU, that is comparable to the highest resolution obtained by other fiber sensors reported in previous literatures. Other advantages of our sensor include very low cost, high sensitivity, straightforward sensing mechanism, and ease of fabrication.

Silicon Nanowires for Gas Sensing: A Review

The unique electronic properties of semiconductor nanowires, in particular silicon nanowires (SiNWs), are attractive for the label-free, real-time, and sensitive detection of various gases. Therefore, over the past two decades, extensive efforts have been made to study the gas sensing function of NWs. This review article presents the recent developments related to the applications of SiNWs for gas sensing. The content begins with the two basic synthesis approaches (top-down and bottom-up) whereby the advantages and disadvantages of each approach have been discussed. Afterwards, the basic sensing mechanism of SiNWs for both resistor and field effect transistor designs have been briefly described whereby the sensitivity and selectivity to gases after different functionalization methods have been further presented. In the final words, the challenges and future opportunities of SiNWs for gas sensing have been discussed.

Silicon Nanowires and their Impact on Cancer Detection and Monitoring

Since the inception of silicon nanowires (SINWs)-based biosensors in 2001, SINWs employed in various detection schemes have routinely demonstrated label-free, real-time, sub femtomolar detection of both protein and nucleic acid analytes. This has allowed SiNW-based biosensors to integrate into the field of cancer detection and cancer monitoring and thus have the potential to be a paradigm shift in how cancer biomarkers are detected and monitored. Combining this with several promising fields such as liquid biopsies and targeted oncology, SiNW based biosensors represents an opportunity for cancer monitoring and treatment to be a more dynamic process. Such advances provide clinicians with more information on the molecular landscape of cancer patients which can better inform cancer treatment guidelines.

Will temperature rise change the biochemical alterations induced in Mytilus galloprovincialis by cerium oxide nanoparticles and mercury?

It is known that, for marine coastal ecosystems, pollution and global warming are among the most threatening factors. Among emerging pollutants, nanoparticles (NPs) deserve particular attention as their possible adverse effects are significantly influenced by environmental factors such as salinity, pH and temperature, as well as by their ability to interact with other contaminants. In this framework, the present study aimed to evaluate the potential interactions between CeO₂ NPs and the toxic classic metal mercury (Hg), under current and warming conditions. The marine bivalve Mytilus galloprovincialis was used as biological model and exposed to CeO₂ NPs and Hg, either alone or in combination, for 28 day at 17 °C and 22 °C. A suite of biomarkers related to energetic metabolism, oxidative stress/damage, redox balance, and neurotoxicity was applied in exposed and non-exposed (control) mussels. The Hg and Ce accumulation was also assessed. Results showed that the exposure to CeO₂ NPs alone did not induce toxic effects in M. galloprovincialis. On the contrary, Hg exposure determined a significant loss of energetic metabolism and a general impairment in biochemical performances. Hg accumulation in mussels was not modified by the presence of CeO₂ NPs, while the biochemical alterations induced by Hg alone were partially canceled upon co-exposure with CeO₂ NPs. The temperature increase induced loss of metabolic and biochemical functions and the effects of temperature prevailed on mussels exposed to pollutants acting alone or combined.

How do international gastric cancer prevention guidelines influence clinical practice globally?

Clinical guidelines recommend particular approaches, including 'screen-and-treat' strategy for Helicobacter pylori, to prevent gastric cancer. However, little of this is implemented in clinical practice. The aim of the study was to identify barriers to implementation of international guidelines. A web-based questionnaire distributed globally to specialists in the field. Altogether 886 responses from 75 countries were received. Of the responders, 570 (64%) were men of mean age 47 years. There were 606 gastroenterologists and 65 epidemiologists among the responders. Altogether, 79.8% of the responders disagreed that the burden of gastric cancer is a diminishing problem. 'Screen-and-treat' strategy for H. pylori in the responder's country was considered appropriate by 44.4%, inappropriate by 24.3%, with 31.3% being uncertain. Population-based screening for gastric cancer was considered appropriate in the respective home-country by 62.2%, in other areas - but not the home country - by 27.6%, and inappropriate by 10.2%. As a screening tool, upper endoscopy was acceptable by 35.6%, upper X-ray series by 55.3%, pepsinogens by 26.2% and breath-tests by 23.4%; accuracy, cost-effectiveness and feasibility among the tests varied widely. The attitude towards H. pylori vaccination was that 4.6% of the responders were eager to start vaccination immediately, 55.9% were supporting vaccination but considered that more data are required 12% were negative, and 27.6% did not have an opinion. In general, the attitude of the specialists was in line with guidelines, but was not always translated into clinical practice, particularly in the case of 'screen-and-treat' strategy.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.

Controlled synthesis of ultrathin MoS2 nanoflowers for highly enhanced NO2 sensing at room temperature

Fabrication of a high-performance room-temperature (RT) gas sensor is important for the future integration of sensors into smart, portable and Internet-of-Things (IoT)-based devices. Herein, we developed a NO₂ gas sensor based on ultrathin MoS₂ nanoflowers with high sensitivity at RT. The MoS₂ flower-like nanostructures were synthesised via a simple hydrothermal method with different growth times of 24, 36, 48, and 60 h. The synthesised MoS₂ nanoflowers were subsequently characterised by scanning electron microscopy, X-ray diffraction, Raman spectroscopy, energy-dispersive X-ray spectroscopy and transmission electron microscopy. The petal-like nanosheets in pure MoS₂ agglomerated to form a flower-like structure with Raman vibrational modes at 378 and 403 cm⁻¹ and crystallisation in the hexagonal phase. The specific surface areas of the MoS₂ grown at different times were measured by using the Brunauer–Emmett–Teller method. The largest specific surface area of 56.57 m² g⁻¹ was obtained for the MoS₂ nanoflowers grown for 48 h. This sample also possessed the smallest activation energy of 0.08 eV. The gas-sensing characteristics of sensors based on the synthesised MoS₂ nanostructures were investigated using oxidising and reducing gases, such as NO₂, SO₂, H₂, CH₄, CO and NH₃, at different concentrations and at working temperatures ranging from RT to 150 °C. The sensor based on the MoS₂ nanoflowers grown for 48 h showed a high gas response of 67.4% and high selectivity to 10 ppm NO₂ at RT. This finding can be ascribed to the synergistic effects of largest specific surface area, smallest crystallite size and lowest activation energy of the MoS₂-48 h sample among the samples. The sensors also exhibited a relative humidity-independent sensing characteristic at RT and a low detection limit of 84 ppb, thereby allowing their practical application to portable IoT-based devices.

Controlled synthesis of ultrathin MoS₂ nanoflowers is crucial to develop a high-performance room-temperature NO₂ gas sensor for the future integration of sensors into smart, portable and Internet-of-Things (IoT)-based devices.

A Silicon Nanowire Array Biosensor Fabricated by Complementary Metal Oxide Semiconductor Technique for Highly Sensitive and Selective Detection of Serum Carcinoembryonic Antigen

In this paper, we present a highly sensitive and selective detection of serum carcinoembryonic antigen (CEA) based on silicon nanowire (SiNW) array device. With the help of traditional microfabrication technology, low-cost and highly controllable SiNW array devices were fabricated. After a series of surface modification processes, SiNW array biosensors show rapid and reliable response to CEA; the detection limit of serum CEA was 10 fg/mL, the current signal is linear with the logarithm of serum CEA concentration in the range of 10 fg/mL to 100 pg/mL. In this work, SiNW array biosensors can obtain strong signal and high signal-to-noise ratio; these advantages can reduce the production cost of the SiNW-based system and promote the application of SiNWs in the field of tumor marker detection.