Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing

Volatile organic compounds (VOCs) detection for the identification of bacterial infections in clinical wound samples

Early detection of wound infections is critical for timely intervention and prevention of possible complications since prompt treatment can help lower pathogen spread and enhance faster healing. Early detection also helps reduce the risk of serious infections requiring extensive medical interventions or life-threatening diseases such as sepsis. Culture-based approaches currently used for bacterial identification have limited sensitivity and specificity. At the same time, they are time-consuming, resulting in delays in therapy and, therefore, having a negative impact on the treatment outcomes. Quantifying the volatile organic compounds (VOCs) released by bacteria residing in wounds is a promising, non-invasive option for detecting infections at early stages. This method allows for continuous monitoring without requiring invasive procedures, thereby reducing patient discomfort and the risk of further complications. Spectroscopy methods and sensors are the primary VOC detection and quantification approaches, but sensors are more rapid, cost-effective, non-invasive, and precise. This review highlights the significance of the early detection of wound infection to enable timely intervention and prevent complications, emphasizing the limitations of culture-based approaches. It also explores the potential of quantifying VOCs using different methods and discusses the correlation between their levels and the rate of bacterial infections in wounds. Additionally, the review evaluates current VOC-based monitoring methods for wound management, identifies gaps in the field, and advocates for further research to advance wound care and enhance patient outcomes.

Olfactory biosensor for smart agriculture

The high-quality development of agriculture is related to the survival of human beings. Olfactory biosensors show great potential for application in agriculture with their significant advantages in sensitivity, selectivity, and stability. This paper reviews the development history of olfactory biosensors, introduces the characteristics of their sensitive layer, analyzes the signal conversion mechanism, describes the preparation techniques, and discusses the application of olfactory biosensors in agriculture. The current application challenges, future trends, and economics of olfactory biosensors are also analyzed. Hopefully, this paper will provide a new perspective for the research of olfactory biosensors in agriculture, promote the further development of related technologies, and support the realization of smart agriculture and green transformation of agriculture.

Spatiotemporal E-Nose with Laser Tailoring and Chromatography Inspiration

The study introduces a spatiotemporal chromatography-mimicking (SCM) e-nose that integrates laser-tailored graphene paper with a microchamber for precise volatile organic compound (VOC) discrimination. The SCM e-nose overcomes traditional array limitations with a single multifunctional component capable of accurate VOC differentiation via chromatography-mimic features. Advanced laser-engraving techniques fabricate a gas-permeable interdigitated electrode from graphene paper as the sieving framework. Key achievements include its single multifunctional component, economical and scalable design, distinct response patterns for different VOCs, remarkable ability to discriminate mixed VOCs, versatility for diverse applications including real-time on-site analysis, and ease of integration with electronic systems. The SCM e-nose represents a significant advancement in electronic nose technology, offering a compact, cost-effective solution for precise VOC detection and analysis.

Selectivity in Chemiresistive Gas Sensors: Strategies and Challenges

The demand for highly functional chemical gas sensors has surged due to the increasing awareness of human health to monitor metabolic disorders or noncommunicable diseases, safety measures against harmful greenhouse and/or explosive gases, and determination of food freshness. Over the years of dedicated research, several types of chemiresistive gas sensors have been realized with appreciable sensitivities toward various gases. However, critical issues such as poor selectivity and sluggish response/recovery speeds continue to impede their widespread commercialization. Specifically, the mechanisms behind the selective response of some chemiresistive materials toward specific gas analytes remain unclear. In this review, we discuss state-of-the-art strategies employed to attain gas-selective chemiresistive materials, with particular emphasis on materials design, surface modification or functionalization with catalysts, defect engineering, material structure control, and integration with physical/chemical gas filtration media. The nature of material surface-gas interactions and the supporting mechanisms are elucidated, opening opportunities for optimizing the materials design, fine-tuning the gas sensing performance, and guiding the selection of the most appropriate materials for the accurate detection of specific gases. This review concludes with recommendations for future research directions and potential opportunities for further selectivity improvements.

Recoverable Detection of Dichloromethane by MEMS Gas Sensor Based on Mo and Ni Co-Doped SnO2 Nanostructure

The challenging problem of chlorine "poisoning" SnO2 for poorly recoverable detection of dichloromethane has been solved in this work. The materials synthesized by Ni or/and Mo doping SnO2 were spread onto the micro-hotplates (<1 mm3) to fabricate the MEMS sensors with a low power consumption (<45 mW). The sensor based on Mo·Ni co-doped SnO2 is evidenced to have the best sensing performance of significant response and recoverability to dichloromethane between 0.07 and 100 ppm at the optimized temperature of 310 °C, in comparison with other sensors in this work and the literature. It can be attributed to a synergetic effect of Mo·Ni co-doping into SnO2 as being supported by characterization of geometrical and electronic structures. The sensing mechanism of dichloromethane on the material is investigated. In situ infrared spectroscopy (IR) peaks identify that the corresponding adsorbed species are too strong to desorb, although it has demonstrated a good recoverability of the material. A probable reason is the formation rates of the strongly adsorbed species are much slower than those of the weakly adsorbed species, which are difficult to form significant IR peaks but easy to desorb, thus enabling the material to recover. Theoretical analysis suggests that the response process is kinetically determined by molecular transport onto the surface due to the free convection from the concentration gradient during the redox reaction, and the output steady voltage thermodynamically follows the equation only formally identical to the Langmuir-Freundlich equation for physisorption but is newly derived from statistical mechanics.

Hierarchical Porous Aggregate-Enabled Chromatography-Inspired Single-Sensor E-Nose for Volatile Monitoring

Monitoring volatile organic compounds (VOCs) is crucial for ensuring safety and health. In this study, we introduce a strategy to engineer a chromatography-inspired single-sensor (CISS) e-nose tailored for VOC monitoring. This approach overcomes the limitations of traditional methodologies and conventional e-noses. A hierarchical porous multicomponent aggregate, named CuP@G, was initially developed as the sole sensor material. This aggregate integrates a Cu2+-polydopamine (CuP) network with reduced graphene oxide, enhancing its chemoresistive properties. Using laser processing, we fabricated a grooved laser-induced graphene interdigitated electrode that is loaded with CuP@G ink and subsequently integrated into a compact laser-engraved microchamber. This process results in the production of the CISS e-nose. Notably, this e-nose enables swift, reversible, and precise detection of various VOCs using a time-space-resolved methodology. The developed module, known for its affordability and portability, is especially suitable for the point-of-care testing (POCT) of VOCs. Consequently, our research advances the development of streamlined cost-effective e-noses that are essential for proficient VOC monitoring.

Light-activated semiconductor gas sensors: pathways to improve sensitivity and reduce energy consumption

Resistive type gas sensors based on wide-bandgap semiconductor oxides are remaining one of the principal players in environmental air monitoring. The rapid development of technology and the desire to miniaturize electronics require the creation of devices with minimal energy consumption. A promising solution may be the use of photoactivation, which can initiate/accelerate physico-chemical processes at the solid-gas interface and realize detection of flammable and explosive gases at close to room temperature. This work examines the mechanism underlying the increased sensitivity to various gases under photoactivation. The review is intended to clarify the current situation in the field of light-activated gas sensors and set the vector for their further development in order to integrate with the latest technological projects.

TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Infection Diagnostics Enabled by Selective Adsorption of Breath-Based Biomarkers in Zr-Based Metal-Organic Frameworks

Exhaled breath contains trace levels of volatile organic compounds (VOCs) that can reveal information about metabolic processes or pathogens in the body. These molecules can be used for medical diagnosis, but capturing and accurately measuring them is a significant challenge in chemical separations. A highly selective nanoporous sorbent can be used to capture target molecules from a breath sample and preconcentrate them for use in a detector. In this work, we present a combined predictive modeling-experimental validation study in which five Zr-based metal-organic frameworks (MOFs) were identified and tested. These MOFs display good selectivity for a variety of VOCs known to be indicators of viral infections such as influenza and COVID-19. We first used molecular simulation to identify promising MOF candidates that were subsequently synthesized and tested for recovery of a variety of VOCs (toluene, propanal, butanone, octane, acetaldehyde) at concentrations of 20 ppm in humid nitrogen. We show that MOF-818, PCN-777, and UiO-66 have particularly good selectivity for the target molecules in the presence of humidity. These three MOFs each recover around 40-60% of the targets (with the exception of acetaldehyde) at up to 95% relative humidity. MOF-818 recovers 63% of butanone and 60% of toluene at 80% relative humidity. Recovery for acetaldehyde is lower across all MOFs at high humidity, but notably, MOF-808 recovers 90% of acetaldehyde at 60% humidity.

Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVM algorithm: A novel system for Daqu Grade identification

Electronic nose is a bionic technology that uses sensor arrays and pattern recognition algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognition algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples ("Excellent+", "Excellent", "Grade I", and "Grade II") determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM) algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

Characteristic volatile compounds of white tea with different storage times using E-nose, HS-GC-IMS, and HS-SPME-GC-MS

This paper studied the influence of storage duration on the flavor profile of white tea in detail, with samples produced between 2020 and 2023. Sensory evaluation was performed by quantitative descriptive analysis (QDA), followed by an in-depth aroma components analysis employing an electronic nose (E-nose), headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The QDA findings revealed a gradual transition in the flavor profile of white tea during storage, shifting from sweet, fruity, and floral to more herbal and stale characteristics. E-nose could well distinguish white tea with different storage times. A total of 55 and 53 volatile compounds were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. The orthogonal partial least squares-discriminant analysis models, based on HS-GC-IMS (R2Y = 0.998, Q2 = 0.987) and HS-SPME-GC-MS (R2Y = 0.984, Q2 = 0.993), successfully distinguished white tea samples stored for different storage times. Furthermore, 14 and 8 key compounds were screened based on the double variable criterion of one-way analysis of variance (p < 0.05) and variable importance in projection (VIP) >1.2, and their content changes were also compared. It is the gradual decrease of important aroma components such as 2-hexenal, 2-methyl-2-hepten-6-one, linalool, and geraniol, which are positively correlated with sweet, fruity, and floral aromas, and the gradual increase of hexanoic acid, thiophene, propanoic acid, dimethyl disulfide, and borneyl acetate, which are positively correlated with herbal and stale flavor, that leads to the changes in flavor and aroma of white tea during storage. The results of the study provided a reference for elucidating the aroma characteristics of white tea at different storage times as well as a theoretical basis for the quality control of white tea.

Progress and Challenges of Monometallic Titanium Coordination Polymers

The realm of titanium coordination polymer research is still in its nascent stages and presents a formidable challenge in the field of coordination chemistry. In recent decades, the focus has predominantly been on manipulating titanium reactions in solution, resulting in the synthesis of ≈60 targeted compounds. Despite the limited number of documented instances, these materials showcase a diverse array of structures, encompassing 1D chains, 2D layers, and 3D frameworks. This suggests potential for fine-tuning coordination modes and structural features in future investigations. Moreover, titanium coordination polymers not only exhibit photo-active and photo-responsive properties but also hold promise for various other significant applications. This article offers an exhaustive review tracing the evolution of titanium coordination polymer development while providing an update on recent advancements. The review encompasses a synopsis of reported synthetic strategies, methodologies, structural diversity, and associated applications. Additionally, it delves into critical issues that necessitate attention for future progressions and proposes potential avenues to effectively propel this research field forward at an accelerated pace.

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

INTRODUCTION

Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer.

METHODS

This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models.

RESULTS

Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001).

CONCLUSIONS

In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

The difference analysis of physicochemical indexes and volatile flavor compounds of chili oil prepared from different varieties of chili pepper

Because of its peculiar flavor, chili oil is widely used in all kinds of food and is welcomed by people. Chili pepper is an important raw material affecting its quality, and commercial chili oil needs to meet various production needs, so it needs to be made with different chili peppers. However, the current compounding method mainly relies on the experience of professionals and lacks the basis of objective numerical analysis. In this study, the chroma and capsaicinoids of different chili oils were analyzed, and then the volatile components were determined by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-ion migration spectrometer (GC-IMS) and electronic nose (E-nose). The results showed that Zidantou chili oil had the highest L*, b*, and color intensity (ΔE) (52.76 ± 0.52, 88.72 ± 0.89, and 118.84 ± 1.14), but the color was tended to be greenyellow. Xinyidai chili oil had the highest a* (65.04 ± 0.2). But its b* and L* were relatively low (76.17 ± 0.29 and 45.41 ± 0.16), and the oil was dark red. For capsaicinoids, Xiaomila chili oil had the highest content of capsaicinoids was 2.68 ± 0.07 g/kg, Tianjiao chili oil had the lowest content of capsaicinoids was 0.0044 ± 0.0044 g/kg. Besides, 96 and 54 volatile flavor substances were identified by GC-MS and GC-IMS respectively. And the main volatile flavor substances of chili oil were aldehydes, alcohols, ketones, and esters. A total of 11 key flavor compounds were screened by the relative odor activity value (ROAV). Moguijiao chili oil and Zidantou chili oil had a prominent grass aroma because of hexanal, while Shizhuhong chili oil, Denglongjiao chili oil, Erjingtiao chili oil, and Zhoujiao chili oil had a prominent floral aroma because of 2, 3-butanediol. Chili oils could be well divided into 3 groups by the partial least squares discriminant analysis (PLS-DA). According to the above results, the 10 kinds of chili oil had their own characteristics in color, capsaicinoids and flavor. Based on quantitative physicochemical indicators and flavor substances, the theoretical basis for the compounding of chili oil could be provided to meet the production demand more scientifically and accurately.

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

Machine Learning Driven Atom-Thin Materials for Fragrance Sensing

Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

A Strategy for Multigas Identification Using Multielectrical Parameters Extracted from a Single Carbon-Based Field-Effect Transistor Sensor

Given the widespread utilization of gas sensors across various industries, the detection of diverse and complex target gases presents a significant challenge in designing sensors with multigas detection capability. Although constructing a sensor array with widely used chemiresistive gas sensors is one solution, it is difficult for a single chemiresistive gas sensor to simultaneously detect different gases, as it can only detect a single target gas. The intrinsic reason for this bottleneck is that chemiresistive gas sensors rely entirely on the resistivity as the unique parameter to evaluate the diverse gas sensing properties of sensors, such as sensitivity, selectivity, etc. Herein, a field-effect transistor (FET) with abundant electrical parameters is employed to prepare a gas sensor for the detection of a variety of gases. Semiconducting carbon nanotubes (CNTs) are selected as the channel material, which is modified by Pd nanoparticles to enhance the gas sensing properties of the sensors. By extracting various electrical parameters such as transconductance, threshold voltage, etc. from the transfer characteristic curves of FET, a correlation between multielectrical parameters and various gas detection information is established for subsequent data analysis. Through the utilization of the principal component analysis algorithm, the identification of six gases can be finally achieved by relying solely on a single carbon-based FET-type gas sensor. We hope our work can solve the bottleneck of multigas identification by a single sensor in principle and is expected to reduce the system complexity and cost caused by the design of sensor arrays, offering a valuable guidance for multigas identification technology.

Optimized Detection of Volatile Organic Compounds Utilizing Durable and Selective Arrays of Tailored UiO-66-X SURMOF Sensors

Metal-organic frameworks (MOFs), with their well-defined and highly flexible nanoporous architectures, provide a material platform ideal for fabricating sensors. We demonstrate that the efficacy and specificity of detecting and differentiating volatile organic compounds (VOCs) can be significantly enhanced using a range of slightly varied MOFs. These variations are obtained via postsynthetic modification (PSM) of a primary framework. We alter the original MOF's guest adsorption affinities by incorporating functional groups into the MOF linkers, which yields subtle changes in responses. These responses are subsequently evaluated by using machine learning (ML) techniques. Under severe conditions, such as high humidity and acidic environments, sensor stability and lifespan are of utmost importance. The UiO-66-X MOFs demonstrate the necessary durability in acidic, neutral, and basic environments with pH values ranging from 2 to 11, thus surpassing most other similar materials. The UiO-66-NH2 thin films were deposited on quartz-crystal microbalance (QCM) sensors in a high-temperature QCM liquid cell using a layer-by-layer pump method. Three different, highly stable surface-anchored MOFs (SURMOFs) of UiO-66-X obtained via the PSM approach (X: NH2, Cl, and N3) were employed to fabricate arrays suitable for electronic nose applications. These fabricated sensors were tested for their capability to distinguish between eight VOCs. Data from the sensor array were processed using three distinct ML techniques: linear discriminant (LDA), nearest neighbor (k-NN), and neural network analysis methods. The discrimination accuracies achieved were nearly 100% at high concentrations and over 95% at lower concentrations (50-100 ppm).

Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability

As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature-rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmental surveillance.

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis

Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.

Nanotechnology in precision agriculture: Advancing towards sustainable crop production

Nanotechnology offers many potential solutions for sustainable agroecosystem, including improvement in nutrient use efficiency, efficacy of pest management, and minimizing the adverse environmental effects of agricultural production. Herein, we first highlighted the integrated application of nanotechnology and precision agriculture for sustainable productivity. Application of nanoparticle mediated material and advanced biosensors in precision agriculture is only possible by nanochips or nanosensors. Nanosensors offers the measurement of various stresses, soil quality parameters and detection of heavy metals along with the enhanced data collection, enabling precise decision-making and resource management in agricultural systems. Nanoencapsulation of conventional chemical fertilizers (known as nanofertilizers), and pesticides (known as nanopesticides) helps in sustained and slow release of chemicals to soils and results in precise dosage to plants. Further, nano-based disease detection kits are popular tools for early and speedy detection of viral diseases. Many other innovative approaches including biosynthesized nanoparticles have been evaluated and proposed at various scales, but in fact there are some barriers for practical application of nanotechnology in soil-plant system, including safety and regulatory concerns, efficient delivery at field levels, and consumer acceptance. Finally, we outlined the policy options and actions required for sustainable agricultural productivity, and proposed various research pathways that may help to overcome the upcoming challenges regarding practical implications of nanotechnology.

Characterization of aroma profiles of chinese four most famous traditional red-cooked chickens using GC-MS, GC-IMS, and E-nose

The aroma profile of the four most popular types of red-cooked chickens in China was analyzed using a combination of gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS), and electronic nose (E-nose). Principal component analysis (PCA) demonstrated that the E-nose could successfully distinguish between the four types of red-cooked chickens. Additionally, a fingerprint was created using GC-IMS to examine the variations in volatile organic compounds (VOCs) distribution in the four chicken types. A total number of 84 and 62 VOCs were identified in the four types of red-cooked chickens using GC-MS and GC-IMS, respectively. Odor activity value (OAV) showed that 1-octen-3-ol, heptanal, hexanal, nonanal, octanal, eugenol, dimethyl trisulfide, anethole, anisaldehyde, estragole, and eucalyptol were the key volatile components in all samples. Furthermore, partial least squares-discriminant analysis (PLS-DA) demonstrated that (E, E)-2,4-decadienal, dimethyl trisulfide, octanal, eugenol, hexanal, (E)-2-nonenal, 1-octen-3-ol, butanal, ethyl acetate, ethyl acetate (D), nonanal, and heptanal could be used as markers to distinguish aroma of the four types of red-cooked chickens. Also, it is worth noting that levels of VOCs varied between chicken breast muscle and skin. The obtained results offer theoretical and technological support for flavor identification and control in red-cooked chickens to enhance their quality and encourage consumer consumption, which will be advantageous for the red-cooked chicken production chain.

Recent advances in whiskey analysis for authentication, discrimination, and quality control

In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications

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.

Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness

The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.

Colorimetric Multigas Sensor Arrays and an Artificial Olfactory Platform for Volatile Organic Compounds

Herein, we develop colorimetric multigas sensor arrays assembling chemo-reactive fluorescent patch arrays and 10 × 10 indium gallium zinc oxide phototransistor arrays and apply them to an artificial olfactory platform to recognize five different volatile organic compounds (VOCs). Porous nanofibers, coupled with two organic emitters and emitting fluorescence, rapidly respond to gas-phased VOCs and offer unique fluorescent patterns associated with particular gas conditions, including gas kinds, concentrations, and exposure times by forming patch arrays with different fluorophore component ratios. These VOC-induced fluorescent patterns could be quantified and amplified by indium gallium zinc oxide (IGZO) phototransistor arrays functioning as a signal-generating component, resulting in gas-fingerprint patterns regarding electrical signals. Thus, the pattern library associated with VOCs and their concentration enables us to determine each airborne analyte as the artificial olfactory platform. Therefore, this system could achieve rapid, early quantitative recognition of hazardous gases and be applied as a preventative, portable, and wearable multigas identifier in various fields.

Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella

Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.

Influence of ventilatory parameters on the concentration of exhaled volatile organic compounds in mechanically ventilated patients

Analysis of volatile organic compounds (VOC) within exhaled breath is subject to numerous sources of methodological and physiological variability. Whilst breathing pattern is expected to influence the concentrations of selected exhaled VOCs, it remains challenging to investigate respiratory rate and depth accurately in awake subjects. Online breath sampling was performed in 20 mechanically ventilated patients using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). The effect of variation in respiratory rate (RR) and tidal volume (TV) on the VOC release profiles was examined. A panel of nineteen VOCs were selected, including isoprene, acetone, propofol, volatile aldehydes, acids and phenols. Variation in RR had the greatest influence on exhaled isoprene levels, with maximum and average concentrations being inversely correlated with RR. Variations in RR had a statistically significant impact on acetone, C3-C7 linear aldehydes and acetic acid. In comparison, phenols (including propofol), C8-C10 aldehydes and C3-C6 carboxylic acids were not influenced by RR. Isoprene was the only compound to be influenced by variation in TV. These findings, obtained under controlled conditions, provide useful guidelines for the optimisation of breath sampling protocols to be applied on awake patients.

Ultrasensitive Gas Detection Based on Electrically Enhanced Nanoplasmonic Sensor with Graphene-Encased Gold Nanorod

Nanoplasmonic sensors are a widely known concept and have been studied with various applications. Among them, gas detection is engaging attention in many fields. However, the analysis performance of nanoplasmonic sensors based on refractive index confined to the metal nanostructure characteristics causes challenges in gas detection. In this study, we develop a graphene-encased gold nanorod (AuNR)-based nanoplasmonic sensor to detect cadaverine gas. The graphene-encased AuNR (Gr@AuNR) presents an ultrasensitive peak wavelength shift even with tiny molecules. In addition, the external potential transmitted through graphene induces an additional shift. A chemical receptor is immobilized on Gr@AuNR (CR@Gr@AuNR) for selectively capturing cadaverine. The CR@Gr@AuNR achieves ultrasensitive detection of cadaverine gas, and the detection limit is increased to 15.99 ppb by applying a voltage to graphene. Furthermore, the experimental results of measuring cadaverine generated from spoiled pork show the practicality of CR@Gr@AuNR. The strategy of external-boosted nanoplasmonics provides new insight into plasmonic sensing and applications.

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors: Overview

Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring, exhaled breath diagnosis, and food freshness analysis. Among various chemiresistive sensing materials, noble metal-decorated semiconducting metal oxides (SMOs) have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble metals. This review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures (e.g., nanoparticles, nanowires, nanorods, nanosheets, nanoflowers, and microspheres) for high-performance gas sensors with higher response, faster response/recovery speed, lower operating temperature, and ultra-low detection limits. The key topics include Pt, Pd, Au, other noble metals (e.g., Ag, Ru, and Rh.), and bimetals-decorated SMOs containing ZnO, SnO2, WO3, other SMOs (e.g., In2O3, Fe2O3, and CuO), and heterostructured SMOs. In addition to conventional devices, the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also discussed. Moreover, the relevant mechanisms for the sensing performance improvement caused by noble metal decoration, including the electronic sensitization effect and the chemical sensitization effect, have also been summarized in detail. Finally, major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.

Metal-Organic Framework (MOF) Derivatives as Promising Chemiresistive Gas Sensing Materials: A Review

The emission of harmful gases has seriously exceeded relative standards with the rapid development of modern industry, which has shown various negative impacts on human health and the natural environment. Recently, metal-organic frameworks (MOFs)-based materials have been widely used as chemiresistive gas sensing materials for the sensitive detection and monitoring of harmful gases such as NO_x, H₂S, and many volatile organic compounds (VOCs). In particular, the derivatives of MOFs, which are usually semiconducting metal oxides and oxide-carbon composites, hold great potential to prompt the surface reactions with analytes and thus output amplified resistance changing signals of the chemiresistors, due to their high specific surface areas, versatile structural tunability, diversified surface architectures, as well as their superior selectivity. In this review, we introduce the recent progress in applying sophisticated MOFs-derived materials for chemiresistive gas sensors, with specific emphasis placed on the synthesis and structural regulation of the MOF derivatives, and the promoted surface reaction mechanisms between MOF derivatives and gas analytes. Furthermore, the practical application of MOF derivatives for chemiresistive sensing of NO₂, H₂S, and typical VOCs (e.g., acetone and ethanol) has been discussed in detail.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

Several ML Algorithms and Their Feature Vector Design for Gas Discrimination and Concentration Measurement with an Ultrasonically Catalyzed MOX Sensor

Although gas-borne ultrasound catalysis has been developed as a new method to discriminate gas species and measure the concentration, applications of machine learning methods in gas analyses with a single metal oxide (MOX) gas sensor catalyzed by gas-borne ultrasound are still scarce. In this work, with an ultrasonically catalyzed MOX gas sensor, we explored the effectiveness of K-nearest neighbors (KNN), support vector machine (SVM), and single-hidden-layer BP-ANN (SHBP) in gas discrimination and the application of the SHBP in concentration measurement. The target gases in this work are ethanol, acetone, methanol, hydrogen, and n-butane in clean air, respectively, and the discrimination and concentration regression are implemented by two different ML models. With the properly designed feature vectors, the SHBP method has an acceptable capability of both of species discrimination and concentration regression (success rate of gas discrimination = 99.5%, relative error of concentration regression = 6.406%). The KNN and SVM methods have similar capabilities of gas discrimination as the SHBP. This work also demonstrates a method to design the feature vectors for the ultrasonically catalyzed MOX gas sensor and to choose the feature parameters.

Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm

Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.

Smart Wearable Systems for Health Monitoring

Smart wearable systems for health monitoring are highly desired in personal wisdom medicine and telemedicine. These systems make the detecting, monitoring, and recording of biosignals portable, long-term, and comfortable. The development and optimization of wearable health-monitoring systems have focused on advanced materials and system integration, and the number of high-performance wearable systems has been gradually increasing in recent years. However, there are still many challenges in these fields, such as balancing the trade-off between flexibility/stretchability, sensing performance, and the robustness of systems. For this reason, more evolution is required to promote the development of wearable health-monitoring systems. In this regard, this review summarizes some representative achievements and recent progress of wearable systems for health monitoring. Meanwhile, a strategy overview is presented about selecting materials, integrating systems, and monitoring biosignals. The next generation of wearable systems for accurate, portable, continuous, and long-term health monitoring will offer more opportunities for disease diagnosis and treatment.

Food Freshness Measurements and Product Distinguishing by a Portable Electronic Nose Based on Organic Field-Effect Transistors

Determination of food freshness, which is the most ancient role of the human sense of smell, is still a challenge for compact and inexpensive electronic nose devices. Fast, sensitive, and reusable sensors are long-awaited in the food industry to replace slow, labor-intensive, and expensive bacteriological methods. In this work, we present microbiological verification of a novel approach to food quality monitoring and spoilage detection using an electronic nose based on organic field-effect transistors (OFETs) and its application for distinguishing products. The compact device presented is able to detect spoilage-related gases as early as at the 4 × 10⁴ CFU g^-1 bacteria count level, which is 2 orders of magnitude below the safe consumption threshold. Cross-selective sensor array based on OFETs with metalloporphyrin receptors were made on a single substrate using solution processing leading to a low production cost. Moreover, machine learning methods applied to the sensor array response allowed us to compare spoilage profiles and separate them by the type of food: pork, chicken, fish, or milk. The approach presented can be used to monitor food spoilage and distinguish different products with an affordable and portable device.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning

As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In₂O₃ film coated with plasmonic metal nanoparticles (NPs) directly on the μLED. The e-nose system consists of two different μLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-μLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO₂, acetone, methanol) was developed. The μLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.

Recent Progress in Thin-Film Transistors toward Digital, Analog, and Functional Circuits

Thin-film transistors have been extensively developed due to their process merit: high compatibility with various substrates, large-area processes, and low-cost processes. Despite these advantages, most efforts for thin-film transistors still remain at the level of unit devices, so the circuit level for practical use needs to be further developed. In this regard, this review revisits digital and analog thin-film circuits using carbon nanotubes (CNTs), organic electrochemical transistors (OECTs), organic semiconductors, metal oxides, and two-dimensional materials. This review also discusses how to integrate thin-film circuits at the unit device level and some key issues such as metal routing and interconnection. Challenges and opportunities are also discussed to pave the way for developing thin-film circuits and their practical applications.

Non-Specific Responsive Nanogels and Plasmonics to Design MathMaterial Sensing Interfaces: The Case of a Solvent Sensor

The combination of non-specific deformable nanogels and plasmonic optical probes provides an innovative solution for specific sensing using a generalistic recognition layer. Soft polyacrylamide nanogels that lack specific selectivity but are characterized by responsive behavior, i.e., shrinking and swelling dependent on the surrounding environment, were grafted to a gold plasmonic D-shaped plastic optical fiber (POF) probe. The nanogel-POF cyclically challenged with water or alcoholic solutions optically reported the reversible solvent-to-phase transitions of the nanomaterial, embodying a primary optical switch. Additionally, the non-specific nanogel-POF interface exhibited more degrees of freedom through which specific sensing was enabled. The real-time monitoring of the refractive index variations due to the time-related volume-to-phase transition effects of the nanogels enabled us to determine the environment's characteristics and broadly classify solvents. Hence the nanogel-POF interface was a descriptor of mathematical functions for substance identification and classification processes. These results epitomize the concept of responsive non-specific nanomaterials to perform a multiparametric description of the environment, offering a specific set of features for the processing stage and particularly suitable for machine and deep learning. Thus, soft MathMaterial interfaces provide the ground to devise devices suitable for the next generation of smart intelligent sensing processes.

Environmental gas sensors based on electroactive hybrid organic-inorganic nanocomposites using nanostructured materials

Advanced gas sensing devices are urgently demanded in the modern scientific world to control air pollution and protect human life. For this purpose, semiconducting electroactive materials can revolutionize the idea of conventional gas sensors. Chemi-resistive gas sensors based on electroactive hybrid organic-inorganic nanocomposites are incredibly promising gas sensing materials because they possess the advantages of excellent selectivity, high sensitivity, low response time, repeatability, high stability, cost-effectiveness, and simple fabrication techniques, and they can be operated at room temperature. This review emphasizes the recent developments of organic-inorganic hybrid nanocomposite-based electroactive gas sensors, including metal oxide nanocomposites, which are potential gas sensing materials due to the presence of numerous charge carriers. The review also focuses on nanostructured materials of different dimensions, such as semiconducting quantum dots, carbon dots, nanotubes, nanowires, and nanosheets, used for developing these gas sensing compounds and their significance and challenges. Some possible fabrication techniques for developing efficient gas sensors with different morphologies are discussed, with their probable sensing mechanism behind the detection of toxic vapours. Subsequently, a summary and possible outcome of this study, along with the various achievements and prospects in this field, are also discussed.

Efficient SERS Response of Porous-ZnO-Covered Gold Nanoarray Chips to Trace Benzene-Volatile Organic Compounds

Fast and sensitive detection of gaseous volatile organic compounds (VOCs), based on surface-enhanced Raman spectroscopy (SERS), is still a challenge due to their weak interaction with plasmonic metals and overly small Raman scattering cross sections. Herein, we propose a simple strategy to achieve the SERS-based highly efficient detection of trace benzene-VOCs (B-VOCs) based on a composite chip. The composite chip is designed and fabricated via covering the porous zinc oxide on gold nanoarrays by a one-step solution growth method. Such composite chip shows highly selective capture of gaseous B-VOCs (benzene, toluene, nitrobenzene, xylene, and chlorobenzene, etc.), which leads to the rapid and sensitive SERS responses to them. Typically, this chip can response to gaseous toluene within 30 s, and the lowest detectable concentration is below 10 ppb. Further experiments have revealed that there exists an optimal thickness of the ZnO covering layer for the highly efficient SERS response to the B-VOCs, which is about 150 nm. Also, such a composite chip is recoverable in SERS response and hence reusable. The highly efficient SERS response of the composite chip to the B-VOCs is attributed to the porous structure-enhanced molecular adsorption and the electromagnetic-chemical dual-enhancement mechanism. This work not only presents a practical SERS chip for the efficient detection of the typical B-VOCs but also provides a deep understand the interaction between the B-VOCs and the ZnO as well as the chemical enhancement mechanism.

Recent Progress on Flexible Room-Temperature Gas Sensors Based on Metal Oxide Semiconductor

With the rapid development of the Internet of Things, there is a great demand for portable gas sensors. Metal oxide semiconductors (MOS) are one of the most traditional and well-studied gas sensing materials and have been widely used to prepare various commercial gas sensors. However, it is limited by high operating temperature. The current research works are directed towards fabricating high-performance flexible room-temperature (FRT) gas sensors, which are effective in simplifying the structure of MOS-based sensors, reducing power consumption, and expanding the application of portable devices. This article presents the recent research progress of MOS-based FRT gas sensors in terms of sensing mechanism, performance, flexibility characteristics, and applications. This review comprehensively summarizes and discusses five types of MOS-based FRT gas sensors, including pristine MOS, noble metal nanoparticles modified MOS, organic polymers modified MOS, carbon-based materials (carbon nanotubes and graphene derivatives) modified MOS, and two-dimensional transition metal dichalcogenides materials modified MOS. The effect of light-illuminated to improve gas sensing performance is further discussed. Furthermore, the applications and future perspectives of FRT gas sensors are also discussed.

The Application of In Situ Methods to Monitor VOC Concentrations in Urban Areas—A Bibliometric Analysis and Measuring Solution Review

Urbanisation development affects urban vegetation both directly and indirectly. Since this process usually involves a dramatic change in land use, it is seen as likely to cause ecological pressure on local ecosystems. All forms of human activity, including urbanisation of areas close to residential buildings, significantly impact air quality. This study aims to identify and characterise different measurement solutions of VOCs, allowing the quantification of total and selective compounds in a direct at source (in situ) manner. Portable devices for direct testing can generally be divided into detectors, chromatographs, and electronic noses. They differ in parameters such as operating principle, sensitivity, measurement range, response time, and selectivity. Direct research allows us to obtain measurement results in a short time, which is essential from the point of view of immediate reaction in the case of high concentrations of tested compounds and the possibility of ensuring the well-being of people. The paper also attempts to compare solutions and devices available on the market and assess their application.

Effect of the Dispersion Process and Nanoparticle Quality on Chemical Sensing Performance

On the surface of chemiresistive films, the scarce heterogeneity of a molecularly capped gold nanoparticle (MCGNP) colloidal dispersion and uneven evaporation of the MCGNP-contained drying drop applied to this surface are among the main factors that affect reproducibility, and repeatable fabrication of thin films of MCGNPs. This article shows that an increase in reproducibility and repeatability is possible using a dispersant and a surfactant during the deposition and annealing processes of the MCGNP. The results show higher sensitivity and accuracy of the sensors for the detection of volatile organic compounds in air and an increased limit of detection. These simple and practical additions might serve as a launching pad for fabrication of other types of thin-film-based sensors.

Analysis of the Response Characteristics of Toluene Gas Sensors with a ZnO Nanorod Structure by a Heat Treatment Process

The sensing characteristics of toluene gas are monitored by fabricating ZnO nanorod structures. ZnO nanostructured sensor materials are produced on a Zn film via an ultrasonic process in a 0.01 M aqueous solution of C₆H₁₂N₄ and Zn(NO₃)₂∙6H₂O. The response of the sensors subjected to heat treatment in oxygen and nitrogen atmospheres is improved by 20% and 10%, respectively. The improvement is considered to be correlated with the increase in grain size. The relationship between the heat treatment and sensing characteristics is evaluated.