Trace-Level, Multi-Gas Detection for Food Quality Assessment Based on Decorated Silicon Transistor Arrays

Wafer-Scale Integration of Metal Oxide Nanocrystals on Gas Sensor Chips via Direct Lithographic Patterning

Nanomaterial-based gas sensors are essential due to their high sensitivity and scalability, enabling efficient gas detection across diverse applications. However, a key challenge hindering their practical applications is the variation in sensing performance between devices. Addressing this requires careful consideration of the relationship between on-chip sensing materials and miniaturized devices. As feature sizes reduce to the microscale, accurately and uniformly positioning sensing nanomaterials onto specific regions of the device electrodes becomes increasingly difficult. This challenge arises from the incompatibility between the bottom-up nanomaterial synthesis methods and the top-down lithography-based fabrication processes. Herein, we introduce a cleanroom-compatible fabrication workflow for chemiresistive gas sensors employing direct lithographic patterning of metal oxide nanocrystals. Gas sensors located across different regions of a 4 in. wafer exhibit highly consistent gas-sensing performances, highlighting the potential of this approach, which integrates the strengths of both top-down and bottom-up approaches. This approach opens new opportunities for integrating a wide range of bottom-up synthesized functional nanomaterials into diverse types of chemical sensors.

Room-Temperature Operable, Fully Recoverable Ethylene Gas Sensor via Pulsed Electric Field Modulation

Ethylene (C2H4) is an important plant hormone, and its concentration can be used as an essential indicator of fruit quality. However, C2H4 is a non-polar gas with a relatively stable structure, making it challenging to detect and desorb without heating or irradiation. Here, a pulsed electric field modulation mode for non-polar gas detection is proposed, which enables fast and complete recovery of sensors at room temperature. Compared to the nearly impossible desorption without electric field assistance, the recovery time for 9 ppm C2H4 can be reduced to 78 s when the +60 V pulse gate voltage is applied, which is nearly equivalent to the recorded values under heating or irradiation (50 s under 250 °C). Most crucially, with the help of a gate-induced electric field, the sensor achieves complete desorption within 100 s. This work offers a new approach for fast non-polar gas detection at room temperature and on-chip integration of gas sensors.

Emerging Initiated Chemical Vapor Deposition Nanocoatings for Sustainable Food and Agriculture

Initiated chemical vapor deposition (iCVD) has emerged as a versatile technique for developing functional nanocoatings that address critical food and agricultural challenges. This review highlights the unique capacities of iCVD nanocoatings, which enable precise engineering of surface properties, such as targeted cellular and molecular interactions, antimicrobial activity, and fouling resistance. In addition, the solvent-free nature of iCVD is particularly advantageous for coating sensitive materials and complex geometries commonly used across food and agriculture applications. This review provides an overview of iCVD's chemistry, deposition mechanisms, and ability to control nanocoating morphology and composition. Key applications discussed include iCVD nanocoatings for food quality monitoring, pathogen detection, antimicrobial food packaging, biomass extraction, and irrigation water purification. By summarizing recent advancements and identifying emerging trends, this review showcases the potential of iCVD as a powerful tool for developing sustainable, nanoenabled solutions in modern food and agriculture production.

Amplifying Electron-Donor Signal of the "Water Gate" Enabled Low Detection Ability of a Field-Effect Transistor-Based Humidity Sensor

Low humidity detection down to the parts per million level is urgently demanded in various industrial applications. The hardly detected tiny electrical signal variations caused by a very small amount of water adsorption are one of the intrinsic reasons that restrain the detection limit of the humidity sensors. Herein, a carbon-based field-effect transistor (FET) humidity sensor utilizing adsorbed water as the dual function of a sensing gate and analyte was proposed. Owing to the electron donor property of the "water gate" that can serve as a negative voltage exerted on the dielectric layer, the electrical conductivity of the FET's channel can be significantly modulated, therefore realizing signal amplification. The proposed sensor presents a detection limit of lower than 1% RH. Besides, the fabricated sensors show good batch consistency (response deviation of 0.5%), repeatability, long-term stability, and acceptable hysteresis (6.3% relative humidity (RH)) in humidity detection. We hope that our work can offer a novel strategy for the application and integration of low humidity detection from the device aspect rather than sensing materials synthesis.

Optical and Electrical Dual-Mode Detection of a Carcinogenic Substance Based on Synergy of Liquid Crystals and Ionic Liquids

Visual, sensitive, and selective detection of carcinogenic substances is highly desired in portable health protection and practical medicine production. However, achieving this goal presents significant challenges with the traditional single-mode sensors reported so far, as they have limited sensing mechanisms and provide only a single output signal. Here, we report an effective optical and electrical dual-mode sensor for the visual, sensitive, and selective detection of N-nitrosodiethylamine (NDEA), a typical volatile carcinogenic substance, leveraging the synergy of ionic liquid-doped liquid crystals (IL-LC). The optical mode derived from LCs provides the sensor with a visual identification recognizable by the naked eye, while the electrical mode derived from ILs offers a quantitative detection capability. It is noteworthy that the synergistic effect of the IL and LC enhances the performance of both optical and electrical modes. Unique sensing mechanisms derived from the interaction between NDEA and IL-LC endow the sensor with excellent selectivity. As a proof of concept, a portable kit based on a dual-mode sensor has been developed for the real-time and on-site analysis of N-nitrosamine impurities in pharmaceuticals. This work provides valuable insights and a theoretical foundation for developing portable multimode chemical sensors.

Ratiometric fluorescent paper chip for monitoring the freshness of high protein foods

Accurately monitoring the freshness of high-protein foods has significant implications for both food safety and public welfare. Since a large amount of hydrogen sulfide (H2S) is produced during spoilage-related processes, abnormal H2S levels are often considered an important indicator of food spoilage. Therefore, we synthesized novel nanoparticles (NPs) containing Silicon (Si) dots and CdTe quantum dots to accurately assess the amount of sulfide ions (S2-) and thus the quality of high-protein foods in the early stage of storage. As the concentration of S2- increased, the fluorescence intensity of Si/CdTe NPs at λem = 488 nm increased, while the fluorescence intensity at λem = 620 nm was quenched. The fluorescence intensity ratio (F620/F488) was negatively linearly correlated to S2- concentrations in the range of 1-20 μM, with a detection limit of 0.3 μM. Furthermore, to achieve portable detection, we mixed Si/CdTe NPs with sodium carboxymethyl cellulose to prepare effective visual fluorescent sensing paper chips, which exhibited ideal porous structure, good particle dispersion, and excellent fluorescence properties. Incubating the paper chips with high-protein foods allowed for accurate monitoring of food freshness during storage. Therefore, this approach provided a reliable and portable method to determine H2S concentration using a novel concept to ensure the freshness and safety of high-protein foods.

Smart Packaging with Disposable NFC-enabled Wireless Gas Sensors for Monitoring Food Spoilage

Gas sensors present an alternative to traditional off-package food quality assessment, due to their high sensitivity and fast response without the need of sample pretreatment. The safe integration of gas sensors into packaging without compromising sensitivity, response rate, and stability, however, remains a challenge. Such packaging integration of spoilage sensors is crucial for preventing food waste and transitioning toward more sustainable supply chains. Here, we demonstrate a wide-ranging solution to enable the use of gas sensors for the continuous monitoring of food spoilage, building upon our previous work on paper-based electrical gas sensors (PEGS). By comparing various materials commonly used in the food industry, we analyze the optimal membrane to encapsulate PEGS for packaging integration. Focusing on spinach as a high-value crop, we assess the feasibility of PEGS to monitor the gases released during its spoilage at low and room temperatures. Finally, we integrated the sensors with wireless communication and batteryless electronics, creating a user-friendly system to evaluate the spoilage of spinach, operated by a smartphone via near-field communication (NFC). The work reported here provides an alternative approach that surpasses traditional on-site and in-line monitoring, ensuring comprehensive monitoring of food shelf life.

Triboelectric Nanogenerator for Self-Powered Gas Sensing

With the continuous acceleration of industrialization, gas sensors are evolving to become portable, wearable and environmentally friendly. However, traditional gas sensors rely on external power supply, which severely limits their applications in various industries. As an innovative and environmentally adaptable power generation technology, triboelectric nanogenerators (TENGs) can be integrated with gas sensors to leverage the benefits of both technologies for efficient and environmentally friendly self-powered gas sensing. This paper delves into the basic principles and current research frontiers of the TENG-based self-powered gas sensor, focusing particularly on innovative applications in environmental safety monitoring, healthcare, as well as emerging fields such as food safety assurance and smart agriculture. It emphasizes the significant advantages of TENG-based self-powered gas sensor systems in promoting environmental sustainability, achieving efficient sensing at room temperature, and driving technological innovations in wearable devices. It also objectively analyzes the technical challenges, including issues related to performance enhancement, theoretical refinement, and application expansion, and provides targeted strategies and future research directions aimed at paving the way for continuous progress and widespread applications in the field of self-powered gas sensors.

Inkjet-Printed Localized Surface Plasmon Resonance Subpixel Gas Sensor Array for Enhanced Identification and Visualization of Gas Spatial Distributions from Multiple Odor Sources

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities

BACKGROUND

Real-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff.

RESULTS

We have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment.

SIGNIFICANCE

Accurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.

Phase-Engineered MoSe2/CeO2 Composites for Room-Temperature Gas Sensing with a Drastic Discrimination of NH3 and TEA Gases

Detecting and distinguishing between hazardous gases with similar odors by using conventional sensor technology for safeguarding human health and ensuring food safety are significant challenges. Bulky, costly, and power-hungry devices, such as that used for gas chromatography-mass spectrometry (GC-MS), are widely employed for gas sensing. Using a single chemiresistive semiconductor or electric nose (e-nose) gas sensor to achieve this objective is difficult, mainly because of its selectivity issue. Thus, there is a need to develop new materials with tunable and versatile sensing characteristics. Phase engineering of two-dimensional materials to better utilize their physiochemical properties has attracted considerable attention. Here, we show that MoSe2 phase-transition/CeO2 composites can be effectively used to distinguish ammonia (NH3) and triethylamine (TEA) at room temperature. The phase transition of nanocomposite samples from semimetallic (1T) to semiconducting (2H) prepared at different synthesis temperatures is confirmed via X-ray photoelectron spectroscopy (XPS). A composite sensor in which the 2H phase of MoSe2 is predominant lacks discrimination capability and is less responsive to NH3 and TEA. An MoSe2/CeO2 composite sensor with a higher 1T phase content exhibits high selectivity for NH3, whereas one with a higher 2H phase content (2H > 1T) shows more selective behavior toward TEA. For example, for 50% relative humidity, the MoSe2/CeO2 sensor's signal changes from the baseline by 45% and 58% for 1 ppm of NH3 and TEA, respectively, indicating a low limit of detection (LOD) of 70 and 160 ppb, respectively. The composites' superior sensing characteristics are mainly attributed to their large specific surface area, their numerous active sites, presence of defects, and the n-n type heterojunction between MoSe2 and CeO2. The sensing mechanism is elucidated using Raman spectroscopy, XPS, and GC-MS results. Their phase-transition characteristics render MoSe2/CeO2 sensors promising for use in distributed, low-cost, and room-temperature sensor networks, and they offer new opportunities for the development of integrated advanced smart sensing technologies for environmental and healthcare.

Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence

In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

Research progress of chilled meat freshness detection based on nanozyme sensing systems

It is important to develop rapid, accurate, and portable technologies for detecting the freshness of chilled meat to meet the current demands of meat industry. This report introduces freshness indicators for monitoring the freshness changes of chilled meat, and systematically analyzes the current status of existing detection technologies which focus on the feasibility of using nanozyme for meat freshness sensing detection. Furthermore, it examines the limitations and foresees the future development trends of utilizing current nanozyme sensing systems in evaluating chilled meat freshness. Harmful chemicals are produced by food spoilage degradation, including biogenic amines, volatile amines, hydrogen sulfide, and xanthine, which have become new freshness indicators to evaluate the freshness of chilled meat. The recognition mechanisms are clarified based on the special chemical reaction with nanozyme or directly inducting the enzyme-like catalytic activity of nanozyme.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

In Situ Growth of Conductive Metal-Organic Framework onto Cu2O for Highly Selective and Humidity-Independent Hydrogen Sulfide Detection in Food Quality Assessment

The sensitivity of chemiresistive gas sensors based on metal oxide semiconductors (MOSs) has been inherently affected by ambient humidity because their reactive oxygen species are easily hydroxylated by water molecules, which significantly reduces the accuracy of the gas sensors in food quality assessment. Although conventional metal organic frameworks (MOFs) can serve as coatings for MOSs for humidity-independent gas detection, they have to operate at high working temperatures due to their low or nonconductivity, resulting in high power consumption, significant manufacturing inconvenience, and short-term stability due to the oxidation of MOFs. Here, the conductive and thickness-controlled CuHHTP (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene)-coated Cu2O are developed by combining in situ etching and layer-by-layer liquid-phase growth method, which achieves humidity-independent detection of H2S at room temperature. The response to H2S only decreases by 2.6% below 75% relative humidity (RH), showing a 9.6-fold improvement than the bare Cu2O sensor, which is ascribed to the fact that the CuHHTP layer hinders the adsorption of water molecules. Finally, a portable alarm system is developed to monitor food quality by tracking released H2S. Compared with gas chromatography method, their relative error is within 9.4%, indicating a great potential for food quality assessment.

Multi-Functional Applications of H-Glass Embedded with Stable Plasmonic Gold Nanoislands

Metal nanoparticles (MNPs) are synthesized using various techniques on diverse substrates that significantly impact their properties. However, among the substrate materials investigated, the major challenge is the stability of MNPs due to their poor adhesion to the substrate. Herein, it is demonstrated how a newly developed H-glass can concurrently stabilize plasmonic gold nanoislands (GNIs) and offer multifunctional applications. The GNIs on the H-glass are synthesized using a simple yet, robust thermal dewetting process. The H-glass embedded with GNIs demonstrates versatility in its applications, such as i) acting as a room temperature chemiresistive gas sensor (70% response for NO2 gas); ii) serving as substrates for surface-enhanced Raman spectroscopy for the identifications of Nile blue (dye) and picric acid (explosive) analytes down to nanomolar concentrations with enhancement factors of 4.8 × 106 and 6.1 × 105 , respectively; and iii) functioning as a nonlinear optical saturable absorber with a saturation intensity of 18.36 × 1015 W m-2 at 600 nm, and the performance characteristics are on par with those of materials reported in the existing literature. This work establishes a facile strategy to develop advanced materials by depositing metal nanoislands on glass for various functional applications.

Recent design strategies and applications of organic fluorescent probes for food freshness detection

Food spoilage poses a significant risk to human health, making the assessment of food freshness essential for ensuring food safety and quality. In recent years, there has been rapid progress in the development of fast detection technologies for food freshness. Among them, organic fluorescent probes have garnered significant attention in the field of food safety and sensing due to their easy functionalization, high sensitivity, and user-friendly nature. To comprehensively examine the latest advancements in organic fluorescent probes for food freshness detection, this review summarized their applications within the past five years. Initially, the fundamental detection principles of organic fluorescent probes are outlined. Subsequently, the recent research progress in utilizing organic fluorescent probes to detect various chemical indicators of freshness are discussed. Finally, the challenges and future directions for organic fluorescent probes in food freshness detection are elaborated upon. While, organic fluorescent probes have demonstrated their effectiveness in evaluating food freshness and possess great potential for practical applications, further research is still needed to enable their widespread commercial utilization. With continued advancements in synthesis and functionalization techniques, organic fluorescent probes will contribute to enhancing the efficiency of food safety detection.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

A flexible and wearable PET-based chemiresistive H2S gas sensor modified with MoS2-AgCl@AgNPs nanocomposite for the dynamic monitoring of egg spoilage

In this study, a flexible and wearable chemiresistive hydrogen sulfide (H2S) sensor is developed by modifying the MoS2-AgCl@AgNPs (MAAN) nanocomposite on a flexible PET-based Au interdigital electrode (FPAIDE) (MAAN/FPAIDE) to monitor egg spoilage at room temperature inexpensively. A new method is developed for the low-cost batch fabrication of MAAN/FPAIDEs by laser direct writing. The morphology and composition of the synthesized MAAN nanocomposite are investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and transmission electron microscopy (TEM). Based on the oxygen adsorption model, a new H2S sensing mechanism is discussed, which is related to the formation of p-n junctions between MoS2 and AgCl and the specific adsorption of H2S by AgNPs on the MAAN sensing layer, causing a decrease in resistance. X-ray photoelectron spectroscopy (XPS) is used to characterize the charge transfer between gas molecules and the MAAN sensing layer and sulfide generation during the response process. The concentration of H2S can be detected down to 27 ppb at 25 °C. Finally, the prepared sensor has been successfully utilized in the real-time monitoring of egg spoilage with satisfactory results, indicating its great potential for the application of fresh food quality and safety supervision and the smart packaging of poultry eggs.

Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas-sensing materials, and it exhibits low detection limits for H2 S and NH3 gases (0.01 ppm), which are high-protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.

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.

Four levels of in-sensor computing in bionic olfaction: from discrete components to multi-modal integrations

Sensing and computing are two important ways in which humans attempt to perceive and understand the analog world through digital devices. Analog-to-digital converters (ADCs) discretize analog signals while the data bus transmits digital data between the components of a computer. With the increase in sensor nodes and the application of deep neural networks, the energy and time consumption limit the increment of data throughput. In-sensor computing is a computing paradigm that integrates sensing, storage, and processing in one device without ADCs and data transfer. According to the integration degree, herein, we summarize four levels of in-sensor computing in the field of artificial olfactory. In the first level, we show that different functions are conducted by using discrete components. Next, the data conversion and transfer are exempt within the in-memory computing architecture with necessary data encoding. Subsequently, in-sensor computing is integrated into a single device. Finally, multi-modal in-sensor computing is proposed to improve the quality and reliability of the classification results. At the end of this minireview, we provide an outlook on the use of metal nanoparticle devices to achieve such in-sensor computing for bionic olfaction.

Quartz-Enhanced Photoacoustic Spectroscopy Assisted by Partial Least-Squares Regression for Multi-Gas Measurements

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3), and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3, and CH4 and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

Wafer-Scale Field-Effect Transistor-Type Sensor Using a Carbon Nanotube Film as a Channel for Ppb-Level Hydrogen Sulfide Detection

Sulfur hexafluoride is widely used in power equipment because of its excellent insulation and arc extinguishing properties. However, severe damage to power equipment may be caused and a large-scale collapse of the power grid may occur when SF6 is decomposed into H2S, SOF2, and SO2F2. It is difficult to detect the SF6 concentration as it is a kind of inert gas. Generally, the trace gas decomposed in the early stage of SF6 is detected to achieve the function of early warning. Consequently, it is of great significance to realize the real-time detection of trace gases decomposed from SF6 for the early fault diagnosis of power equipment. In this work, a wafer-scale gate-sensing carbon-based FET gas sensor is fabricated on a four-inch carbon wafer for the detection of H2S, a decomposition product of SF6. The carbon nanotubes with semiconductor properties and the noble metal Pt are respectively used as a channel and a sensing gate of the FET-type gas sensor, and the channel transmission layer and the sensing gate layer each play an independent role and do not interfere with each other by introducing the gate dielectric layer Y2O3, giving full play to their respective advantages to forming an integrated sensor of gas detection and signal amplification. The detection limit of the as-prepared gate-sensing carbon-based FET gas sensor can reach 20 ppb, and its response deviation is not more than 3% for the different batches of gas sensors. This work provides a potentially useful solution for the industrial production of miniaturized and integrated gas sensors.

Two-in-One Sensor Based on PV4D4-Coated TiO2 Films for Food Spoilage Detection and as a Breath Marker for Several Diseases

Certain molecules act as biomarkers in exhaled breath or outgassing vapors of biological systems. Specifically, ammonia (NH₃) can serve as a tracer for food spoilage as well as a breath marker for several diseases. H₂ gas in the exhaled breath can be associated with gastric disorders. This initiates an increasing demand for small and reliable devices with high sensitivity capable of detecting such molecules. Metal-oxide gas sensors present an excellent tradeoff, e.g., compared to expensive and large gas chromatographs for this purpose. However, selective identification of NH₃ at the parts-per-million (ppm) level as well as detection of multiple gases in gas mixtures with one sensor remain a challenge. In this work, a new two-in-one sensor for NH₃ and H₂ detection is presented, which provides stable, precise, and very selective properties for the tracking of these vapors at low concentrations. The fabricated 15 nm TiO₂ gas sensors, which were annealed at 610 °C, formed two crystal phases, namely anatase and rutile, and afterwards were covered with a thin 25 nm PV4D4 polymer nanolayer via initiated chemical vapor deposition (iCVD) and showed precise NH₃ response at room temperature and exclusive H₂ detection at elevated operating temperatures. This enables new possibilities in application fields such as biomedical diagnosis, biosensors, and the development of non-invasive technology.

Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

A Biomass-Based Colorimetric Sulfur Dioxide Gas Sensor for Smart Packaging

Sulfur dioxide (SO₂) gas, which can effectively prohibit the growth of pathogenic microorganisms, has been internationally used in commercial food packaging to maintain high-quality food and reduce the incidence of foodborne illnesses. However, the current mainstream methods for SO₂ detection are either large and expensive instruments or synthesized chemical-based labels, which are not suitable for large-scale gas detection in food packaging. Recently, we discovered that petunia dye (PD), which is extracted from natural petunia flowers, demonstrates a highly sensitive colorimetric response to SO₂ gas with its total color difference (ΔE) modulation reaching up to 74.8 and detection limit down to 1.52 ppm. To apply the extracted petunia dye in smart packaging for real-time gas sensing and food-quality prediction, a flexible and freestanding PD-based SO₂ detection label is prepared by incorporating PD in biopolymers and assembling the films through a layer-by-layer approach. The developed label is utilized to predict grapes' quality and safety by monitoring the embedded SO₂ gas concentration. The developed colorimetric SO₂ detection label could potentially be used as an intelligent gas sensor for food status prediction in daily life, food storage, and supply chains.

Metal-oxide-semiconductor resistive gas sensors for fish freshness detection

Fish are prone to spoilage and deterioration during processing, storage, or transportation. Therefore, there is a need for rapid and efficient techniques to detect and evaluate fish freshness during different periods or conditions. Gas sensors are increasingly important in the qualitative and quantitative evaluation of high-protein foods, including fish. Among them, metal-oxide-semiconductor resistive (MOSR) sensors with advantages such as low cost, small size, easy integration, and high sensitivity have been extensively studied in the past few years, which gradually show promising practical application prospects. Herein, we take the detection, classification, and assessment of fish freshness as the actual demand, and summarize the physical and chemical changes of fish during the spoilage process, the volatile marker gases released, and their production mechanisms. Then, we introduce the advantages, performance parameters, and working principles of gas sensors, and summarize the MOSR gas sensors aimed at detecting different kinds of volatile marker gases of fish spoiling in the last 5 years. After that, this paper reviews the research and application progress of MOSR gas sensor arrays and electronic nose technology for various odor indicators and fish freshness detection. Finally, this review points out the multifaceted challenges (sampling system, sensing module, and pattern recognition technology) faced by the rapid detection technology of fish freshness based on metal oxide gas sensors, and the potential solutions and development directions are proposed from the view of multidisciplinary intersection.

Recovery of off-state stress-induced damage in FET-type gas sensor using self-curing method

The need for high-performance gas sensors is driven by concerns over indoor and outdoor air quality, and industrial gas leaks. Due to their structural diversity, vast surface area, and geometric tunability, metal oxides show significant potential for the development of gas sensing systems. Despite the fact that several previous reports have successfully acquired a suitable response to various types of target gases, it remains difficult to maintain the reliability of metal oxide-based gas sensors. In particular, the degradation of the sensor platform under repetitive operation, such as off-state stress (OSS) causes significant reliability issues. We investigate the impact of OSS on the gas sensing performances, including response, low-frequency noise, and signal-to-noise ratio of horizontal floating-gate field-effect-transistor (FET)-type gas sensors. The 1/f noise is increased after the OSS is applied to the sensor because the gate oxide is damaged by hot holes. Therefore, the SNR of the sensor is degraded by the OSS. We applied a self-curing method based on a PN-junction forward current at the body-drain junction to repair the damaged gate oxide and improve the reliability of the sensor. It has been demonstrated that the SNR degradation caused by the OSS can be successfully recovered by the self-curing method.

Machine Learning System To Monitor Hg2+ and Sulfide Using a Polychromatic Fluorescence-Colorimetric Paper Sensor

An optical monitoring device combining a smartphone with a polychromatic ratiometric fluorescence-colorimetric paper sensor was developed to detect Hg2+ and S2- in water and seafood. This monitoring included the detection of food deterioration and was made possible by processing the sensing data with a machine learning algorithm. The polychromatic fluorescence sensor was composed of blue fluorescent carbon quantum dots (CDs) (BU-CDs) and green and red fluorescent CdZnTe quantum dots (QDs) (named GN-QDs and RD-QDs, respectively). The experimental results and density functional theory (DFT) prove that the incorporation of Zn can improve the stability and quantum yield of CdZnTe QDs. According to the dynamic and static quenching mechanisms, GN-QDs and RD-QDs were quenched by Hg2+ and sulfide, respectively, but BU-CDs were not sensitive to them. The system colors change from green to red to blue as the concentration of the two detectors rises, and the limits of detection (LOD) were 0.002 and 1.488 μM, respectively. Meanwhile, the probe was combined with the hydrogel to construct a visual sensing intelligent test strip, which realized the monitoring of food freshness. In addition, a smartphone device assisted by multiple machine learning methods was used to text Hg2+ and sulfide in real samples. It can be concluded that the fabulous stability, sensitivity, and practicality exhibited by this sensing mechanism give it unlimited potential for assessing the contents of toxic and hazardous substances Hg2+ and sulfide.

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.

Fe3O4@uio66 core-shell composite for detection of electrolyte leakage from lithium-ion batteries

Fe₃O₄is an environmentally friendly gas sensing material with high response, but the cross-response to various analytes and poor thermal stability limit its practical applications. In this work, we prepared Fe₃O₄@uio66 core-shell composite via a facile method. The selective response to volatile organic compounds, especially to electrolyte vapors of lithium-ion batteries, as well as long-term stability of Fe₃O₄@uio66 has been dramatically enhanced compared to pure Fe₃O₄, due to the preconcentrator feature and thermal stability of the uio66 thin shell. Real-time detection of electrolyte leakage for an actual punctured lithium-ion battery was further demonstrated. The Fe₃O₄@uio66 sensor, after aging for 3 months, was able to detect the electrolyte leakage in 30 s, while the voltage of the punctured battery was maintained at the same level as that of a pristine battery over 6 h. This practical test results verified ability of the Fe₃O₄@uio66 sensor with long-term aging stability for hours of early safety warning of lithium-ion batteries.

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.

Engineered nanomaterials-based sensing systems for assessing the freshness of meat and aquatic products: A state-of-the-art review

Meat and aquatic products are susceptible to spoilage during distribution, transportation, and storage, increasing the urgency of freshness evaluation. Engineered nanomaterials (ENMs) typically with the diameter in the range of 1-100 nm exhibit fascinating physicochemical properties. ENMs-based sensing systems have received extensive attention for food freshness assessment due to the advantages of being fast, simple, and sensitive. This review focuses on summarizing the recent application of ENMs-based sensing systems for food freshness detection. First, chemical indicators related to the freshness of meat and aquatic products are described. Then, how to apply the ENMs including noble metal nanomaterials, metal oxide nanomaterials, carbon nanomaterials, and metal-organic frameworks for the construction of different sensing systems were described. Besides, the recent advance in ENMs-based colorimetric, fluorescent, electrochemical, and surface-enhanced Raman spectroscopy sensing systems for assessing the freshness of meat and aquatic products were outlined. Finally, the challenges and future research perspectives for the application of ENMs-based sensing systems were discussed. The ENMs-based sensing systems have been demonstrated as effective tools for freshness evaluation. The sensing performance of ENMs employed in different sensing systems depends on their composition, size, shape, and stability of nanoparticles. For the real application of ENMs in food industries, the risks and regulatory issues associated with nanomaterials need to be further considered. With the continuous development of nanomaterials and sensing devices, the ENMs-based sensors are expected to be applied in-field for rapid detection of the freshness of meat and aquatic products in the future.

Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning. Delightedly, rapid developments in neuromorphic computing provide a new paradigm to solve this dilemma. Furthermore, neuromorphic devices that can realize synaptic plasticity and neuromorphic function have extraordinary significance for neuromorphic system. A three-terminal neuromorphic transistor is one of the typical representatives. In addition, human body has five senses, including vision, touch, auditory sense, olfactory sense and gustatory sense, providing abundant information for brain. Inspired by the human perception system, developments in artificial perception system will give new vitality to intelligent robots. This review discusses the operation mechanism, function and application of neuromorphic transistors. The latest progresses in artificial perception systems based on neuromorphic transistors are provided. Finally, the opportunities and challenges of artificial perception systems are summarized.

Room Temperature Ammonia Gas Sensor Based on p-Type-like V2O5 Nanosheets towards Food Spoilage Monitoring

Gas sensors play an important role in many areas of human life, including the monitoring of production processes, occupational safety, food quality assessment, and air pollution monitoring. Therefore, the need for gas sensors to monitor hazardous gases, such as ammonia, at low operating temperatures has become increasingly important in many fields. Sensitivity, selectivity, low cost, and ease of production are crucial characteristics for creating a capillary network of sensors for the protection of the environment and human health. However, developing gas sensors that are not only efficient but also small and inexpensive and therefore integrable into everyday life is a difficult challenge. In this paper, we report on a resistive sensor for ammonia detection based on thin V₂O₅ nanosheets operating at room temperature. The small thickness and porosity of the V₂O₅ nanosheets give the sensors good performance for sensing ammonia at room temperature (RT), with a relative change of resistance of 9.4% to 5 ppm ammonia (NH₃) and an estimated detection limit of 0.4 ppm. The sensor is selective with respect to the seven interferents tested; it is repeatable and stable over the long term (four months). Although V₂O₅ is generally an n-type semiconductor, in this case the nanosheets show a p-type semiconductor behavior, and thus a possible sensing mechanism is proposed. The device's performance, along with its size, low cost, and low power consumption, makes it a good candidate for monitoring freshness and spoilage along the food supply chain.

Triboelectric Nanogenerators as Power Sources for Chemical Sensors and Biosensors

The recent advances of portable sensors in flexible and wearable form factors are drawing increasing attention worldwide owing to their requirement applications ranging from health monitoring to environment monitoring. While portability is critical for these applications, real-time data gathering also requires a reliable power supply-which is largely met with batteries. Besides the need for regular charging, the use of toxic chemicals in batteries makes it difficult to rely on them, and as a result different types of energy harvesters have been explored in recent years. Among these, triboelectric nanogenerators (TENGs) provide a promising platform for harnessing ambient energy and converting it into usable electric signals. The ease of fabrication and possibility to develop TENGs with a diverse range of easily available materials also make them attractive. This review focuses on the TENG technology and its efficient use as a power source for various types of chemical sensors and biosensors. The paper describes the underlying mechanism, various modes of working of TENGs, and representative examples of their utilization as power sources for sensing a multitude of analytes. The challenges associated with their adoption for commercial solutions are also discussed to stimulate further advances and innovations.

Colorimetric Sensor Based on Ag-Fe NTs for H2S Sensing

Meat waste is widely associated with spoilage caused by microbial growth and metabolism. Volatile compounds produced by microbial growth such as volatile sulfides could directly indicate the freshness of meat during distribution and storage. Herein, silver-iron nanotriangles (Ag-Fe NTs) for hydrogen sulfide (H₂S) detection were developed via one-pot facile reflux reactions. The Ag-Fe NTs were integrated into food packaging systems for the rapid, real-time, and nondestructive detection of the freshness of chilled broiler poultry. The principle of color development is that an increase in the volatile sulfide content leads to a change in the absorption wavelength caused by the etching of the Ag-Fe NTs, resulting in a color change (yellow to brown). The minimum H₂S concentrations detected by the naked eye and UV-vis spectrophotometer were 4 and 2 mg/m³, respectively. This label is economical and practical and can monitor the spoilage of chilled broiler meat products in real-time.

Ultra-Fast Portable and Wearable Sensing Design for Continuous and Wide-Spectrum Molecular Analysis and Diagnostics

The design and characterization of spatiotemporal nano-/micro-structural arrangement that enable real-time and wide-spectrum molecular analysis is reported and demonestrated in new horizons of biomedical applications, such as wearable-spectrometry, ultra-fast and onsite biopsy-decision-making for intraoperative surgical oncology, chiral-drug identification, etc. The spatiotemporal sesning arrangement is achieved by scalable, binder-free, functionalized hybrid spin-sensitive (<↑| or <↓|) graphene-ink printed sensing layers on free-standing films made of porous, fibrous, and naturally helical cellulose networks in hierarchically stacked geometrical configuration (HSGC). The HSGC operates according to a time-space-resolved architecture that modulate the mass-transfer rate for separation, eluation and detection of each individual compound within a mixture of the like, hereby providing a mass spectrogram. The HSGC could be used for a wide range of applictions, including fast and real-time spectrogram generator of volatile organic compounds during liquid-biopsy, without the need of any immunochemistry-staining and complex power-hungry cryogenic machines; and wearable spectrometry that provide spectral signature of molecular profiles emiited from skin in the course of various dietry conditions.

Emerging Applications of Versatile Polyaniline-Based Polymers in the Food Industry

Intrinsically conducting polymers (ICPs) have been widely studied in various applications, such as sensors, tissue engineering, drug delivery, and semiconductors. Specifically, polyaniline (PANI) stands out in food industry applications due to its advantageous reversible redox properties, electrical conductivity, and simple modification. The rising concerns about food safety and security have encouraged the development of PANI as an antioxidant, antimicrobial agent, food freshness indicator, and electronic nose. At the same time, it plays an important role in food safety control to ensure the quality of food. This study reviews the emerging applications of PANI in the food industry. It has been found that the versatile applications of PANI allow the advancement of modern active and intelligent food packaging and better food quality monitoring systems.

Wet-Adhesive On-Skin Sensors Based on Metal-Organic Frameworks for Wireless Monitoring of Metabolites in Sweat

Metal-organic frameworks (MOFs) with well-defined porous structures and tailored functionalities have been widely used in chemical sensing. However, the integration of MOFs with flexible electronic devices for wearable sensing is challenging because of their low electrical conductivity and fragile mechanical properties. Herein, a wearable sweat sensor for metabolite detection is presented by integrating an electrically conductive Ni-MOF with a flexible nanocellulose substrate. The MOF-based layered film sensor with inherent conductivity, highly porous structure, and active catalytic properties enables the selective and accurate detection of vitamin C and uric acid. More importantly, the lightweight sensor can conformably self-adhere to sweaty skin and exhibits high water-vapor permeability. Furthermore, a wireless epidermal nutrition tracking system for the in situ monitoring of the dynamics of sweat vitamin C is demonstrated, the results of which are comparable to those tested by high-performance liquid chromatography. This study opens a new avenue for integrating MOFs as the active layer in wearable electronic devices and holds promise for the future development of high-performance electronics with enhanced sensing, energy production, and catalytic capabilities through the implementation of multifunctional MOFs.

Ti3C2Tx MXene/urchin-like PANI hollow nanosphere composite for high performance flexible ammonia gas sensor

Ammonia (NH₃) has been used as a typical indicator to monitor food spoilage, human health, and air quality. However, the development of flexible NH₃ sensors with high response, excellent selectivity and low cost remains a huge challenge. Herein, a high performance NH₃ sensor based on Ti₃C₂T_x MXene nanosheet/urchin-like PANI hollow nanosphere composite (MP) was fabricated through template method and in situ polymerization. The NH₃ sensor is fabricated with no high cost electrodes through directly depositing this composite on flexible polyethylene terephthalate (PET) during polymerization. This optimized MP film sensor exhibits high response of 3.70 to 10 ppm NH₃ at room temperature, which is 4.74-fold in comparison with urchin-like PANI hollow nanosphere (u-PANI). It also shows excellent selectivity, good repeatability, satisfactory flexibility, air stability and low detection limit of 30 ppb. The effective morphology control and heterojunction construction of MP composite are responsible for superior sensing performance. Moreover, the application of this film sensor in the monitoring of the spoilage process of fresh pork is demonstrated. This study offers a new strategy for fabricating high performance flexible room-temperature NH₃ sensors, which may be scale fabrication and application in daily life.

A Family of 2D-MXenes: Synthesis, Properties, and Gas Sensing Applications

Gas sensors, capable of detecting and monitoring trace amounts of gas molecules or volatile organic compounds (VOCs), are in great demand for numerous applications including diagnosing diseases through breath analysis, environmental and personal safety, food and agriculture, and other fields. The continuous emergence of new materials is one of the driving forces for the development of gas sensors. Recently, 2D materials have been gaining huge attention for gas sensing applications, owing to their superior electrical, optical, and mechanical characteristics. Especially for 2D MXenes, high specific area and their rich surface functionalities with tunable electronic structure make them compelling for sensing applications. This Review discusses the latest advancements in the 2D MXenes for gas sensing applications. It starts by briefly explaining the family of MXenes, their synthesis methods, and delamination procedures. Subsequently, it outlines the properties of MXenes. Then it describes the theoretical and experimental aspects of the MXenes-based gas sensors. Discussion is also extended to the relation between sensing performance and the structure, electronic properties, and surface chemistry. Moreover, it highlights the promising potential of these materials in the current gas sensing applications and finally it concludes with the limitations, challenges, and future prospects of 2D MXenes in gas sensing applications.

Rapid Assessment of Meat Freshness by the Differential Sensing of Organic Sulfides Emitted during Spoilage

In this work, we report the fabrication of a two-member fluorescence sensor array that enables the assessment of three stages (fresh, slightly spoiled, and moderately or severely spoiled) of meat spoilage. The first member of the array, which has strong chalcogen bonding and sulfur-π interactions with organic sulfides, exhibits very high sensitivity, while the second member of the array, which has weak chalcogen bonding and sulfur-π interactions with organic sulfides, exhibits lower sensitivity. On the basis of the combined fluorescence responses of the two members, three stages of meat spoilage, including fresh, slightly spoiled, and moderately or severely spoiled, can be monitored. Notably, using the volatiles collected from 5 g of meat products over a short period of time (1 min), this two-member sensor array achieves sensitive responses to the organic sulfides emitted from the meats. The capacity of this method to rapidly assess meat freshness facilitates its practical application, as illustrated by the monitoring of the freshness of chicken and pork products in the real world.

A Colorimetric Ag+ Probe for Food Real-Time Visual Monitoring

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance.

Impact of Lateral SnO2 Nanofilm Channel Geometry on a 1024 Crossbar Chemical Sensor Array

We propose a rational strategy to fabricate thermally robust, highly integrated molecular and gas sensors utilizing a lateral SnO₂ nanofilm channel geometry on a 1024 crossbar sensor array. The proposed lateral channel geometry substantially suppresses the detrimental effects of parasitic interconnect wire resistances compared with those of a conventional vertical sandwich-type crossbar array because of its excellent resistance controllability. A conductive oxide top-contact electrode on the lateral SnO₂ nanofilm channel enhances the thermal stability at temperatures of up to 500 °C in ambient air. Integrating this lateral SnO₂ nanofilm geometry with analog circuits enables the operation of a 1024 crossbar sensor array without selector devices to avoid sneak currents. The developed 1024 crossbar sensor array system detects the local spatial distribution of the molecular gas concentration. The spatial data of molecular concentrations include molecule-specific data to distinguish various volatile molecules based on their vapor pressures. Thus, this integrated crossbar sensor array system using lateral nanofilm geometry offers a platform for robust, reliable, highly integrated molecular and gas sensors.

UV illumination-enhanced ultrasensitive ammonia gas sensor based on (001)TiO2/MXene heterostructure for food spoilage detection

Ammonia has been used as an important marker to indicate the extent of food spoilage. However, current gas sensors for ammonia suffer from either insufficient sensitivity and selectivity or unsatisfactory levels of automation, impeding their practical application for on-site and real-time monitoring of food quality. To overcome these limitations, we propose here the design of a sensing material by in-situ growing (001)TiO₂ onto a two-dimensional transition-metal carbide (Ti₃C₂T_x, MXene). In this design, TiO₂ with a highly active (001) crystal plane provides efficient photogeneration under UV irradiation, while Ti₃C₂T_x can store holes through Schottky junction formed at the interface with TiO₂, which greatly promotes the separation of electron-hole pairs, thereby enhancing ammonia sensing performance. By further introducing UV light for electron excitation, the (001)TiO₂/Ti₃C₂T_x based sensor shows 34 times higher sensitivity for ammonia (30 ppm) than that of Ti₃C₂T_x. The density functional theory further revealed that the (001) plane of TiO₂ and Ti₃C₂T_x composite configuration exhibited the highest adsorption affinity towards ammonia. Finally, an integrated circuit alarm system including near-field communication and a micro-controller system was designed to detect the decay process of fresh pork, fish, and shrimp. We believe such a sensing technology holds great promise in food quality monitoring.