A survey on gas sensing technology

Artificial Intelligence in Gas Sensing: A Review

The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based intelligent gas sensors include environmental monitoring, industrial safety, remote sensing, and medical diagnostics. AI, ML, and DL methods can process and interpret complex sensor data, allowing for improved accuracy, sensitivity, and selectivity, enabling rapid gas detection and quantitative concentration measurements based on sophisticated multiband, multispecies sensor systems. These methods can discern subtle patterns in sensor signals, allowing sensors to readily distinguish between gases with similar sensor signatures, enabling adaptable, cross-sensitive sensor systems for multigas detection under various environmental conditions. Integrating AI in gas sensor technology represents a paradigm shift, enabling sensors to achieve unprecedented performance, selectivity, and adaptability. This review describes gas sensor technologies and AI while highlighting approaches to AI-sensor integration.

From materials to applications: a review of research on artificial olfactory memory

Olfactory memory forms the basis for biological perception and environmental adaptation. Advancing artificial intelligence to replicate this biological perception as artificial olfactory memory is essential. The widespread use of various robotic systems, intelligent wearable devices, and artificial olfactory memories modeled after biological olfactory memory is anticipated. This review paper highlights current developments in the design and application of artificial olfactory memory, using examples from materials science, gas sensing, and storage systems. These innovations in gas sensing and neuromorphic technology represent the cutting edge of the field. They provide a robust scientific foundation for the study of intelligent bionic devices and the development of hardware architectures for artificial intelligence. Artificial olfaction will pave the way for future advancements in intelligent recognition by progressively enhancing the level of integration, understanding of mechanisms, and application techniques of machine learning algorithms.

ENose: a new frontier for non-invasive cancer detection and monitoring

Electronic Nose (ENose) technology has emerged as a transformative tool in medical diagnostics, leveraging sensor arrays that mimic the human olfactory system to detect odors and volatile organic compounds (VOCs) in various biological samples. ENose systems utilize a range of sensor types, such as metal oxide semiconductors and conducting polymers, to generate unique “smell fingerprints” through pattern recognition algorithms. These systems have shown promise in diagnosing various medical conditions, including respiratory diseases, infectious diseases, metabolic disorders, and neurological conditions. Notably, ENose technology holds significant promise in cancer diagnostics, offering a non-invasive, cost-effective, and rapid approach to early detection and monitoring. It has demonstrated impressive accuracy (85%-95%) in detecting cancers and monitoring complications. However, challenges remain, including issues with standardization, sensor sensitivity, and data interpretation. Despite these hurdles, ENose technology’s market growth is fueled by the increasing prevalence of chronic diseases. Recent developments in Artificial Intelligence (AI), particularly machine learning techniques like deep learning, have enhanced the diagnostic accuracy and robustness of ENose devices. This paper explores the evolution, core principles, applications, challenges, and future potential of ENose technology, with particular emphasis on integrating recent advancements in AI for enhanced detection and interpretation. Future research and collaboration across sectors are essential to overcome existing challenges and integrate ENose into mainstream healthcare.

Advancements in Breathomics: Special Focus on Electrochemical Sensing and AI for Chronic Disease Diagnosis and Monitoring

This Review examines the potential of breathomics in enhancing disease monitoring and diagnostic precision when integrated with artificial intelligence (AI) and electrochemical sensing techniques. It discusses breathomics' potential for early and noninvasive disease diagnosis with a focus on chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and lung cancer, which have been well studied in the context of VOC association with diseases. The noninvasive nature of exhaled breath analysis can be advantageous compared to traditional diagnostic methods for CKD, which often rely on blood and urine testing. VOC analysis can enhance spirometry and imaging methods used in COPD diagnosis, providing a more comprehensive picture of the disease's progression. Breathomics could also provide a less intrusive and potentially earlier diagnostic approach for lung cancer, which is now dependent on imaging and biopsy. The combination of breathomics, electrochemical sensing, and AI could lead to more personalized and successful treatment plans for chronic illnesses using AI algorithms to decipher complicated VOC patterns. This Review assesses the viability and effectiveness of combining breathomics with electrochemical sensors and artificial intelligence by synthesizing recent research findings and technological developments.

Toward the Optimization of a Perovskite-Based Room Temperature Ozone Sensor: A Multifaceted Approach in Pursuit of Sensitivity, Stability, and Understanding of Mechanism

Metal halide perovskites (MHPs) have attracted significant attention owing to their simple manufacturing process and unique optoelectronic properties. Their reversible electrical or optical property changes in response to oxidizing or reducing environments make them prospective materials for gas detection technologies. Despite advancements in perovskite-based sensor research, the mechanisms behind perovskite-gas interactions, vital for sensor performance, are still inconclusive. This work presents the first evaluation of the sensing performance and long-term stability of MHPs, considering factors such as halide composition variation and Mn doping levels. The research reveals a clear correlation between halide composition and sensing behavior, with Br-rich sensors displaying a p-type response to O3 gas, while Cl-rich counterparts exhibit n-type sensing behavior. Notably, Mn-doping significantly enhances O3 sensing performance by facilitating the gas adsorption process, as supported by both atomistic simulations and experimental evidence. Long-term evaluation of the sensors provides valuable insights into evolving sensing behaviors, highlighting the impact of dynamic instabilities over time. Overall, this research offers insights into optimal halide combination and Mn-doping levels, representing a significant step forward in engineering room temperature perovskite-based gas sensors that are not only low-cost and high-performing but also durable, marking a new era in sensor technology.

Cutting-Edge Sensor Design: MIP Nanoparticle-Functionalized Nanofibers for Gas-Phase Detection of Limonene in Predictive Agriculture

As population growth and climate change intensify pressures on agriculture, innovative strategies are vital for ensuring food security, optimizing resources, and protecting the environment. This study introduces a novel approach to predictive agriculture by utilizing the unique properties of terpenes, specifically S(-)-limonene, emitted by plants under stress. Advanced sensors capable of detecting subtle limonene variations offer the potential for early stress diagnosis and precise crop interventions. This research marks a significant leap in sensor technology, introducing an innovative active sensing material that combines molecularly imprinted polymer (MIP) technology with electrospinning. S(-)-limonene-selective MIP nanoparticles, engineered using methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA), were synthesized with an average diameter of ~160 nm and integrated into polyvinylpyrrolidone (PVP) nanofibers reinforced with multiwall carbon nanotubes (MWCNTs). This design produced a conductive and highly responsive sensing layer. The sensor exhibited rapid stabilization (200 s), a detection limit (LOD) of 190 ppb, and a selectivity index of 73% against similar monoterpenes. Optimal performance was achieved at 55% relative humidity, highlighting environmental conditions' importance. This pioneering use of polymeric MIP membranes in chemiresistive sensors for limonene detection opens new possibilities for monitoring VOCs, with applications in agricultural stress biomarkers, contaminant detection, and air quality monitoring, advancing precision agriculture and environmental protection.

Graphene-Based Fiber Materials for Gas Sensing Applications: State of the Art Review

The importance of gas sensors is apparent as the detection of gases and pollutants is crucial for environmental monitoring and human safety. Gas sensing devices also hold the potential for medical applications as health monitoring and disease diagnostic tools. Gas sensors fabricated from graphene-based fibers present a promising advancement in the field of sensing technology due to their enhanced sensitivity and selectivity. The diverse chemical and mechanical properties of graphene-based fibers-such as high surface area, flexibility, and structural stability-establish them as ideal gas-sensing materials. Most significantly, graphene fibers can be readily tuned to detect a wide range of gases, making them highly versatile in gas-sensing technologies. This review focuses on graphene-based composite fibers for gas sensors, with an emphasis on the preparation processes used to achieve these fibers and the gas sensing mechanisms involved in their sensors. Graphene fiber gas sensors are presented based on the chemical composition of their target gases, with detailed discussions on their sensitivity and performance. This review reveals that graphene-based fibers can be prepared through various methods and can be effectively integrated into gas-sensing devices for a diverse range of applications. By presenting an overview of developments in this field over the past decade, this review highlights the potential of graphene-based fiber sensors and their prospective integration into future technologies.

Computational Design of the Electronic Response for Volatile Organic Compounds Interacting with Doped Graphene Substrates

Changes in the work function provide a fingerprint to characterize analyte binding in charge transfer-based sensor devices. Hence, a rational sensor design requires a fundamental understanding of the microscopic factors controlling the modification of the work function. In the current investigation, we address the mechanisms behind the work function change (WFC) for the adsorption of four common volatile organic compounds (toluene, ethanol, 2-Furfurylthiol, and guaiacol) on different nitrogen-doped graphene-based 2D materials using density functional theory. We show that competition between the surface dipole moment change induced by spatial charge redistribution, the one induced by the pure adsorbate, and the one caused by the surface deformation can quantitatively predict the work function change. Furthermore, we also show this competition can explain the non-growing work function change behavior in the increasing concentrations of nitrogen-doped graphenes. Finally, we propose possible design principles for WFC of VOCs interacting with N-doped graphene materials.

Novel Gas Sensing Approach: ReS2/Ti3C2Tx Heterostructures for NH3 Detection in Humid Environments

Continuous monitoring of ammonia (NH3) in humid environments poses a notable challenge for gas sensing applications because of its effect on sensor sensitivity. The present work investigates the detection of NH3 in a natural humid environment utilizing ReS2/Ti3C2Tx heterostructures as a sensing platform. ReS2 nanosheets were vertically grown on the surface of Ti3C2Tx sheets through a hydrothermal synthetic approach, resulting in the formation of ReS2/Ti3C2Tx heterostructures. The structural, morphological, and optical properties of ReS2/Ti3C2Tx were investigated using various state-of-the-art techniques, including scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, zeta potential, Brunauer-Emmett-Teller technique, and Raman spectroscopy. The heterostructures exhibited 1.3- and 8-fold increases in specific surface area compared with ReS2 and Ti3C2Tx, respectively, potentially enhancing the active gas adsorption sites. The electrical investigations of the ReS2/Ti3C2Tx-based sensor demonstrated enhanced selectivity and superior sensing response ranging from 7.8 to 12.4% toward 10 ppm of NH3 within a relative humidity range of 15-85% at room temperature. These findings highlight the synergistic effect of ReS2 and Ti3C2Tx, offering valuable insights for NH3 sensing in environments with high humidity, and are explained in the gas sensing mechanism.

Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures

Gas sensing is essential for detecting and measuring gas concentrations across various environments, with applications in environmental monitoring, industrial safety, and healthcare. The integration of two-dimensional (2D) materials, organic materials, and metal oxides has significantly advanced gas sensor technology, enhancing its sensitivity, selectivity, and response times at room temperature. This review examines the progress in optically activated gas sensors, with emphasis on 2D materials, metal oxides, and organic materials, due to limited studies on their use in optically activated gas sensors, in contrast to other traditional gas-sensing technologies. We detail the unique properties of these materials and their impact on improving the figures of merit (FoMs) of gas sensors. Transition metal dichalcogenides (TMDCs), with their high surface-to-volume ratio and tunable band gap, show exceptional performance in gas detection, especially when activated by UV light. Graphene-based sensors also demonstrate high sensitivity and low detection limits, making them suitable for various applications. Although organic materials and hybrid structures, such as metal-organic frameworks (MoFs) and conducting polymers, face challenges related to stability and sensitivity at room temperature, they hold potential for future advancements. Optically activated gas sensors incorporating metal oxides benefit from photoactive nanomaterials and UV irradiation, further enhancing their performance. This review highlights the potential of the advanced materials in developing the next generation of gas sensors, addressing current research gaps and paving the way for future innovations.

Graphene Thermal Infrared Emitters Integrated into Silicon Photonic Waveguides

Cost-efficient and easily integrable broadband mid-infrared (mid-IR) sources would significantly enhance the application space of photonic integrated circuits (PICs). Thermal incandescent sources are superior to other common mid-IR emitters based on semiconductor materials in terms of PIC compatibility, manufacturing costs, and bandwidth. Ideal thermal emitters would radiate directly into the desired modes of the PIC waveguides via near-field coupling and would be stable at very high temperatures. Graphene is a semimetallic two-dimensional material with comparable emissivity to thin metallic thermal emitters. It allows maximum coupling into waveguides by placing it directly into their evanescent fields. Here, we demonstrate graphene mid-IR emitters integrated with photonic waveguides that couple directly into the fundamental mode of silicon waveguides designed to work in the so-called "fingerprint region" relevant for gas sensing. High broadband emission intensity is observed at the waveguide-integrated graphene emitter. The emission at the output grating couplers confirms successful coupling into the waveguide mode. Thermal simulations predict emitter temperatures up to 1000 °C, where the blackbody radiation covers the mid-IR region. A coupling efficiency η, defined as the light emitted into the waveguide divided by the total emission, of up to 68% is estimated, superior to data published for other waveguide-integrated emitters.

Feedback based gas sensing setup for ppb to ppm level sensing

Sensing and quantification of gas at low concentrations is of paramount importance, especially with highly flammable and explosive gases such as hydrogen. Standard gas sensing setups have a limit of measuring ultra-low concentrations of few parts per billion unless the external gas cylinders are changed to ones with low concentrations. In this work, we describe a home-built resistance based gas sensing setup that can sense across a wide concentration range, from parts per billion to parts per million, accurately. This was achieved using two dilution chambers: a process chamber and a feedback assembly where a part of the output gas from the dilution chamber is fed back to the inlet mass flow controller, enabling enhanced dilutions without increasing the number of mass flow controllers. In addition, the gas-sensing setup can measure across a large temperature range of 77-900 K. The developed setup was then calibrated using palladium thin films and ZnO nanoparticle thin films. The setup was tested for reproducibility, concentration response, temperature response, etc. Corresponding sensitivity values were calculated and found to be in good agreement with published values, validating our setup design.

Effect of gas turbulence in quartz-enhanced photoacoustic spectroscopy: A comprehensive flow field analysis

Here we present a computational and experimental fluid dynamics study for the characterization of the flow field within the gas chamber of a Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) sensor, at different flow rates at the inlet of the chamber. The transition from laminar to turbulent regime is ruled both by the inlet flow conditions and dimension of the gas chamber. The study shows how the distribution of the flow field in the chamber can influence the QEPAS sensor sensitivity, at different operating pressures. When turbulences and eddies are generated within the gas chamber, the efficiency of photoacoustic generation is significantly altered.

High-Performance Refractive Index and Temperature Sensing Based on Toroidal Dipole in All-Dielectric Metasurface

This article shows an all-dielectric metasurface consisting of "H"-shaped silicon disks with tilted splitting gaps, which can detect the temperature and refractive index (RI). By introducing asymmetry parameters that excite the quasi-BIC, there are three distinct Fano resonances with nearly 100% modulation depth, and the maximal quality factor (Q-factor) is over 104. The predominant roles of different electromagnetic excitations in three distinct modes are demonstrated through near-field analysis and multipole decomposition. A numerical analysis of resonance response based on different refractive indices reveals a RI sensitivity of 262 nm/RIU and figure of merit (FOM) of 2183 RIU-1. This sensor can detect temperature fluctuations with a temperature sensitivity of 59.5 pm/k. The proposed metasurface provides a novel method to induce powerful TD resonances and offers possibilities for the design of high-performance sensors.

Fabrication of alcohol sensor using undoped and Al doped ZnO nanostructure film with polymer electrolyte gating

We report the fabrication of two terminal and three terminal gas sensor using Al-doped ZnO nanostructured-films and polymer electrolyte gate dielectric on glass substrate using vacuum free chemical method. The Al doped ZnO films are characterized by UV-vis Spectrometer, SEM, EDX and XRD. The characterization results have revealed the polycrystalline structure of both undoped and doped ZnO; with loosely packed, porous, and spherical granny nanostructure with mean grain size 20-10 nm and bandgap of the films is within the range of 3.12-3.16 eV. The conductivity of the ZnO film is tuned by Al concentration and the maximum value of conductivity was observed in 3 % Al doped ZnO films. Similarly, the best performance index of TFT such as current ON/OFF ratio, high transconductance and low threshold voltage was observed in 3 % Al doping concentration. The ordinary (two-terminal) sensor and three-terminal (FET) sensors' responses towards three different concentrations 50, 250, 500 ppm of ethanol and methanol vapors have been studied. The sensitivity of the film is modulated by Al concentration and higher value of sensitivity was achieved at 3 % Al doped ZnO films. The use of polymer electrolyte enhanced the sensitivity of the device which is more effective in methanol vapor. The Response-Recovery time of the sensor is significantly improved in three terminal devices than the two terminal devices.

Smart mid-infrared metasurface microspectrometer gas sensing system

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Ammonia Detection by Electronic Noses for a Safer Work Environment

Providing employees with proper work conditions should be one of the main concerns of any employer. Even so, in many cases, work shifts chronically expose the workers to a wide range of potentially harmful compounds, such as ammonia. Ammonia has been present in the composition of products commonly used in a wide range of industries, namely production in lines, and also laboratories, schools, hospitals, and others. Chronic exposure to ammonia can yield several diseases, such as irritation and pruritus, as well as inflammation of ocular, cutaneous, and respiratory tissues. In more extreme cases, exposure to ammonia is also related to dyspnea, progressive cyanosis, and pulmonary edema. As such, the use of ammonia needs to be properly regulated and monitored to ensure safer work environments. The Occupational Safety and Health Administration and the European Agency for Safety and Health at Work have already commissioned regulations on the acceptable limits of exposure to ammonia. Nevertheless, the monitoring of ammonia gas is still not normalized because appropriate sensors can be difficult to find as commercially available products. To help promote promising methods of developing ammonia sensors, this work will compile and compare the results published so far.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Modeling of gas sensor based on zinc oxide thin films by feedback loop using operational amplifier

Nanostructured Zincoxide thin-film is widely used as a sensing material because of its tunable surface microstructure and wide optical bandgap but synthesizing a film with desired value of resistance and reproducibility of film is challenging, particularly by chemical method. In this work, we showed how a ZnO film of arbitrary resistance can be used as a sensor without application of heat using operational amplifier. Zinc oxide thin film was synthesized by using the Sol-gel method (Spin coating) and was characterized by XRD and SEM which revealed wurtzite polycrystalline nature of Zinc oxide film with average grain size 17-25 nm. In this report, we designed a noble electronic circuit capable of detecting analyte gas molecule even if very small change in film resistance occurs due to the influence of gas molecule. In recently available sensors, the quality of the film degrades over time due to repeated heating and cooling, resulting in a reduced lifetime for the sensor. To address this issue and achieve higher sensitivity, as well as to fabricate an affordable, portable, precise, energy-efficient and durable device, this electronic model offers advantages over classical temperature-dependent sensors.

A new gas detection technique through cross-correlation with a complex aperiodic FBG

Optical cross-correlation is a technique that can achieve both high specificity and high sensitivity when deployed as the basis for a sensing technology. Offering significant gains in cost, size and complexity, it can also deliver significantly higher signal-to-noise ratios than traditional approaches such as absorption methodologies. In this paper, we present an optical cross-correlation technology constructed around a bespoke customised Fiber Bragg Grating (FBG). Exploiting the remarkable flexibility in design enabled by multiple aperiodic Bragg gratings, optical filters are devised that exactly mimic the absorption features of a target gas species (for this paper, acetyleneC 2 H 2 ) over some waveband of interest. This grating forms the heart of the sensor architecture described here that employs modulated optical cross-correlation for gas detection. An experimental demonstration of this approach is presented, and shown to be capable of differentiating between different concentrations of theC 2 H 2 target gas. Furthermore these measurements are shown to be robust against interloper species, with minimal impact on the detection signal-to-noise arising from the introduction of contaminant gases. This represents is a significant step toward the use of customised FBGs as low-cost, compact, and highly customisable photonic devices for deployment in gas detection.

Sensing potential of C6N8 for ammonia (NH3) and nitrogen triflouride (NF3): A DFT study

The detection of toxic gases (NH3 and NF3) in regulating and monitoring air quality in the atmosphere has drawn a lot of attention. Herein, we explored a novel material (C6N8) for the detection of the important but toxic gases (NH3 and NF3). We investigated the interactions of the NH3 and NF3 with C6N8 through DFT at B3LYP, ωB97XD, and non-DFT M06-2X. Counterpoise interaction energy values (Eint. cp.) of NH3@C6N8 and NF3@C6N8 are -0.45 eV and -3.51 eV (for B3LYP), -0.42 eV and 2.11 eV (for ωB97XD) and -0.44 eV and -3.41eV (for M06-2X), respectively. Complexes having the most stable configurations were then subjected to further analyses including frontier molecular orbitals, H-L gap, and conductivity of complexes. An increase in the H-L gap in complexes (NH3@C6N8 and NF3@C6N8) is observed. The conductivity of NH3@C6N8 and NF3@C6N8 decreases as compared to C6N8. A considerable change in dipole moment was seen in C6N8 before and after complex formation. This is because of the shifting of charge between C6N8 and gases (NH3 and NF3). CHELPG and NBO charge analysis were used to evaluate the amount of charge transfer between C6N8 and gases. These analyses demonstrate that NH3 and NF3 withdraw electron density from C6N8. It was found that NH3 tends to be physically adsorbed on C6N8 while NF3 adsorbs chemically on C6N8. NCI and QTAIM analyses were performed to investigate the kind of interactions between the surface (C6N8) and gases (NH3 and NF3). Furthermore, the recovery time of NH3@C6N8 and NF3@C6N8 shows that C6N8 can be a better choice for sensing NH3 and NF3 gases.

Effects of Visible Light on Gas Sensors: From Inorganic Resistors to Molecular Material-Based Heterojunctions

In the last two decades, many research works have been focused on enhancing the properties of gas sensors by utilising external triggers like temperature and light. Most interestingly, the light-activated gas sensors show promising results, particularly using visible light as an external trigger that lowers the power consumption as well as improves the stability, sensitivity and safety of the sensors. It effectively eliminates the possible damage to sensing material caused by high operating temperature or high energy light. This review summarises the effect of visible light illumination on both chemoresistors and heterostructure gas sensors based on inorganic and organic materials and provides a clear understanding of the involved phenomena. Finally, the fascinating concept of ambipolar gas sensors is presented, which utilised visible light as an external trigger for inversion in the nature of majority charge carriers in devices. This review should offer insight into the current technologies and offer a new perspective towards future development utilising visible light in light-assisted gas sensors.

Exploring the sensing potential of Fe-decorated h-BN toward harmful gases: a DFT study

Gas sensing technology has a broad impact on society, ranging from environmental and industrial safety to healthcare and everyday applications, contributing to a safer, healthier, and more sustainable world. We studied pure and Fe-decorated hexagonal boron nitride (h-BN) gas sensor for monitoring of carbon-based gases using density functional theory (DFT). The calculations utilized the Generalized Gradient Approximation with the Perdew-Burke-Ernzerhof (GGA-PBE) exchange-correlation functional. The novelty of our study lies in the investigation of the adsorption of harmful gases such as carbonyl sulfide, carbinol, carbimide, and carbonyl fluoride on both pure and Fe-decorated h-BN. The deviation in structural, electronic, and adsorption properties of h-BN due to Fe decoration has been studied along with the sensing ability to design said material towards carbon monoxide (CO), carbon dioxide (CO2), carbonyl sulfide (COS), carbinol, (CH4O), carbimide (CH2N2), and carbonyl fluoride (CF2O) gases. Gases such as CO, COS, CH2N2, and CF2O exhibited chemisorption, while CO2, and CH4O exhibited physisorption behavior. The introduction of Fe altered the semiconductor properties of h-BN and rendered it metallic. Enhanced electronic properties were observed due to a robust hybridization occurring between the d-orbitals of Fe-decorated BN and the gas molecules. The extended recovery periods observed for gases, aside from CO2, indicate their adhesive interactions with Fe-decorated h-BN. The reduction in desorption duration as temperature rises allows Fe-decorated h-BN to function as a reversible gas sensor. This research opens up a novel pathway for the synthesis and advancement of cost-effective, environmentally friendly double-atom catalysts with high sensitivity for capturing and detecting molecules such as CO, COS, CH2N2, CO2, CH4O, and CF2O.

Recent Advances in Low-Dimensional Metal Oxides via Sol-Gel Method for Gas Detection

Low-dimensional metal oxides have drawn significant attention across various scientific domains due to their multifaceted applications, particularly in the field of environment monitoring. Their popularity is attributed to a constellation of unique properties, including their high surface area, robust chemical stability, and remarkable electrical conductivity, among others, which allow them to be a good candidate for detecting CO, CO2, H2, NH3, NO2, CH4, H2S, and volatile organic compound gases. In recent years, the Sol-Gel method has emerged as a powerful and versatile technique for the controlled synthesis of low-dimensional metal oxide materials with diverse morphologies tailored for gas sensing applications. This review delves into the manifold facets of the Sol-Gel processing of metal oxides and reports their derived morphologies and remarkable gas-sensing properties. We comprehensively examine the synthesis conditions and critical parameters governing the formation of distinct morphologies, including nanoparticles, nanowires, nanorods, and hierarchical nanostructures. Furthermore, we provide insights into the fundamental principles underpinning the gas-sensing mechanisms of these materials. Notably, we assess the influence of morphology on gas-sensing performance, highlighting the pivotal role it plays in achieving exceptional sensitivity, selectivity, and response kinetics. Additionally, we highlight the impact of doping and composite formation on improving the sensitivity of pure metal oxides and reducing their operation temperature. A discussion of recent advances and emerging trends in the field is also presented, shedding light on the potential of Sol-Gel-derived nanostructures to revolutionize the landscape of gas sensing technologies.

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments

Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimizing the morphology and doping of printed structures, we achieve a record-high and stable response of 15.23% for 1 part per million formaldehyde and an ultralow detection limit of 8.02 parts per billion consuming only ∼130-microwatt power. On the basis of measured dynamic response snapshots, we also develop intelligent computational algorithms for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room temperature sensors. Our framework in combining materials engineering, structural design, and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature.

The synthesis and application of crystalline-amorphous hybrid materials

Crystalline-amorphous hybrid materials (CA-HMs) possess the merits of both pure crystalline and amorphous phases. Abundant dangling bonds, unsaturated coordination atoms, and isotropic structural features in the amorphous phase, as well as relatively high electronic conductivity and thermodynamic structural stability of the crystalline phase simultaneously take effect in CA-HMs. Furthermore, the atomic and bandgap mismatch at the CA-HM interface can introduce more defects as extra active sites, reservoirs for promoted catalytic and electrochemical performance, and induce built-in electric field for facile charge carrier transport. Motivated by these intriguing features, herein, we provide a comprehensive overview of CA-HMs on various aspects-from synthetic methods to multiple applications. Typical characteristics of CA-HMs are discussed at the beginning, followed by representative synthetic strategies of CA-HMs, including hydrothermal/solvothermal methods, deposition techniques, thermal adjustment, and templating methods. Diverse applications of CA-HMs, such as electrocatalysis, batteries, supercapacitors, mechanics, optoelectronics, and thermoelectrics along with underlying structure-property mechanisms are carefully elucidated. Finally, challenges and perspectives of CA-HMs are proposed with an aim to provide insights into the future development of CA-HMs.

Electrostatically Doped Junctionless Graphene Nanoribbon Tunnel Field-Effect Transistor for High-Performance Gas Sensing Applications: Leveraging Doping Gates for Multi-Gas Detection

In this paper, a new junctionless graphene nanoribbon tunnel field-effect transistor (JLGNR TFET) is proposed as a multi-gas nanosensor. The nanosensor has been computationally assessed using a quantum simulation based on the self-consistent solutions of the mode space non-equilibrium Green's function (NEGF) formalism coupled with the Poisson's equation considering ballistic transport conditions. The proposed multi-gas nanosensor is endowed with two top gates ensuring both reservoirs' doping and multi-gas sensing. The investigations have included the IDS-VGS transfer characteristics, the gas-induced electrostatic modulations, subthreshold swing, and sensitivity. The order of change in drain current has been considered as a sensitivity metric. The underlying physics of the proposed JLGNR TFET-based multi-gas nanosensor has also been studied through the analysis of the band diagrams behavior and the energy-position-resolved current spectrum. It has been found that the gas-induced work function modulation of the source (drain) gate affects the n-type (p-type) conduction branch by modulating the band-to-band tunneling (BTBT) while the p-type (n-type) conduction branch still unaffected forming a kind of high selectivity from operating regime point of view. The high sensitivity has been recorded in subthermionic subthreshold swing (SS < 60 mV/dec) regime considering small gas-induced gate work function modulation. In addition, advanced simulations have been performed for the detection of two different types of gases separately and simultaneously, where high-performance has been recorded in terms of sensitivity, selectivity, and electrical behavior. The proposed detection approach, which is viable, innovative, simple, and efficient, can be applied using other types of junctionless tunneling field-effect transistors with emerging channel nanomaterials such as the transition metal dichalcogenides materials. The proposed JLGNRTFET-based multi-gas nanosensor is not limited to two specific gases but can also detect other gases by employing appropriate gate materials in terms of selectivity.

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.

Lead-Free Perovskite Thin Films for Gas Sensing through Surface Acoustic Wave Device Detection

Thin film technology shows great promise in fabricating electronic devices such as gas sensors. Here, we report the fabrication of surface acoustic wave (SAW) sensors based on thin films of (1 - x) Ba(Ti0.8Zr0.2)O3-x(Ba0.7Ca0.3)TiO3 (BCTZ50, x = 50) and Polyethylenimine (PEI). The layers were deposited by two laser-based techniques, namely pulsed laser deposition (PLD) for the lead-free material and matrix assisted pulsed laser evaporation (MAPLE) for the sensitive polymer. In order to assay the impact of the thickness, the number of laser pulses was varied, leading to thicknesses between 50 and 350 nm. The influence of BCTZ film's crystallographic features on the characteristics and performance of the SAW device was studied by employing substrates with different crystal structures, more precisely cubic Strontium Titanate (SrTiO3) and orthorhombic Gadolinium Scandium Oxide (GdScO3). The SAW sensors were further integrated into a testing system to evaluate the response of the BCTZ thin films with PEI, and then subjected to tests for N2, CO2 and O2 gases. The influence of the MAPLE's deposited PEI layer on the overall performance was demonstrated. For the SAW sensors based on BCTZ/GdScO3 thin films with a PEI polymer, a maximum frequency shift of 39.5 kHz has been obtained for CO2; eight times higher compared to the sensor without the polymeric layer.

Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry

Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Nanoporous MoS2 Field-Effect Transistor Based Artificial Olfaction: Achieving Enhanced Volatile Organic Compound Detection Inspired by the Drosophila Olfactory System

Olfaction, a primal and effective sense, profoundly impacts our emotions and instincts. This sensory system plays a crucial role in detecting volatile organic compounds (VOCs) and realizing the chemical environment. Animals possess superior olfactory systems compared to humans. Thus, taking inspiration from nature, artificial olfaction aims to achieve a similar level of excellence in VOC detection. In this study, we present the development of an artificial olfaction sensor utilizing a nanostructured bio-field-effect transistor (bio-FET) based on transition metal dichalcogenides and the Drosophila odor-binding protein LUSH. To create an effective sensing platform, we prepared a hexagonal nanoporous structure of molybdenum disulfide (MoS2) using block copolymer lithography and selective etching techniques. This structure provides plenty of active sites for the integration of the LUSH protein, enabling enhanced binding with ethanol (EtOH) for detection purposes. The coupling of the biomolecule with EtOH influences the bio-FETs potential, which generates indicative electrical signals. By mimicking the sniffing techniques observed in Drosophila, these bio-FETs exhibit an impressive limit of detection of 10-6% for EtOH, with high selectivity, sensitivity, and detection ability even in realistic environments. This bioelectric sensor demonstrates substantial potential in the field of artificial olfaction, offering advancements in VOC detection.

Analytical Gas Sensing in the Terahertz Spectral Range

Exploiting the terahertz (THz) part of the electromagnetic spectrum is attracting attention in various scientific and applied disciplines worldwide. THz technology has also revealed its potential as an effective tool for gas analysis in astronomy, biomedicine and chemical analysis. Recently, it has also become important in environmental applications for monitoring hazardous and toxic gases in the atmosphere. This paper gives an overview of THz gas detection analytical methods for environmental and biomedical applications, starting with a brief introduction to THz technology and an explanation of the interaction of THz radiation with gaseous species and the atmosphere. The review focuses on several gaseous species and groups of air pollutants that have been or can be analysed by THz spectrometry. The review concludes that different but complementary THz detection methods allow unique detection, identification and quantification of gaseous and particulate air pollutants with high selectivity, specificity and sensitivity. THz detection methods also allow further technological improvements and open new application possibilities.

Resonant Gas Sensing in the Terahertz Spectral Range Using Two-Wire Phase-Shifted Waveguide Bragg Gratings

The development of low-cost sensing devices with high compactness, flexibility, and robustness is of significance for practical applications of optical gas sensing. In this work, we propose a waveguide-based resonant gas sensor operating in the terahertz frequency band. It features micro-encapsulated two-wire plasmonic waveguides and a phase-shifted waveguide Bragg grating (WBG). The modular semi-sealed structure ensures the controllable and efficient interaction between terahertz radiation and gaseous analytes of small quantities. WBG built by superimposing periodical features on one wire shows high reflection and a low transmission coefficient within the grating stopband. Phase-shifted grating is developed by inserting a Fabry-Perot cavity in the form of a straight waveguide section inside the uniform gratings. Its spectral response is optimized for sensing by tailoring the cavity length and the number of grating periods. Gas sensor operating around 140 GHz, featuring a sensitivity of 144 GHz/RIU to the variation in the gas refractive index, with resolution of 7 × 10-5 RIU, is developed. In proof-of-concept experiments, gas sensing was demonstrated by monitoring the real-time spectral response of the phase-shifted grating to glycerol vapor flowing through its sealed cavity. We believe that the phase-shifted grating-based terahertz resonant gas sensor can open new opportunities in the monitoring of gaseous analytes.

Response Surface Modeling of the Steady-State Impedance Responses of Gas Sensor Arrays Comprising Functionalized Carbon Nanotubes to Detect Ozone and Nitrogen Dioxide

Carbon nanotube (CNT) sensors provide a versatile chemical platform for ambient monitoring of ozone (O3) and nitrogen dioxide (NO2), two important airborne pollutants known to cause acute respiratory and cardiovascular health problems. CNTs have shown great potential for use as sensing layers due to their unique properties, including high surface to volume ratio, numerous active sites and crystal facets with high surface reactivity, and high thermal and electrical conductivity. With operational advantages such as compactness, low-power operation, and easy integration with electronics devices, nanotechnology is expected to have a significant impact on portable low-cost environmental sensors. Enhanced sensitivity is feasible by functionalizing the CNTs with polymers, metals, and metal oxides. This paper focuses on the design and performance of a two-element array of O3 and NO2 sensors comprising single-walled CNTs functionalized by covalent modification with organic functional groups. Unlike the conventional chemiresistor in which the change in DC resistance across the sensor terminals is measured, we characterize the sensor array response by measuring both the magnitude and phase of the AC impedance. Multivariate response provides higher degrees of freedom in sensor array data processing. The complex impedance of each sensor is measured at 5 kHz in a controlled gas-flow chamber using gas mixtures with O3 in the 60-120 ppb range and NO2 between 20 and 80 ppb. The measured data reveal response change in the 26-36% range for the O3 sensor and 5-31% for the NO2 sensor. Multivariate optimization is used to fit the laboratory measurements to a response surface mathematical model, from which sensitivity and selectivity are calculated. The ozone sensor exhibits high sensitivity (e.g., 5 to 6 MΩ/ppb for the impedance magnitude) and high selectivity (0.8 to 0.9) for interferent (NO2) levels below 30 ppb. However, the NO2 sensor is not selective.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Preferred surface orientation for CO oxidation on SnO2 surfaces

In the present study, we perform a comparative study on the oxidation mechanism of CO gas molecules on SnO2 (110), (101), and (100) surfaces. The optimized adsorption configurations show that the adsorption of CO molecules could occur similarly on the three SnO2 surfaces via two adsorption modes, physisorption of CO on the Sn5c site that is considered as the first step for CO oxidation, followed by CO chemisorption on the O2c site resulting in the formation of CO2 species. Based on the calculated adsorption energies and CO molecule diffusion on SnO2 surfaces, CO molecule adsorption on the (101) surface exhibits the highest adsorption energy and the lowest reaction barrier for CO oxidation compared to the widely considered (110) surface or the (100) surface. These findings are expected to have a major impact on improving sensing properties toward toxic gas by means of surface-orientation engineering.

The influence of advanced materials on the analytical performance of semiconductor-based gas sensors

Chemiresistive gas sensors are metal oxide-based sensors that have received significant attention in different fields. Ambient gas sensors are especially important in the fabrication of wearable probes for the real-time detection of biomarkers in human body samples. Usually, room temperature sensors are affordable due to their low power consumption, resulting in simple instrumentation and maintenance. To fabricate versatile gas sensors, i.e. sensitive, selective, ambient temperature operating gas sensors, and improve the sensing performance of the traditionally used sensor, new materials play an important role. In other words, new advanced materials are essential for designing and fabricating new gas sensors. Hence, in this review, the application and impact of new advanced materials in the fabrication of reliable gas sensors are discussed in detail. Special emphasis is given to the effect of new materials in the fabrication of room-temperature operating systems. Finally, future research outlook and possible challenges that may be encountered by reliable gas sensors are also explained.

Microfluidic integrated gas sensors for smart analyte detection: a comprehensive review

The utilization of gas sensors has the potential to enhance worker safety, mitigate environmental issues, and enable early diagnosis of chronic diseases. However, traditional sensors designed for such applications are often bulky, expensive, difficult to operate, and require large sample volumes. By employing microfluidic technology to miniaturize gas sensors, we can address these challenges and usher in a new era of gas sensors suitable for point-of-care and point-of-use applications. In this review paper, we systematically categorize microfluidic gas sensors according to their applications in safety, biomedical, and environmental contexts. Furthermore, we delve into the integration of various types of gas sensors, such as optical, chemical, and physical sensors, within microfluidic platforms, highlighting the resultant enhancements in performance within these domains.

Metasurface-Enhanced Infrared Spectroscopy: An Abundance of Materials and Functionalities

Infrared spectroscopy provides unique information on the composition and dynamics of biochemical systems by resolving the characteristic absorption fingerprints of their constituent molecules. Based on this inherent chemical specificity and the capability for label-free, noninvasive, and real-time detection, infrared spectroscopy approaches have unlocked a plethora of breakthrough applications for fields ranging from environmental monitoring and defense to chemical analysis and medical diagnostics. Nanophotonics has played a crucial role for pushing the sensitivity limits of traditional far-field spectroscopy by using resonant nanostructures to focus the incident light into nanoscale hot-spots of the electromagnetic field, greatly enhancing light-matter interaction. Metasurfaces composed of regular arrangements of such resonators further increase the design space for tailoring this nanoscale light control both spectrally and spatially, which has established them as an invaluable toolkit for surface-enhanced spectroscopy. Starting from the fundamental concepts of metasurface-enhanced infrared spectroscopy, a broad palette of resonator geometries, materials, and arrangements for realizing highly sensitive metadevices is showcased, with a special focus on emerging systems such as phononic and 2D van der Waals materials, and integration with waveguides for lab-on-a-chip devices. Furthermore, advanced sensor functionalities of metasurface-based infrared spectroscopy, including multiresonance, tunability, dielectrophoresis, live cell sensing, and machine-learning-aided analysis are highlighted.

Metal-Phenolic Film Coated Quartz Crystal Microbalance as a Selective Sensor for Methanol Detection in Alcoholic Beverages

The facile real-time monitoring of methyl content in fermented beverages is of fundamental significance in the alcohol and restaurant industry, since as little as 4 mL of methanol entering the blood may cause intoxication or blindness. So far, the practical applicability of available methanol sensors, including the piezoresonance analogs, is somewhat limited to laboratory use due to the complexity and bulkiness of the measuring equipment involving multistep procedures. This article introduces a hydrophobic metal-phenolic film-coated quartz crystal microbalance (MPF-QCM) as a novel streamlined detector of methanol in alcoholic drinks. Unlike other QCM-based alcohol sensors, our device operates under saturated vapor pressure conditions, permitting rapid detection of methyl fractions up to seven times below the tolerable levels in spirits (e.g., whisky) while effectively suppressing the cross-sensitivity to interfering chemical compounds such as water, petroleum ether or ammonium hydroxide. Furthermore, the good surface adhesion of metal-phenolic complexes endows the MPF-QCM with superior long-term stability, contributing to the repeatable and reversible physical sorption of the target analytes. These features, combined with the lack of mass flow controllers, valves and connecting pipes delivering the gas mixture, outline the likelihood for future design of a portable MPF-QCM prototype suitable to point-of-use analysis in drinking establishments.

Volatile organic compound sensing in breath using conducting polymer coated chemi-resistive filter paper sensors

In this work, a disposable sensor array was designed based on the chemi-resistive behavior of the conducting polymers to detect three volatile organic compounds (VOCs), i.e., acetone, ethanol, and methanol in air and breath. Four disposable resistive sensors were designed by coating polypyrrole and polyaniline (in their doped and de-doped forms) on filter paper substrates and tested against VOCs in air. Change in conductivity of the polymer resulting from exposure to various VOC concentration was measured as percentage resistance change using a standard multimeter. The lowest concentration detected for acetone, ethanol, and methanol vapors was 400 ppb, 150 ppb, and 300 ppb, respectively within 2 min. These VOC-responsive sensors, housed in an indigenous inert chamber, showed good stability, repeatability, and reversibility while sensing, thus making it suitable for environmental pollutant detection at room temperature. Furthermore, the non-specific nature of these easy to fabricate sensors towards all VOCs is considered favorable and upon classifying with principal component analysis (PCA), the gases were qualitatively distinguished in separate clusters. These developed sensors were also tested and analyzed using VOC spiked real breath samples as proof of concept.

Assessment of vibrational-translational relaxation dynamics of in a wet-nitrogen matrix through QEPAS

Here we report on a study of the non-radiative relaxation dynamic of 12CH4 and 13CH4 in wet nitrogen-based matrixes by using the quartz-enhanced photoacoustic spectroscopy (QEPAS) technique. The dependence of the QEPAS signal on pressure at fixed matrix composition and on H2O concentration at fixed pressure was investigated. We demonstrated that QEPAS measurements can be used to retrieve both the effective relaxation rate in the matrix, and the V-T relaxation rate associated to collisions with nitrogen and water vapor. No significant differences in measured relaxation rates were observed between the two isotopologues.

Improving Hazardous Gas Detection Behavior with Palladium Decorated SnO2 Nanobelts Networks

Transparent Conductive Oxides (TCOs) have been widely used as sensors for various hazardous gases. Among the most studied TCOs is SnO₂, due to tin being an abundant material in nature, and therefore being accessible for moldable-like nanobelts. Sensors based on SnO₂ nanobelts are generally quantified according to the interaction of the atmosphere with its surface, changing its conductance. The present study reports on the fabrication of a nanobelt-based SnO₂ gas sensor, in which electrical contacts to nanobelts are self-assembled, and thus the sensors do not need any expensive and complicated fabrication processes. The nanobelts were grown using the vapor-solid-liquid (VLS) growth mechanism with gold as the catalytic site. The electrical contacts were defined using testing probes, thus the device is considered ready after the growth process. The sensorial characteristics of the devices were tested for the detection of CO and CO₂ gases at temperatures from 25 to 75 °C, with and without palladium nanoparticle deposition in a wide concentration range of 40-1360 ppm. The results showed an improvement in the relative response, response time, and recovery, both with increasing temperature and with surface decoration using Pd nanoparticles. These features make this class of sensors important candidates for CO and CO₂ detection for human health.

In-vehicle wireless driver breath alcohol detection system using a microheater integrated gas sensor based on Sn-doped CuO nanostructures

In this paper, we have developed an in-vehicle wireless driver breath alcohol detection (IDBAD) system based on Sn-doped CuO nanostructures. When the proposed system detects the ethanol trace in the driver`s exhaled breath, it can alarm and then prevents the car to be started and also sends the location of the car to the mobile phone. The sensor used in this system is a two-sided micro-heater integrated resistive ethanol gas sensor fabricated based on Sn-doped CuO nanostructures. Pristine and Sn-doped CuO nanostructures were synthesized as the sensing materials. The micro-heater is calibrated to provide the desired temperature by applying voltage. The results showed that by Sn-doping in CuO nanostructures, the sensor performance can be significantly improved. The proposed gas sensor has a fast response, good repeatability along with good selectivity that makes it suitable for being used in practical applications such as the proposed system.

Moisture-enhanced trace chloroalkanes detection in bimetallic metal-organic frameworks 3-dimensional photonic crystal

Chloroalkanes have long been a threat to environmental protection and human health, however, rapid and efficient detection of chloroalkanes remains challenging. Herein, 3-dimensional photonics crystals (3-D PCs) based on bimetallic materials of institute lavoisier frameworks-127 (MIL-127, Fe₂M, M = Fe, Ni, Co, Zn) demonstrate the great potential of chloroalkanes sensing. Particularly, at temperature of 25 °C and dry conditions, the 3-D PC consisting of MIL-127 (Fe₂Co) shows optimal selectivity and high concentration sensitivity of 0.0351 ± 0.00007 nm ppm^-1 to carbon tetra-chloride (CCl₄), and the limit of detection (LOD) can reach 2.85 ± 0.01 ppm. Meanwhile, MIL-127 (Fe₂Co) 3-D PC sensor presents a rapid response of 1 s and recovery time of 4.5 s for CCl₄ vapor, and can maintain excellent sensing performance under heat-treatment of 200 °C or in the long-term storage (30 days). Mechanism studies indicated that the excellent sensing property derived from the doping of transition metals. Moreover, the moisture-enhanced adsorption of CCl₄ for the MIL-127 (Fe₂Co) 3-D PC sensor is also observed. H₂O molecule can remarkably enhance the adsorption of MIL-127 (Fe₂Co) to CCl₄. The MIL-127 (Fe₂Co) 3-D PC sensor shows the highest concentration sensitivity of 0.146 ± 0.00082 nm ppm^-1 to CCl₄ and the lowest limit of detection (LOD) of 685 ± 4 ppb under the pre-adsorption of 75 ppm H₂O. Our results provide an insight for a trace gas detection using metal-organic frameworks (MOFs) in the optical sensing field.

TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra

The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220-330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

A review of piezoelectric MEMS sensors and actuators for gas detection application

Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors' characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal-organic framework, and graphene.

Innovative Photonic Sensors for Safety and Security, Part I: Fundamentals, Infrastructural and Ground Transportations

Our group, involving researchers from different universities in Campania, Italy, has been working for the last twenty years in the field of photonic sensors for safety and security in healthcare, industrial and environment applications. This is the first in a series of three companion papers. In this paper, we introduce the main concepts of the technologies employed for the realization of our photonic sensors. Then, we review our main results concerning the innovative applications for infrastructural and transportation monitoring.