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

Recent advances in medical gas sensing with artificial intelligence-enabled technology

Recent advancements in artificial intelligence-enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency in healthcare. Medical gases, including oxygen, nitrous oxide, and carbon dioxide, are essential for various treatments but pose health risks if improperly managed. This review highlights the integration of artificial intelligence in medical gas sensing, enhancing traditional sensors through advanced data processing, pattern recognition, and real-time monitoring capabilities. Artificial intelligence improves the ability to detect harmful gas levels, enabling immediate intervention to prevent adverse health effects. Moreover, developments in nanotechnology have resulted in advanced materials, such as metal oxides and carbon-based nanomaterials, which increase sensitivity and selectivity. These innovations, combined with artificial intelligence, support continuous patient monitoring and predictive diagnostics, paving the way for future breakthroughs in medical care.

Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms

The increasing demand for gas molecule detection emphasizes the need for portable sensor devices possessing selectivity, a low limit of detection (LOD), and a large dynamic range. Despite substantial progress in developing nanostructured sensor materials with heightened sensitivity, achieving sufficient selectivity remains a challenge. Here, we introduce a strategy to enhance the performance of chemiresistive gas sensors by combining an advanced sensor design with machine learning (ML). Our sensor architecture consists of a tungsten oxide (WO3) nanoparticle network, as the primary sensing layer, with an integrated zeolitic imidazolate framework (ZIF-8) membrane layer, used to induce a gas-specific delay to the diffusion of analytes, sharing conceptual similarities to gas chromatography. However, the miniaturized design and chemical activity of the ZIF-8 results in a nontrivial impact of the ZIF-8 membrane on the target analyte diffusivity and sensor response. An ML method was developed to evaluate the response dynamics with a panel of relevant analytes including acetone, ethanol, propane, and ethylbenzene. Our advanced sensor design and ML algorithm led to an excellent capability to determine the gas molecule type and its concentration, achieving accuracies of 97.22 and 86.11%, respectively, using a virtual array of 4 sensors. The proposed ML method can also reduce the necessary sensing time to only 5 s while maintaining an accuracy of 70.83%. When compared with other ML methods in the literature, our approach also gave superior performance in terms of sensitivity, specificity, precision, and F1-score. These findings show a promising approach to overcome a longstanding challenge of the highly miniaturized but poorly selective semiconductor sensor technology, with impact ranging from environmental monitoring to explosive detection and health care.

Highly Sensitive Surface Acoustic Wave Sensors for Ammonia Gas Detection at Room Temperature Using Gold Nanoparticles-Cuprous Oxide/Reduced Graphene Oxide/Polypyrrole Hybrid Nanocomposite Film

Gold nanoparticles-cuprous oxide/reduced graphene oxide/polypyrrole (AuNPs-Cu2O/rGO/PPy) hybrid nanocomposites were synthesized for surface acoustic wave (SAW) sensors, achieving high sensitivity (2 Hz/ppb), selectivity, and fast response (~2 min) at room temperature. The films, deposited via spin-coating, were characterized by SEM, EDS, and XRD, revealing a rough, wrinkled morphology beneficial for gas adsorption. The sensor showed significant frequency shifts to NH3, enhanced by AuNPs, Cu2O, rGO, and PPy. It had a 6.4-fold stronger response to NH3 compared to CO2, H2, and CO, confirming excellent selectivity. The linear detection range was 12-1000 ppb, with a limit of detection (LOD) of 8 ppb. Humidity affected performance, causing negative frequency shifts, and sensitivity declined after 30 days due to resistivity changes. Despite this, the sensor demonstrated excellent NH3 selectivity and stability across multiple cycles. In simulated breath tests, it distinguished between healthy and patient-like samples, highlighting its potential as a reliable, non-invasive diagnostic tool.

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.

Innovations in Air Quality Monitoring: Sensors, IoT and Future Research

Recently, Air Quality Monitoring (AQM) has gained significant R&D attention from academia and industries, leading to advanced sensor-enabled IoT solutions. Literature highlights the use of nanomaterials in sensor design, emphasising miniaturisation, enhanced calibration, and low voltage, room-temperature operation. Significant efforts are aimed at improving sensitivity, selectivity, and stability, while addressing challenges like high power consumption and drift. The integration of sensors with IoT technology is driving the development of accurate, scalable, and real-time AQM systems. This paper provides technical insights into recent AQM advancements, focusing on air pollutants, sensor technologies, IoT frameworks, performance evaluation, and future research directions. It presents a detailed analysis of air quality composition and potential air pollutants. Relevant sensors are examined in terms of design, materials and methodologies for pollutant monitoring. A critical review of IoT frameworks for AQM is conducted, highlighting their strengths and weaknesses. As a technical contribution, an experimental performance evaluation of three commercially available AQM systems in the UK is discussed, with a comparative and critical analysis of the results. Lastly, future research directions are also explored with a focus on AQM sensor design and IoT framework development.

Nanomaterials-Enabled Sensors for Detecting and Monitoring Chemical Warfare Agents

Despite their restrictions under international treaties, many chemical warfare agents (CWAs) and their toxic analogues are still used in various industrial sectors such as agriculture and chemical manufacturing. Thus, the need for sensitive and selective CWA detection remains critical. Commercially available detection methods, while accurate, are often bulky, expensive, and require specialized personnel. Sensors incorporating nanomaterials present a promising alternative, offering rapid, portable, and cost-effective detection due to their unique properties, such as high surface area and tunable reactivity. This review covers the four main CWA categories: nerve agents, blister agents, blood agents, and choking agents, highlighting recent progress in nanosensor development for each category. It discusses various sensing mechanisms employed, including fluorescence, colorimetry, chemiresistivity, electrochemistry, and Raman spectroscopy. Despite these advancements, challenges remain, particularly regarding the scalability, stability, and selectivity of nanomaterials-based sensors in complex environments. The review concludes by emphasizing the need to address these challenges and explore novel nanomaterials, the development of scalable nanomanufacturing techniques, and the integration of artificial intelligence to fully unlock the potential of nanomaterials in CWA sensing for homeland security and personal safety.

Design Principles From Natural Olfaction for Electronic Noses

Natural olfactory systems possess remarkable sensitivity and precision beyond what is currently achievable by engineered gas sensors. Unlike their artificial counterparts, noses are capable of distinguishing scents associated with mixtures of volatile molecules in complex, typically fluctuating environments and can adapt to changes. This perspective examines the multifaceted biological principles that provide olfactory systems their discriminatory prowess, and how these ideas can be ported to the design of electronic noses for substantial improvements in performance across metrics such as sensitivity and ability to speciate chemical mixtures. The topics examined herein include the fluid dynamics of odorants in natural channels; specificity and kinetics of odorant interactions with olfactory receptors and mucus linings; complex signal processing that spatiotemporally encodes physicochemical properties of odorants; active sampling techniques, like biological sniffing and nose repositioning; biological priming; and molecular chaperoning. Each of these components of natural olfactory systems are systmatically investigated, as to how they have been or can be applied to electronic noses. While not all artificial sensors can employ these strategies simultaneously, integrating a subset of bioinspired principles can address issues like sensitivity, drift, and poor selectivity, offering advancements in many sectors such as environmental monitoring, industrial safety, and disease diagnostics.

Ultrafast Detection of ppb-Level NH3 Gas at Room Temperature Using CuO Nanoparticles Decorated AlN-Based Surface Acoustic Wave Sensor

Rational design of heterostructure (HS)-based surface acoustic wave (SAW) smart gas sensors for efficient and accurate subppm level ammonia (NH3) detection at room temperature (RT) is of great significance in environmental protection and human safety. This study introduced a novel HS composed of an AlN-based SAW resonator and CuO nanoparticles (NPs) as a chemical interface for NH3 detection at RT (∼26 °C). The structural, morphological, and chemical compositions were detailly investigated, which demonstrates that the CuO/AlN HS was successfully formed via interfacial modulation. The CuO/AlN HS SAW sensor exhibited a significant positive frequency shift of 52.60 kHz in response to 100 ppm of NH3, which is 4.8 times higher than that of the as-grown AlN SAW sensor. Additionally, the CuO/AlN HS SAW sensor exhibited ultrafast response/recovery times of 5/25 s, a remarkably low limit of detection (LOD) of 24 ppb, and excellent long-term stability and selectivity. These results are attributed to the high porosity and defect sites of CuO NPs, which enhanced charge transfer at the heterointerface, as well as decreased mass loading and conductivity effects. The CuO/AlN HS SAW sensor also demonstrated distinct frequency responses to 100 ppm of NH3, under varying relative humidity (RH): a positive shift at low RH (5%-10%) due to increased conductivity, and a negative shift at high RH (20%-80%) due to enhanced mass loading. These NH3 gas sensing characteristics of the CuO/AlN HS SAW sensor were validated through X-ray photoelectron spectroscopy band diagram analysis and resistive-type gas sensing measurements. These findings highlight the potential of the integrating metal oxide with nitride semiconductors for advanced SAW-based gas sensing technology in environmental and industrial applications.

Tandem Imaging of Breath Ethanol and Acetaldehyde Based on Multiwavelength Enzymatic Biofluorometry

Highly sensitive and selective imaging of human-borne volatile organic compounds (VOCs) enables an intuitive understanding of their concentrations and release sites. While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. In this study, we achieved simultaneous imaging of ethanol (EtOH) and acetaldehyde (AcH), the starting molecule and an intermediate metabolite of alcohol metabolism, using a multiwavelength VOC imaging system. The system employed alcohol dehydrogenase-catalyzed substrate oxidation (ADHOX) and reduction (ADHRD) reactions. The oxidation of EtOH by ADHOX in the presence of NAD+ produced NADH, which was subsequently oxidized by diaphorase (DP) with resazurin, leading to the resorufin formation, characterized by red fluorescence (excitation at 560 nm and fluorescence at 590 nm). Reduction of AcH by ADHRD consumed NADH, leading to a decrease in blue fluorescence (ex. 340 nm, fl. 490 nm). Meshes incorporating ADHOX-DP or ADHRD were arranged in tandem in front of a camera. Fluorescence images were captured, while a mixture of gaseous EtOH and AcH was applied by switching between two bandpass filters at 1 Hz. Each mesh exhibited selective responses to the target VOCs, with no significant impact on the dynamic range observed in either the single or tandem configurations (EtOH 1-300 ppm, AcH 0.2-5 ppm). The 90% response time was close after time-domain image differential analysis (EtOH = 26 s and AcH = 15 s). Furthermore, the system enabled simultaneous and quantitative imaging of EtOH and AcH concentrations in the breath after alcohol consumption. It also distinguished differences in alcohol metabolism based on the alcohol dehydrogenase 2 (ALDH2) activity, as indicated by the EtOH/AcH ratio (ALDH2 active vs nonactive: 120.9/0.71 ppm vs 129.2/1.99 ppm).

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen's role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology.

Conductive Framework Materials for Chemiresistive Detection and Differentiation of Toxic Gases

ConspectusSensing complex gaseous mixtures and identifying their composition and concentration have the potential to achieve unprecedented improvements in environmental monitoring, medical diagnostics, industrial safety, and the food/agriculture industry. Electronically transduced chemical sensors capable of recognizing and differentiating specific target gases and transducing these chemical stimuli in a portable electronic device offer an opportunity for impact by bridging the utility of chemical information with global wireless connectivity. Among electronically transduced chemical sensors, chemiresistors stand out as particularly promising due to combined features of low-power requirements, room temperature operation, non-line-of-sight detection, high portability, and exceptional modularity. Relying on changes in resistance of a functional material triggered by variations in the surrounding chemical environment, these devices have achieved part-per-billion sensitivities of analytes by employing conductive polymers, graphene, carbon nanotubes (CNTs), metal oxides, metal nanoparticles, metal dichalcogenides, or MXenes as sensing materials. Despite these tremendous developments, the need for stable, selective, and sensitive chemiresistors demands continued innovation in material design in order to operate in complex mixtures with interferents as well as variations in humidity and temperature.To fill existing gaps in sensing capabilities, conductive metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have recently emerged as a promising class of materials for chemiresistive sensing. In contrast to previously reported chemiresistors, these materials offer at least three unique features for gas sensing applications: (i) bottom-up synthesis from molecularly precise precursors that allows for strategic control of material-analyte interactions, (ii) intrinsic conductivity that simultaneously facilitates charge transport and signal transduction under low power requirements, and (iii) high surface area that enables the accessibility of abundant active sites and decontamination of gas streams by coordinating to and, sometimes, detoxifying harmful analytes. Through an emphasis on molecular engineering of structure-property relationships in conductive MOFs and COFs, combined with strategic innovations in device integration strategies and device form factor (i.e., the physical dimensions and design of device components), our group has paved the way to demonstrating the multifunctional utility of these materials in the chemiresistive detection of gases and vapors. Backed by spectroscopic assessment of material-analyte interactions, we illustrated how molecular-level features lead to device performance in detection, filtration, and detoxification of gaseous analytes. By merging the bottom-up synthesis of these materials with device integration, we show the versatility and scalability of using these materials in low-power electronic sensing devices. Taken together, our achievements, combined with the progress spearheaded on this class of materials by other researchers, establish conductive MOFs and COFs as promising multifunctional materials for applications in electronically transduced, portable, low-power sensing devices.

Low-Temperature NO2 Gas-Sensing System Based on Metal-Organic Framework-Derived In2O3 Structures and Advanced Machine Learning Techniques

In the bustling metropolis of tomorrow, where pollution levels are a constant concern, a team of innovative researchers embarked on a quest to revolutionize air quality monitoring. In pursuit of this objective, this study embarked on the synthesis of indium oxide materials via a straightforward solvothermal method purposely for NO2 detection. Through meticulous analysis of their gas-sensing capabilities, a remarkable discovery came to light. Among the materials tested, In2O3 (IO-2) exhibited exceptional sensitivity toward 100 ppm of NO2 gas at an optimal working temperature of 150 °C and even at room temperature (RT). The response value reached an impressive 12.69, showcasing the material's outstanding capability to detect NO2 gas even at 100 ppb. Further investigation revealed a significant linear relationship (R2 = 0.89454) and commendable reproducibility, highlighting IO-2's potential as a reliable and stable sensing material. Moreover, machine learning techniques were utilized to predict the response characteristics of the sensing materials to various environmental conditions, concentrations of target gases, and operational parameters. This predictive capability can guide the design of more efficient and robust gas sensors, ultimately contributing to improved safety and environmental monitoring. As the demand for efficient, portable, and eco-friendly electronics continues to grow, these findings contribute to the development of sustainable and high-performance materials that can meet the needs of modern technology.

A smart mask for exhaled breath condensate harvesting and analysis

Recent respiratory outbreaks have garnered substantial attention, yet most respiratory monitoring remains confined to physical signals. Exhaled breath condensate (EBC) harbors rich molecular information that could unveil diverse insights into an individual's health. Unfortunately, challenges related to sample collection and the lack of on-site analytical tools impede the widespread adoption of EBC analysis. Here, we introduce EBCare, a mask-based device for real-time in situ monitoring of EBC biomarkers. Using a tandem cooling strategy, automated microfluidics, highly selective electrochemical biosensors, and a wireless reading circuit, EBCare enables continuous multimodal monitoring of EBC analytes across real-life indoor and outdoor activities. We validated EBCare's usability in assessing metabolic conditions and respiratory airway inflammation in healthy participants, patients with chronic obstructive pulmonary disease or asthma, and patients after COVID-19 infection.

Multiple Probe Measurements at Uranus Motivated by Spatial Variability

A major motivation for multiple atmospheric probe measurements at Uranus is the understanding of dynamic processes that create and maintain spatial variation in thermal structure, composition, and horizontal winds. But origin questions-regarding the planet's formation and evolution, and conditions in the protoplanetary disk-are also major science drivers for multiprobe exploration. Spatial variation in thermal structure reveals how the atmosphere transports heat from the interior, and measuring compositional variability in the atmosphere is key to ultimately gaining an understanding of the bulk abundances of several heavy elements. We review the current knowledge of spatial variability in Uranus' atmosphere, and we outline how multiple probe exploration would advance our understanding of this variability. The other giant planets are discussed, both to connect multiprobe exploration of those atmospheres to open questions at Uranus, and to demonstrate how multiprobe exploration of Uranus itself is motivated by lessons learned about the spatial variation at Jupiter, Saturn, and Neptune. We outline the measurements of highest value from miniature secondary probes (which would complement more detailed investigation by a larger flagship probe), and present the path toward overcoming current challenges and uncertainties in areas including mission design, cost, trajectory, instrument maturity, power, and timeline.

Calibration of a Low-Cost Methane Sensor Using Machine Learning

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

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Disclosing Fast Detection Opportunities with Nanostructured Chemiresistor Gas Sensors Based on Metal Oxides, Carbon, and Transition Metal Dichalcogenides

With the emergence of novel sensing materials and the increasing opportunities to address safety and life quality priorities of our society, gas sensing is experiencing an outstanding growth. Among the characteristics required to assess performances, the overall speed of response and recovery is adding to the well-established stability, selectivity, and sensitivity features. In this review, we focus on fast detection with chemiresistor gas sensors, focusing on both response time and recovery time that characterize their dynamical response. We consider three classes of sensing materials operating in a chemiresistor architecture, exposed to the most investigated pollutants, such as NH3, NO2, H2S, H2, ethanol, and acetone. Among sensing materials, we first selected nanostructured metal oxides, which are by far the most used chemiresistors and can provide a solid ground for performance improvement. Then, we selected nanostructured carbon sensing layers (carbon nanotubes, graphene, and reduced graphene), which represent a promising class of materials that can operate at room temperature and offer many possibilities to increase their sensitivities via functionalization, decoration, or blending with other nanostructured materials. Finally, transition metal dichalcogenides are presented as an emerging class of chemiresistive layers that bring what has been learned from graphene into a quite large portfolio of chemo-sensing platforms. For each class, studies since 2019 reporting on chemiresistors that display less than 10 s either in the response or in the recovery time are listed. We show that for many sensing layers, the sum of both response and recovery times is already below 10 s, making them promising devices for fast measurements to detect, e.g., sudden bursts of dangerous emissions in the environment, or to track the integrity of packaging during food processing on conveyor belts at pace with industrial production timescales.

Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Recent Progress on Functionalized Graphene Quantum Dots and Their Nanocomposites for Enhanced Gas Sensing Applications

Gas-sensing technology has witnessed significant advancements that have been driven by the emergence of graphene quantum dots (GQDs) and their tailored nanocomposites. This comprehensive review surveys the recent progress made in the construction methods and applications of functionalized GQDs and GQD-based nanocomposites for gas sensing. The gas-sensing mechanisms, based on the Fermi-level control and charge carrier depletion layer theory, are briefly explained through the formation of heterojunctions and the adsorption/desorption principle. Furthermore, this review explores the enhancements achieved through the incorporation of GQDs into nanocomposites with diverse matrices, including polymers, metal oxides, and 2D materials. We also provide an overview of the key progress in various hazardous gas sensing applications using functionalized GQDs and GQD-based nanocomposites, focusing on key detection parameters such as sensitivity, selectivity, stability, response and recovery time, repeatability, and limit of detection (LOD). According to the most recent data, the normally reported values for the LOD of various toxic gases using GQD-based sensors are in the range of 1-10 ppm. Remarkably, some GQD-based sensors exhibit extremely low detection limits, such as N-GQDs/SnO2 (0.01 ppb for formaldehyde) and GQD@SnO2 (0.10 ppb for NO2). This review provides an up-to-date perspective on the evolving landscape of functionalized GQDs and their nanocomposites as pivotal components in the development of advanced gas sensors.

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.

Fabrication of a Highly NO2-Sensitive Gas Sensor Based on a Defective ZnO Nanofilm and Using Electron Beam Lithography

Hazardous substances produced by anthropic activities threaten human health and the green environment. Gas sensors, especially those based on metal oxides, are widely used to monitor toxic gases with low cost and efficient performance. In this study, electron beam lithography with two-step exposure was used to minimize the geometries of the gas sensor hotplate to a submicron size in order to reduce the power consumption, reaching 100 °C with 0.09 W. The sensing capabilities of the ZnO nanofilm against NO2 were optimized by introducing an enrichment of oxygen vacancies through N2 calcination at 650 °C. The presence of oxygen vacancies was proven using EDX and XPS. It was found that oxygen vacancies did not significantly change the crystallographic structure of ZnO, but they significantly improved the electrical conductivity and sensing behaviors of ZnO film toward 5 ppm of dry air.

Structure and Technological Parameters' Effect on MISFET-Based Hydrogen Sensors' Characteristics

The influence of structure and technological parameters (STPs) on the metrological characteristics of hydrogen sensors based on MISFETs has been investigated. Compact electrophysical and electrical models connecting the drain current, the voltage between the drain and the source and the voltage between the gate and the substrate with the technological parameters of the n-channel MISFET as a sensitive element of the hydrogen sensor are proposed in a general form. Unlike the majority of works, in which the hydrogen sensitivity of only the threshold voltage of the MISFET is investigated, the proposed models allow us to simulate the hydrogen sensitivity of gate voltages or drain currents in weak and strong inversion modes, taking into account changes in the MIS structure charges. A quantitative assessment of the effect of STPs on MISFET performances (conversion function, hydrogen sensitivity, gas concentration measurement errors, sensitivity threshold and operating range) is given for a MISFET with a Pd-Ta₂O₅-SiO₂-Si structure. In the calculations, the parameters of the models obtained on the basis of the previous experimental results were used. It was shown how STPs and their technological variations, taking into account the electrical parameters, can affect the characteristics of MISFET-based hydrogen sensors. It is noted, in particular, that for MISFET with submicron two-layer gate insulators, the key influencing parameters are their type and thickness. Proposed approaches and compact refined models can be used to predict performances of MISFET-based gas analysis devices and micro-systems.

V2CTX MXene-based hybrid sensor with high selectivity and ppb-level detection for acetone at room temperature

High-performance, room temperature-based novel sensing materials are one of the frontier research topics in the gas sensing field, and MXenes, a family of emerging 2D layered materials, has gained widespread attention due to their distinctive properties. In this work, we propose a chemiresistive gas sensor made from V₂CT_x MXene-derived, urchin-like V₂O₅ hybrid materials (V₂C/V₂O₅ MXene) for gas sensing applications at room temperature. The as-prepared sensor exhibited high performance when used as the sensing material for acetone detection at room temperature. Furthermore, the V₂C/V₂O₅ MXene-based sensor exhibited a higher response (S% = 11.9%) toward 15 ppm acetone than pristine multilayer V₂CT_x MXenes (S% = 4.6%). Additionally, the composite sensor demonstrated a low detection level at ppb levels (250 ppb) at room temperature, as well as high selectivity among different interfering gases, fast response-recovery time, good repeatability with minimal amplitude fluctuation, and excellent long-term stability. These improved sensing properties can be attributed to the possible formation of H-bonds in multilayer V₂C MXenes, the synergistic effect of the newly formed composite of urchin-like V₂C/V₂O₅ MXene sensor, and high charge carrier transport at the interface of V₂O₅ and V₂C MXene.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Nanocomposite Based on HA/PVTMS/Cl2FeH8O4 as a Gas and Temperature Sensor

In this paper, a novel nanocrystalline composite material of hydroxyapatite (HA)/polyvinyltrimethoxysilane (PVTMS)/iron(II)chloride tetrahydrate (Cl₂FeH₈-O₄) with hexagonal structure is proposed for the fabrication of a gas/temperature sensor. Taking into account the sensitivity of HA to high temperatures, to prevent the collapse and breakdown of bonds and the leakage of volatiles without damaging the composite structure, a freeze-drying machine is designed and fabricated. X-ray diffraction, FTIR, SEM, EDAX, TEM, absorption and photoluminescence analyses of composite are studied. XRD is used to confirm the material structure and the crystallite size of the composite is calculated by the Monshi-Scherrer method, and a value of 81.60 ± 0.06 nm is obtained. The influence of the oxygen environment on the absorption and photoluminescence measurements of the composite and the influence of vaporized ethanol, N₂ and CO on the SiO₂/composite/Ag sensor device are investigated. The sensor with a 30 nm-thick layer of composite shows the highest response to vaporized ethanol, N₂ and ambient CO. Overall, the composite and sensor exhibit a good selectivity to oxygen, vaporized ethanol, N₂ and CO environments.

Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development.

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

Gallium Oxide for Gas Sensor Applications: A Comprehensive Review

Ga₂O₃ has emerged as a promising ultrawide bandgap semiconductor for numerous device applications owing to its excellent material properties. In this paper, we present a comprehensive review on major advances achieved over the past thirty years in the field of Ga₂O₃-based gas sensors. We begin with a brief introduction of the polymorphs and basic electric properties of Ga₂O₃. Next, we provide an overview of the typical preparation methods for the fabrication of Ga₂O₃-sensing material developed so far. Then, we will concentrate our discussion on the state-of-the-art Ga₂O₃-based gas sensor devices and put an emphasis on seven sophisticated strategies to improve their gas-sensing performance in terms of material engineering and device optimization. Finally, we give some concluding remarks and put forward some suggestions, including (i) construction of hybrid structures with two-dimensional materials and organic polymers, (ii) combination with density functional theoretical calculations and machine learning, and (iii) development of optical sensors using the characteristic optical spectra for the future development of novel Ga₂O₃-based gas sensors.

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.

Cascading Photoelectric Detecting and Chemiresistive Gas-Sensing Properties of Pb5 S2 I6 Nanowire Mesh for Multi-Factor Accurate Fire Alarm

Accurate fire warning is very important for people's life and property safety. The most commonly used fire alarm is based on the detection of a single factor of gases, smoke particles, or temperature, which easily causes false alarm due to complex environmental conditions. A facile multi-factor route for fabricating an accurate analog fire alarm using a Pb₅ S₂ I₆ nanowire mesh based on its photoelectric and gas-sensing dual function is presented. The Pb₅ S₂ I₆ nanowire mesh presents excellent photoelectric detection capabilities and is sensitive to ppm-level NO₂ at room temperature. Under the "two-step verification" circuit of light and gas factors, the bimodal simulation fire alarm based on this Pb₅ S₂ I₆ nanowire mesh can resist the interference of complex environmental factors and effectively reduce the false alarm rate.

Chemical Discrimination of Benzene Series and Molecular Recognition of the Sensing Process over Ti-Doped Co3O4

This work achieved the chemical discrimination of benzene series (toluene, xylene isomers, and ethylbenzene gases) based on the Ti-doped Co₃O₄ sensor. Benzene series gases presented different gas-response features due to the differences in redox rate on the surface of the Ti-doped Co₃O₄ sensor, which created an opportunity to discriminate benzene series via the algorithm analysis. Excellent groupings were obtained via the principal component analysis. High prediction accuracies were acquired via k-nearest neighbors, linear discrimination analysis (LDA), and support vector machine classifiers. With the confusion matrix for the data set using the LDA classifier, the benzene series have been well classified with 100% accuracy. Furthermore, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory calculations were conducted to investigate the molecular gas-solid interfacial sensing mechanism. Ti-doped Co₃O₄ showed strong Lewis acid sites and adsorption capability toward reaction species, which benefited the toluene gas-sensing reaction and resulted in the highly boosted gas-sensing performance. Our research proposed a facile distinction methodology to recognize similar gases and provided new insights into the recognition of gas-solid interfacial sensing mechanisms.

Two-Dimensional Field-Effect Transistor Sensors: The Road toward Commercialization

The evolutionary success in information technology has been sustained by the rapid growth of sensor technology. Recently, advances in sensor technology have promoted the ambitious requirement to build intelligent systems that can be controlled by external stimuli along with independent operation, adaptivity, and low energy expenditure. Among various sensing techniques, field-effect transistors (FETs) with channels made of two-dimensional (2D) materials attract increasing attention for advantages such as label-free detection, fast response, easy operation, and capability of integration. With atomic thickness, 2D materials restrict the carrier flow within the material surface and expose it directly to the external environment, leading to efficient signal acquisition and conversion. This review summarizes the latest advances of 2D-materials-based FET (2D FET) sensors in a comprehensive manner that contains the material, operating principles, fabrication technologies, proof-of-concept applications, and prototypes. First, a brief description of the background and fundamentals is provided. The subsequent contents summarize physical, chemical, and biological 2D FET sensors and their applications. Then, we highlight the challenges of their commercialization and discuss corresponding solution techniques. The following section presents a systematic survey of recent progress in developing commercial prototypes. Lastly, we summarize the long-standing efforts and prospective future development of 2D FET-based sensing systems toward commercialization.

Development of a Portable and Modular Gas Generator: Application to Formaldehyde Analysis

This work aims at developing and validating under laboratory-controlled conditions a gas mixture generation device designed for easy on-site or laboratory calibration of analytical instruments dedicated to air monitoring, such as analysers or sensors. This portable device, which has been validated for formaldehyde, is compact and is based on the diffusion of liquid formaldehyde through a short microporous interface with an air stream to reach non-Henry equilibrium gas–liquid dynamics. The geometry of the temperature-controlled assembly has been optimised to allow easy change of the aqueous solution, keeping the microporous tube straight. The formaldehyde generator has been coupled to an on-line formaldehyde analyser to monitor the gas concentration generated as a function of the liquid formaldehyde concentration, the temperature, the air gas flow rate, and the microporous tube length. Our experimental results show that the generated gaseous formaldehyde concentration increase linearly between 10 and 1740 µg m−3 with that of the aqueous solution ranging between 0 and 200 mg L−1 for all the gas flow rates studied, namely 25, 50 and 100 mL min−1. The generated gas phase concentration also increases with increasing temperature according to Henry’s law and with increasing the gas–liquid contact time either by reducing the gas flow rate from 100 to 25 mL min−1 or increasing the microporous tube length from 3.5 to 14 cm. Finally, the performances of this modular formaldehyde generator are compared and discussed with those reported in the scientific literature or commercialised by manufacturers. The technique developed here is the only one allowing to operate with a low flow rate such as 25 to 100 mL min−1 while generating a wide range of concentrations (10–1000 µg m−3) with very good accuracy.

Selective multiple analyte detection using multi-mode excitation of a MEMS resonator

This work reports highly selective multiple analyte detection by exploiting two different mechanisms; absorption and thermal conductivity using a single MEMS device. To illustrate the concept, we utilize a resonator composed of a clamped-guided arch beam connected to a flexural beam and a T-shaped moveable mass. A finite element model is used to study the mode shapes and mechanical behavior of the device with good agreement reported with the experimental data. The resonator displays two distinct out-of-plane modes of vibration. For humidity detection, we utilize physisorption by functionalizing the surface with graphene oxide (GO), which has strong affinity toward water vapors. The GO solution is prepared and drop-casted over the mass surface using an inkjet printer. On the other hand, cooling the heated flexural beams is used for helium (He) detection (thermal-conductivity-based sensor). The sensor characteristics are extensively studied when the modes are individually and simultaneously actuated. Results affirm the successful utilization of each mode for selective detection of relative humidity and He. This novel mode-dependent selective detection of multiple analytes can be a promising building block for the development of miniature, low-powered, and selective smart sensors for modern portable electronic devices.

Recent Advances in Processing and Applications of Heterobimetallic Oxide Thin Films by Aerosol-assisted Chemical Vapor Deposition

The fabrication of smart, efficient, and innovative devices critically needs highly refined thin-film nanomaterials; therefore, facile, scalable, and economical methods of thin films production are highly sought-after for the sustainable growth of the hi-tech industry. The chemical vapor deposition (CVD) technique is widely implemented at the industrial level due to its versatile features. However, common issues with a precursor, such as reduced volatility and thermal stability, restrict the use of CVD to produce novel and unique materials. A modified CVD approach, named aerosol-assisted CVD (AACVD), has been the center of attention due to its remarkable tendency to fabricate uniform, homogenous, and distinct nano-architecture thin films in an uncomplicated and straightforward manner. Above all, AACVD can utilize any custom-made or commercially available precursors, which can be transformed into a transparent solution in a common organic solvent; thus, a vast array of compounds can be used for the formation of nanomaterial thin films. This review article highlights the importance of AACVD in fabricating heterobimetallic oxide thin films and their potential in making energy production (e. g., photoelectrochemical water splitting), energy storage (e. g., supercapacitors), and environmental protection (e. g., electrochemical sensors) devices. A heterobimetallic oxide system involves two metallic species either in a composite, solid solution, or metal-doped metal oxides. Moreover, the AACVD tunable parameters, such as temperature, deposition time, and precursor, which drastically affect thin films microstructure and their performance in device applications, are also discussed. Lastly, the key challenges and issues of scaling up AACVD to the industrial level and processing for emerging functional materials are also highlighted.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Flexible IoT Gas Sensor Node for Automated Life Science Environments Using Stationary and Mobile Robots

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.