Gas Sensors Research Articles

Solution-processed NO2 gas sensor based on poly(3-hexylthiophene)-doped PbS quantum dots operable at room temperature

The global industrial development and increase in the number of transportation vehicles, such as automobiles and ships, have led to a steady increase in the issues related to greenhouse gas emissions. NO2 is a greenhouse gas emitted in large quantities from automobiles and factories, and its emission is unavoidable in the modern world. Therefore, a sensor capable of precise detection of NO2 is required. The most commonly reported types of NO2 sensors are those based on metal oxides. However, their operation at room temperature is impossible owing to their high-temperature operating characteristics, and therefore, a heater must be designed inside or installed outside the sensor for heating. Meanwhile, NO2 sensors based on PbS quantum dots (QDs) are advantageous as they can operate at room temperature and can be easily manufactured through a solution process rather than a complicated semiconductor process. Herein, a NO2 sensor was fabricated by doping PbS QDs with poly(3-hexylthiophene) (P3HT). The as-developed sensor exhibited high responsivity to 100–0.4-ppm NO2 gas with a resolution of 200 ppb owing to the stability of the thin film and high hole mobility of P3HT.

Characterization of SnO2-based gas sensors to analyze diesel and biodiesel blends

This study analyzed biodiesel blends using sensors constituted by SnO2 pellets with gold interdigitated contacts deposited on the surface. Tin dioxide was synthesized by the polymeric precursor method with further calcination treatment at 500°C, conformed and sintered at three temperatures to form three different samples: 700°C (Sn1), 900°C (Sn2) and 1100°C (Sn3). The samples were analyzed by X-Ray Diffraction, FEG-SEM (Field Emission Gun - Scanning Electron Microscopy), Raman spectroscopy, and electrical measurements. Results confirmed the formation of rutile-type tetragonal cassiterite phase, with quasi-spherical particles a moderate degree of heterogeneity in particle size 30-90 nm). Raman vibrational modes and infrared spectrum also confirmed the presence of tin oxide in high purity rutile phase. Sensorial measurements showed high sensitivity, response, and recovery time in tests with biodiesel/ethanol mixtures, allowing to distinguish between blends with a wide range of composition and serving as a potential device to detect adulterated fuel.

Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O

Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor’s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g−1 to H2 concentrations (0–800 ppm) and relative humidity (0–70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations.

Classification and detection of noise in IoT based MQ gas sensors using artificial neural network

Abstract IoT-based Metal Oxide Semiconductor (MOS) gas sensors have potential applications in health, industrial, and agriculture sectors. MQ gas sensors are easy to use, have a large detection range, high sensitivity, and efficiency, and can be interfaced with Arduino for effective use. However, like other any other sensors, the MQ gas sensors are also not able to escape the effect of noise that affects the sensitivity and selectivity causing the unwanted wrong classification and identification of gases, etc finally leading to a wrong justification of results and data. Hence, the study of noise and its removal is very crucial for greater accuracy in its analysis. The main aim of our work is to check whether one can classify different types of noises or noisy signals accurately using even simple ANNs so that after testing the different filtration techniques for the signals we can obtain an independent system specifically for the MQ gas sensors, that can classify as well as filter the noisy signals based on the category to which they are classified. The best and most effective noise filtration method is then obtained. During the study Narrow Neural Network model and Medium Neural Network demonstrated high accuracy in validation and testing, with ROC curves indicating their efficiency and effectiveness.

Theoretical study of adsorption of gas (CO, CO2, NH3) by metal (Au, Ag, Cu)-doped single-layer WS2.

The adsorptions of gas (CO, CO2, NH3) by metal (Au, Ag, Cu)-doped single layer WS2 are studied by density functional theory. The doping of metal atoms makes WS2 behave as n-type semiconductors. The final adsorption sites for CO, CO2, and NH3 are close to the atomic sites of the doped metal. The adsorptions of CO and NH3 gases on Cu/WS2, Ag/WS2, and Au/WS2 are dominated by chemisorption. The doped metal atoms enhance the hybridization of the substrate with the gas molecular orbitals, which contributes to the charge transfer and enhances the adsorption of the gas with the material surface. The adsorptions of CO and NH3 on Cu/WS2 and Ag/WS2 allow favorable desorption in a short time after heating. The single-layer Cu/WS2 is proved to have the potential to be used as a reliable recyclable sensor for CO. This work provides a theoretical basis for developing high-performance WS2-based gas sensors. In this paper, the adsorption energy, electronic structure, charge transfer, and recovery time of CO, CO2, and NH3 in the doped system have been investigated based on the CASTEP code of density functional theory. The exchange correlation function used is the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA). The TS (Tkatchenko-Scheffler) dispersion correction method was used to involve the effects of van der Waals interaction on the adsorption energies for all adsorption system. The ultrasoft pseudopotentials are chosen and the plane-wave cut-off energies are set to 500eV. The k-point mesh generated by the Monkhorst package scheme is used to perform the numerical integration of the Brillouin zone and 5 × 5 × 1k-point grid is used. The tolerances of total energy convergence, maximum ionic force, ionic displacement, and stress component are 1.0 × 10-5eV/atom, 0.03eV/Å, 0.001Å, and 0.05 GPa, respectively.

In Situ Growth of Nanorod-Shaped Ni,Co-MOF on Mo2CTx MXene Surface to Realize Enhanced Energy Storage for Supercapacitors.

Mo2CTx MXene materials, known for their high conductivity and abundant surface functional groups, are widely utilized as electrode materials in supercapacitors. However, their tendency to stack during electrochemical energy storage hinders their performance. The in situ growth of nanorod-shaped Ni,Co bimetallic metal-organic frameworks (Ni,Co-MOF) on Mo2CTx MXene effectively mitigates this stacking. With their porous structure and high specific surface area, MOFs excel in energy storage, and bimetallic MOFs outperform monometallic ones. The synergy between Mo2CTx MXene and Ni,Co-MOF yields an outstanding performance. In a three-electrode system with 1 M KOH, the Mo2CTx/Ni,Co-MOF composite shows a specific capacitance of 58 mAh g-1 (56.26 mAh cm-3) at 1 A g-1. When used in a Mo2CTx/Ni,Co-MOF//AC asymmetric supercapacitor, it achieves an energy density of 22.7 Wh kg-1(0.022 Wh cm-3) at a power density of 293 W kg-1 (0.284 W cm-3). Future work will focus on enhancing synthesis methods, exploring different bimetallic combinations, and optimizing electrode designs for gas sensors, batteries, fuel cells, biological sensors, and so on, with outstanding performance and sustainability.

High performance and low detection limit isopropanol gas sensor based on 3D ZnSnO3 nanocubes co-catalyzed by Au nanoparticles and ZrO2

In this paper, Au nanoparticles (AuNPs) loaded ZrO2/ZnSnO3 nanocubes are prepared through hydrothermal route and chemical reduction method. The crystal structure, morphology, element distribution, chemical composition, specific surface area and pore size of ZrO2/ZnSnO3 nanocubes are described with XRD, TEM, XPS and BET. Interestingly, the incorporation of Zr ions induces the fragmentation of the edge of ZnSnO3 nanocubes, which enhance specific surface area of ZrO2/ZnSnO3 nanocubes. The response of ZnSnO3 materials for isopropanol is increased through doping ZrO2 and modifying AuNPs. The 1.5 at% AuNPs-loaded 5 at% ZrO2/ZnSnO3 nanocubes presents a higher response to 100 ppm isopropanol with a fast response/recovery time (3/4 s) at the operating temperature of 260℃. And the generation of ZnSnO3, ZrO2 and AuNPs heterojunctions contributes to raise the isopropanol response. In addition, the catalysis of ZrO2 and AuNPs also can increase their reaction speed between absorbed oxygen and isopropanol molecules. This research results provide a new scheme for the development of isopropanol sensor.

Deep-Learning-Based Blood Glucose Detection Device Using Acetone Exhaled Breath Sensing Features of α-Fe2O3-MWCNT Nanocomposites.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-Fe2O3-multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-Fe2O3 were added to the MWCNTs by a simple solution method. The optimized acetone gas sensor showed a response of 5.15 to 10 ppm acetone gas at 200 °C. Also, the fabricated sensor showed very good sensing properties even in an atmosphere with high relative humidity. Since the EB has high humidity, the proposed sensor is a promising device to exactly detect the amount of acetone in EB with high humidity. The sensor was powered by a 3200 mAh battery with the possibility of charging using mains electricity. To increase the reliability and calibration of the sensing device, a practical test was taken to detect acetone EB from 50 volunteers, and a deep learning algorithm (DLA) was used to detect the effect of various factors on the amount of acetone in each person's acetone EB. The proposed device with ±15 errors had almost 85% correct responses. Also, the proposed device had excellent response, short response time, good selectivity, and good repeatability, leading it to be a suitable candidate for noninvasive blood glucose sensing.

Pure and Ce-Doped MnO2–ZnO Nanocomposites for Colossal Dielectric Energy Storage and Gas Sensing Applications

Pure and Ce-Doped MnO2–ZnO Nanocomposites for Colossal Dielectric Energy Storage and Gas Sensing Applications

Rapid Detection of Ultralow H2S Concentration with on-chip Fabrication of SnO2-based Gas Sensors by Direct Electrodeposition from Non-Aqueous Solvents

Hydrogen sulfide (H2S) causes significant impacts on human health and the environment due to its highly toxic properties. Thus, the design and development of gas sensors to detect and monitor H2S (especially at extremely low concentrations) are challenging tasks for scientists. In this work, SnO2 gas sensors were successfully synthesized directly on a chip by Sn electrodeposition from ethylene glycol solutions + a post-treatment (calcination) stage (to form and stabilize SnO2). Mechanisms and kinetics of Sn electrodeposition on Pt microelectrodes were thoroughly studied using electrochemical techniques. These fabricated gas sensors exhibit a high selectivity toward H2S gas and an effective response to low-level concentrations of H2S in the range of 0.1 ppm–1 ppm at different working temperatures. The influence of electrodeposition conditions and calcination temperatures on the gas response of sensors were also examined. The results have verified that the electrodeposition method from ethylene glycol solutions is promising for the fabrication of ultrasensitive on-chip gas sensors.

Reusable gasochromic gas sensor with enhanced selectivity for acidic gases via surface-engineered all-inorganic perovskite nanocrystals

The use of strong acids is crucial for the fabrication of highly integrated semiconductor devices. To effectively detect and alert against the potential hazards of acid gas leakage, considerable advancements have been made to enhance gas sensor sensitivity. However, most available gas sensors only detect cations (H+), making it challenging to precisely identify the specific types of acids present. Cesium lead halide perovskite nanocrystals, renowned for their exceptional optical properties, have emerged as promising candidates for gas-sensing applications. Nonetheless, concerns regarding reuse stability persist. In this study, surface engineering via solution-phase ligand exchange is conducted to address halide vacancies and prevent acid-induced structural degradation. The resulting surface-modified perovskites exhibit remarkable gasochromic sensitivity and selectivity towards HCl and HNO3. Furthermore, the vivid emission properties of these perovskites enable the visual detection of danger without the need for additional warning equipment. Finally, the reusability of the gas sensors is evaluated, and the perovskite-based sensors are recyclable.

Quantum Confinement and End-Sealing Effects for Highly Sensitive and Stable Nitrogen Dioxide Detection: Homogeneous Integration of Ti3C2Tx-Based Flexible Gas Sensors.

The real-time and room-temperature detection of nitrogen dioxide (NO2) holds significant importance for environmental monitoring. However, the performance of NO2 sensors has been hampered by the trade-off between the high sensitivity and stability of conventional sensitive materials. Here, we present a novel fully flexible paper-based gas sensing structure by combining a homogeneous screen-printed titanium carbide (Ti3C2Tx) MXene-based nonmetallic electrode with a MoS2 quantum dots/Ti3C2Tx (MoS2 QDs/Ti3C2Tx) gas-sensing film. These precisely designed gas sensors demonstrate an improved response value (16.3% at 5 ppm) and a low theoretical detection limit of 12.1 ppb toward NO2, which exhibit a remarkable 3.5-fold increase in sensitivity compared to conventional Au interdigital electrodes. The outstanding performance can be attributed to the integration of the quantum confinement effect of MoS2 QDs and the conductivity of Ti3C2Tx, establishing the main active adsorption sites and enhanced charge transport pathways. Furthermore, an end-sealing effect strategy was applied to decorate the defect sites with naturally oxygen-rich tannic acid and conductive polymer, and the formed hydrogen bonding network at the interface effectively mitigated the oxidative degradation of the Ti3C2Tx-based gas sensors. The exceptional stability has been achieved with only a 1.8% decrease in response over 4 weeks. This work highlights the innovative design of high-performance gas sensing materials and homogeneous gas sensor techniques.

Flexible, Breathable and Hydrophobic SnO2–SnS2–SiO2/SiO2 All‐Inorganic Self‐Supporting Nanofiber Membrane for Ultralow‐Concentration NO2 Sensing Under High Humidity

AbstractInorganic semiconductor gas sensors, being widely utilized in gas‐sensing applications, face significant challenges in attaining mechanical flexibility and humidity resistance in wearable sensing fields. Herein, a highly flexible, breathable, and hydrophobic all‐inorganic self‐supporting nanofiber (NF) gas sensor is developed using electrospinning combined with thermal sulfidation approach. This innovative sensor features a bilayer configuration, with an amorphous SiO2 nanofiber substrate layer and an interwoven SiO2 and SnO2–SnS2 nanofiber active layer. The relatively low elastic modulus of the amorphous SiO2 nanofibers, combined with the three‐dimensional network interwoven structure, endow the SnO2–SnS2–SiO2/SiO2 sensor with superior mechanical flexibility. The sensor exhibits excellent sensitivity, selectivity, moisture resistance, and cycling stability (>10 000 cycles at 140° bending) to both high and low concentration NO2. Notably, an excellent flexible detecting capability of the sensor to NO2, an asthma‐related biomarker, is demonstrated at ultralow concentrations (≈25 ppb) in simulated exhaled breath environments. The enhanced moisture resistance is attributed to the effective inhibition of hydrogen bond formation from H2O molecules by the Sn─S bonds formed through sulfidation of SnO2 nanofibers. This work represents a substantial advancement in the universal fabrication of flexible, breathable and moisture‐resistant inorganic semiconductor sensors for wearable breath sensing applications.

A Novel Impedimetric Ethylene Gas Sensor Based on Copper Foam/CuO/SnO2 Nanocomposite

Ethylene gas plays a key role in the natural ripening of fruits and vegetables. However, high concentrations of ethylene can reduce the shelf life of the product and exacerbate destructive reactions. Measuring the concentration of ethylene is a powerful method to control the ripening and spoilage of agricultural products. The conventional ethylene detection tools are large and expensive or do not offer sufficient sensitivity and selectivity. Therefore, it is important to build small, energy-efficient, low-cost, high-sensitivity ethylene sensors. In this work, CF/CuO/SnO2 nanocomposite was synthesized based on copper oxide nanoclusters by converting copper foam (CF) into tin dioxide/copper oxide (CuO/SnO2) dual-core nano-hybrid using thermal and hydrothermal methods. Energy-dispersive X-ray analysis, field-emission scanning electron microscopy, X-ray diffraction (XRD), grazing XRD, Brunnauer-Emmett-Teller analysis, and UV–vis spectroscopy techniques were used to characterize CF/CuO/SnO2 nanocomposites. Parameters affecting sensor performance such as temperature, gas concentration, sensor stability, and sensor selectivity were also explored. The results showed that CF/CuO/SnO2 nanocomposite with a specific surface area of 1.4480 m2 g−1, a sensitivity of 83%, and ethylene concentration of 80 ppm at 150 °C, as an n-p hybrid, can be a suitable sensor for ethylene detection in air.

Atomic-level insights into sensing performance of toxic gases on the InSe monolayer decorated with Pd and Pt under humid environment

The timely detection and monitoring of toxic gases are crucial for preserving the ecological environment and preventing their adverse effects on human health. In this study, we investigate the adsorption characteristics and sensitivity performance of Pd- and Pt-decorated InSe monolayers, referred to as Pd-InSe and Pt-InSe, towards five toxic gases (CO, NO, NH3, H2S, and SO2) using first-principle calculations. The results show that the doping of Pd and Pt atoms significantly improves the conductivity, adsorption capabilities, and sensing properties of InSe monolayer to these toxic gases. Both Pd-InSe and Pt-InSe monolayers exhibit chemical adsorption of CO, NO, NH3, and H2S, with adsorption energies ranging from –0.56 eV to –1.04 eV, leading to noticeable changes in the bandgap (8.84 % ∼ 31.03 %). Furthermore, the CO/Pd-InSe and H2S/Pt-InSe systems demonstrate excellent sensitivity, with high sensing response values of 1.54×104 and 66.20, respectively, and their sensitivity remains unaffected under humid environments. Importantly, Pt-InSe exhibits a fast recovery time of 0.05 s at 298 K, making it highly promising for the recyclable detection of H2S at room temperature. In contrast, Pd-InSe can be utilized as a reusable gas sensor for CO at 448 K with a good recovery time of 0.5 s. This work can provides theoretical guidance for the design and fabrication of InSe-based gas sensors for the detection of toxic gases.

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.

Patterned Laser-Induced graphene enabling a High-Performance gas sensing Split-Ring resonator

The outstanding potential of microwave resonator-based energy-efficient gas sensors has been overshadowed by their underperforming gas sensing capabilities, especially insignificant sensitivity to detect low-concentration volatile organic compounds (VOCs). This unmet challenge persists primarily due to predominant metallic resonator structures, which further limit their wearable applications and pose environmental concerns. This work presents a laser-induced graphene (LIG)-enabled split-ring resonator (SRR) sensor on a flexible polyimide substrate for the rapid and sensitive detection of gaseous VOCs. To implement the sensor, the conductive traces of the SRR were created using a computer-controlled CO2 laser at an optimized power level, thus inducing 48 µm thick, conductive (24 S/cm) graphene layers on a polyimide substrate. The SRR gas sensor, in which LIG with three-dimensional networks of porosity serves as conductive and even gas-sensitive traces, benefits from a significantly enhanced interaction between the SRR’s electromagnetic field and VOC gases. As a proof of concept, a prototype of the LIG-enabled SRR was implemented and mounted on a test fixture. The developed gas sensor operated at a resonant frequency of 1.402 GHz, which exhibited rapid (∼17 s) and noticeable shifts when exposed to 200 ppm of different VOCs (acetone, ethanol, methanol, toluene, and isopropyl alcohol). Additionally, the sensor demonstrated a linear correlation between the resonant frequency and acetone gas concentration (1 ppm-200 ppm), with a sensitivity of 188 KHz/ppm. These electromagnetic and gas sensing results suggest that polyimide-derived LIG traces can replace metal microstrip lines in the SRR structure, opening up possibilities for high-performance microwave resonator gas sensors, even suitable for flexible and wearable applications.

Discrimination of VOCs with e-nose consisting of a single MEMS sensor using lightweight deep learning model based on SqueezeNet

The detection of volatile organic compounds (VOCs) has numerous applications in environmental monitoring and biomedicine. Metal oxide semiconductor (MOS) gas sensors are widely used in gas detection due to their simple structure, low fabrication cost, and fast response speed. However, MOS gas sensors have broad-spectrum responsiveness to VOCs, making selectivity a challenge. The most commonly used method to obtain selectivity is the E-nose system consisting of multiple sensors. However, the size of E-noses has increased due to the number of sensors with high power consumption and complicated signal processing. In recent years, deep learning has flourished, allowing for accurate discrimination of VOCs and concentration prediction using E-nose consisting of a single sensor. This method significantly reduces power consumption and facilitates portable applications. In this work, we synthesize SnO2-ZnO composite oxide and fabricate a E-nose consisting of a single MEMS gas sensor. The response performance of the E-nose to VOCs is stable with a high output signal-noise ratio (SNR). We design a lightweight deep learning model based on SqueezeNet and train it using transfer learning. Linear regression is used to predict the concentration of VOCs. Our work achieves accurate identification and low-error concentration prediction of VOCs, which is valuable for subsequent portable VOC detection systems.

Zinc oxide behavior in CO detection as a function of thermal treatment time

Gas sensors are crucial for safety and well-being in various environments. Zinc oxide (ZnO) gas sensors are notable for their broad gas detection capabilities. In this study, ZnO structures were synthesized by optimized chemical precipitation method with urea, followed by a thermal treatment at 500 °C for 5, 10, 13, and 15 h. The microstructural, morphological, and CO sensing properties were examined. X-ray Diffraction analysis confirmed the hexagonal wurtzite phase. Crystallite size increased from 17.28 to 18.95 nm with longer thermal treatment times. Scanning Electron Microscopy revealed spherical and semi-spherical agglomerates with middle distribution of particle sizes ranging from 140 to 445 nm. The synthesized ZnO structures were evaluated as gas sensors for CO detection. Response time, recovery time, and sensor response were analyzed in a CO atmosphere at 100, 200, and 300 °C. The sample with thermal treatment for 13 h exhibited the lowest Tr of 2.43 s at a concentration of 166 parts per million and 300 °C. The Tr reduction correlated with a ZnO decrease particle size observed with longer thermal treatment times, highlighting the influence of particle size on sensor performance.

Fabrication of solution-processed flexible resistive gas sensors based on carbon-quantum-dots decorating mesoporous ZnO fibres for room temperature sensing

In recent years, the gas sensor technology has experimented an interesting growth due to the device improvements driven by nanostructured semiconductor films. Nanostructured sensors have enabled the possibility of reducing the operation temperature at room temperature levels, which implies a significant reduction on the power consumption, as well as the possibility to develop sensors over flexible substrates. Therefore, in this work, the fabrication of flexible gas sensors using solution-processing technologies is presented. Nanostructured ZnO mesoporous fibres decorated with Carbon-Quantum-Dots has been used as active layer, and the electrical response, measured as the ratio between the resistance at the target gas respect the resistance in air, is presented. Furthermore, interdigitated electrode configuration has been used for device fabrication with finger spacing of 0.2, 0.4 and 0.6 mm. A maximum response of 0.016 was achieved.