Gas Sensors Research Articles

Facile Fabrication of Lilac-Like Multiple Self-Supporting WO3 Nanoneedle Arrays with Cubic/Hexagonal Phase Junctions for Highly Sensitive Ethylene Glycol Gas Sensors.

Metal oxides with nanoarray structures have been demonstrated to be prospective materials for the design of gas sensors with high sensitivity. In this work, the WO3 nanoneedle array structures were synthesized by a one-step hydrothermal method and subsequent calcination. It was demonstrated that the calcination of the sample at 400 °C facilitated the construction of lilac-like multiple self-supporting WO3 arrays, with appropriate c/h-WO3 heterophase junction and highly oriented nanoneedles. Sensors with this structure exhibited the highest sensitivity (2305) to 100 ppm ethylene glycol at 160 °C and outstanding selectivity. The enhanced ethylene glycol gas sensing can be attributed to the abundant transport channels and active sites provided by this unique structure. In addition, the more oxygen adsorption caused by the heterophase junction and the aggregation of reaction medium induced by tip effect are both in favor of the improvement on the gas sensing performance.

Toward highly sensitive, selective, and stable palladium‐based MEMS gas sensors for hydrogen energy security

Toward highly sensitive, selective, and stable palladium‐based MEMS gas sensors for hydrogen energy security

A Machine Learning Approach for the Classification of Refrigerant Gases

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels

Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.

Molecularly specific detection towards trace nitrogen dioxide by utilizing Schottky-junction-based Gas Sensor

Trace NO2 detection is essential for the production and life, where the sensing strategy is appropriate for rapid detection but lacks molecular specificity. This investigation proposes a sensing mechanism dominated by surface-scattering to achieve the molecularly-specific detection. Two-dimensional Bi2O2Se is firstly fabricated into a Schottky-junction-based gas-sensor. Applied with an alternating excitation, the sensor simultaneously outputs multiple response signals (i.e., resistance, reactance, and the impedance angle). Their response times are shorter than 200 s at room temperature. In NO2 sensing, these responses present the detection limit in ppt range and the sensitivity is up to 16.8 %·ppb−1. This NO2 sensitivity presents orders of magnitude higher than those of the common gases within the exhaled breath. The impedance angle is involved in the principle component analysis together with the other two sensing signals. Twelve kinds of typical gases containing NO2 are acquired with molecular characteristics. The change in dipole moment of the target molecule adsorbed is demonstrated to correlate with the impedance angle via surface scattering. The proposed mechanism is confirmed to output ultra-sensitive sensing responses with the molecular characteristic.

Cobalt-Doped Ceria Sensitizer Effects on Metal Oxide Nanofibers: Heightened Surface Reactivity for High-Performing Chemiresistive Sensors.

Chemiresistive gas sensors based on semiconducting metal oxides typically rely on noble metal catalysts to enhance their sensitivity and selectivity. However, noble metal catalysts have several drawbacks for practical utilization, including their high cost, their propensity for spontaneous agglomeration, and poisoning effects with certain types of gases. As such, in the interest of commercializing the chemiresistive gas sensor technology, we propose an alternative design for a noble-metal-free sensing material through the case study of Co-doped ceria (Co-CeO2) catalysts embedded in a SnO2 matrix. In this investigation, we utilized electrospinning and subsequent calcination to prepare Co-CeO2 catalyst nanoparticles integrated with SnO2 nanofibers (NFs) with uniform particle distribution and particle size regulation down to the sub-2 nm regime. The resulting Co-CeO2@SnO2 NFs exhibited superior gas sensing characteristics toward isoprene (C5H8) gas, a significant biomarker for monitoring the onset of various diseases through breath diagnostics. In particular, we identified that the Co-CeO2 catalysts, owing to the transition metal doping, facilitated the spillover of chemisorbed oxygen species to the SnO2 sensing body. This resulting in the sensor having a 27.4-fold higher response toward 5 ppm of C5H8 (compared to pristine SnO2), exceptionally high selectivity, and a low detection limit of 100 ppb. The sensor also exhibited high stability for prolonged response-recovery cycles, attesting to the strong anchoring of Co-CeO2 catalysts in the SnO2 matrix. Based on our findings, the transition metal-doped metal oxide catalysts, such as Co-CeO2, demonstrate strong potential to completely replace noble metal catalysts, thereby advancing the development of the commercially viable chemiresistive gas sensors free from noble metals, capable of detecting target gases at sub-ppm levels.

Sensing performance of lead monoxide-doped tin oxide thick film gas sensor

Sensing performance of lead monoxide-doped tin oxide thick film gas sensor

Size-Dependent Thresholds in CuO Nanowires: Investigation of Growth from Microstructured Thin Films for Gas Sensing.

An experimental characterization of cupric oxide nanowire (CuO NW) growth from thermally oxidized, microstructured Cu thin films is performed. We have systematically studied the influence of the thickness and dimension of Cu layers on the synthesis of CuO NW. The objective was to determine the optimum Cu geometries for increased CuO NWs growth to bridge the gap between adjacent Cu structures directly on the chip for gas sensing applications. Thresholds for CuO-NW growth regarding film thickness and lateral dimensions are identified based on SEM images. For a film thickness of 560 nm, NWs with lengths > 500 nm start to grow from the edges of Cu structures with an area ≥ 4 µm2. NWs growing from the upper surface were observed for an area ≥ 16 µm2. NW growth between adjacent thermally oxidized thin films was analyzed. The study provides information on the most relevant parameters of CuO NWs growth, which is mandatory for integrating CuO NWs as gas sensor components directly on microchips. Based on this result, the gap size of the structure was varied to find the optimum value of 3 µm.

Design and development of solidly mounted bulk acoustic wave resonator (SMR)-based ammonia gas sensor

Design and development of solidly mounted bulk acoustic wave resonator (SMR)-based ammonia gas sensor

Signal enhancement of the gas detection based on quartz-enhanced photothermal spectroscopy technology

This paper presents an improved gas sensor based on the dual-excitation of quartz-enhanced photothermal spectroscopy (QEPTS) using a single quartz tuning fork (QTF) for signal detection. The silver coating on one side of the QTF was chemically etched to increase the laser power interacted with QTF for QEPTS signal excitation. By etching the silver coating on one side of QTF, the reflection structure between the silver coating of the other side of QTF and the external flat mirror was established. The device uses an absorption gas cell with an optical range length of 3 m, making the laser beam interact with the gas more completely and posing more gas concentration information. Acetylene was selected as the target gas to verify the performance of the sensor. The experimental results show that the signal amplitude with a flat mirror was 1.41 times that without a flat mirror, and 2.47 times that of traditional QEPTS sensor. The system has a minimum detection limit (MDL) of 1.10 ppmv, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 7.14 × 10−9 cm−1·W·Hz−1/2. Allan variance analysis results show that when the integration time is 700 s, the MDL of the system is 0.21 ppmv. The proposed gas sensor can play an important role on detecting trace gas in many fields.

Development and characterization of ZIF-8/WS2 composites for NH3 detection at room temperature

This study successfully synthesized zeolitic imidazolate frameworks-8 (ZIF-8) particles and tungsten disulfide (WS2) nanosheets, forming ZIF-8/WS2 composites via a layer-by-layer filtration adsorption method. The morphological and structural characterization results revealed that ZIF-8 particles and WS2 nanosheets effectively combine under filtration to form a hierarchical film structure. Room temperature ammonia (NH3) detection results indicated that the ZIF-8/WS2 composites-based gas sensor, due to the high specific surface area (1458 m2/g) and strong gas adsorption capability of ZIF-8, exhibits response intensity to NH3 that is 4.98 times higher than that of pure WS2 nanosheets. Additionally, it demonstrated stable response and recovery curves with short response/recovery times (113.5 ± 3.9 s/115.0 ± 1.9 s). This work provides valuable insights and inspiration for the construction of composite materials involving 2D TMDs and the enhancement of their room-temperature gas sensing performance.

A Verilog-A Model for a Light-Activated Semiconductor Gas Sensor

A Verilog-A Model for a Light-Activated Semiconductor Gas Sensor

Self-Calibrated Method for WMS Gas Sensor Immune to Optical and Electronic Drift

Self-Calibrated Method for WMS Gas Sensor Immune to Optical and Electronic Drift

Highly Sensitive and Selective Gas Sensors for Ethanol Based on Mn Doped ZnO Nanoflakes

Highly Sensitive and Selective Gas Sensors for Ethanol Based on Mn Doped ZnO Nanoflakes

Surface and Interface Investigation on MoS₂-rGO Hybrids for Room Temperature Gas Sensing

Surface and Interface Investigation on MoS₂-rGO Hybrids for Room Temperature Gas Sensing

Sol-gel derived ZnO film as a gas sensor: Influence of UV processing versus a thermal annealing

While preparing oxide layers as gas sensors by a sol-gel approach, a high-temperature annealing makes a challenge to apply in numerous applications like flexible electronics with a heavy influence on the oxide microstructure. Therefore, its replacing by UV irradiation combined with a mild heating as “photo-annealing” paves the way to develop soft protocols when designing oxide-based gas sensors bearing a fine nanocrystallinity. Herein, we consider hierarchical sol-gel derived ZnO films which were a subject of conventional annealing and photoannealing to compare their gas-sensor performance when exposed to alcohol vapors. It is found that films obtained by photoannealing have an X-ray amorphous character, in contrast to ones being thermally annealed; although, the hierarchical organization of both samples revealed by SEM is almost identical. The DFTB modeling performed for ZnO crystal exposed to alcohol molecules and water has indicated the chemiresistive effect to be enhanced with a molecular weight of analytes. These observations were validated in experiment with sol-gel ZnO layers which exhibited an alcohol response in sub-ppm concentration range down to 10 ppb. To selectively compare the impact of various alcohols, we successfully applied a linear-discriminant analysis to the vector signal of the on-chip multisensor array.

Hierarchical 0D/1D/2D Au/PANI/WS2 ternary nanocomposite NH3 sensor with high performance and fast response/recovery for food spoilage detection

Ammonia (NH3) gas is regarded as a biomarker for clinical diagnosis of various diseases and a potential indicator for the spoilage of protein-rich foods. The acuate detection of the ammonia gas concentration depend on reliable gas sensor. In this paper, flexible room temperature ammonia gas sensor based on hierarchical Au/PANI/WS2 ternary composite was successfully prepared by in-situ polymerization, reduction and drop-coating process. The Au/PANI/WS2-based ammonia sensor was tested for its room temperature sensing property including response value, response/recovery time, selectivity, and long-term stability. The effects of flexible state, humidity and operating temperature on the sensor response were also investigated. The 0D/1D/2D Au/PANI/WS2-based sensor showed significantly enhanced ammonia sensing property both in response and response/recovery time as compared to that of pristine WS2. Gas sensing mechanism of the prepared sensor was investigated by complex impedance spectroscopy and in-situ fourier transform infrared spectroscopy analysis. Besides, the sensor was proved for its practical usability in seafood spoilage detection and warning. The construction of multidimensional materials into hierarchical structure may provide an instructive way for the sensibilization of layered two-dimensional nanomaterial of WS2.

Synthesis, Characterisation, and Applications of TiO and Other Black Titania Nanostructures Species (Review)

Black titania, a conductive ceramic material class, has garnered significant interest due to its unique optical and electrochemical properties. However, synthesising and properly characterising these structures pose a considerable challenge. This diverse material family comprises various titanium oxide phases, many of them non-stoichiometric. The term “black TiO2” was first introduced in 2011 by Xiaobo Chen, but Arne Magneli’s groundbreaking discovery and in-depth investigation of black titania in 1957 laid the foundation for our understanding of this material. The non-stoichiometric black titanium oxides were then called the Magneli phases. Since then, the science of black titania has advanced, leading to numerous applications in photocatalysis, electrocatalysis, supercapacitor electrodes, batteries, gas sensors, fuel cells, and microwave absorption. Yet, the literature is rife with conflicting reports, primarily due to the inadequate analysis of black titania materials. This review aims to provide an overview of black titania nanostructures synthesis and the proper characterisation of the most common and applicable black titania phases.

Fast Response Room Temperature Amperometric Gas Sensors with Modified Fe–N–C Electrodes for ppb-Level H2S Detection

Fast Response Room Temperature Amperometric Gas Sensors with Modified Fe–N–C Electrodes for ppb-Level H₂S Detection