Gas Sensors Research Articles

Fast switching hydrogen gas leakage identification using FPGA

Hydrogen will play a key part in the decarbonization of the global energy supply as the wealth of hydrogen continues to rise. However, applying hydrogen knowledge necessitates the installation of strong safety protocols, including the use of reliable hydrogen gas-detecting devices. Gas detection and monitoring at a large scale in industries need programmable electronic systems that can provide fast response and switching. The objective of the research work is to analyze the hydrogen gas detection system using a field programmable gate array (FPGA). The system is integrated with the gas sensor module, temperature control module, buzzer for alarm, liquid crystal display (LCD) and light-emitting diode (LED) for information display, and global system for mobile communication (GSM) for wireless message communication to the mobile. The temperatures module works on the sampling of the heater resistance using a voltage divider and inbuilt analog-to-digital converter (ADC), which depend on the output voltage of the digital-to-analog converter (DAC). The comparator module compares the gas concentration with the pre-decided threshold limit and acknowledges the buzzer accordingly. The system behavior for monitoring and gas concentration detection is simulated in Xilinx Vivado design suite 2020.1. The functionality of the chip is also verified in the Modelsim 10.0 waveform simulator. for the testbench tata sand samples used. LCD (16x2) displays the messages whether hydrogen gas is detected or not and the same message is delivered on mobile. The novelty of the work is that the system is applicable for faster switching in industrial applications and the designed system supported 550 MHz frequency in our simulation and synthesis.

Waving Hand as Infrared Source for Ubiquitous Gas Sensing

Gases in the environment can significantly affect our health and safety. As mobile devices gain popularity, we consider to explore a human-centered gas detection system that can be integrated into commercial mobile devices to realize ubiquitous gas detection. However, existing gas sensors either have too long response delays or are too cumbersome. This paper shows the feasibility of performing gas sensing by shining infrared (IR) signals emitted from our hands through the gas, allowing the system to rely on a single IR detector. The core opportunity arises from the fact that the human hand can provide stable, broadband, and omnidirectional IR radiation. Considering that IR signals experience distinct attenuation when passing through different gases or gases with different concentrations, we can integrate the human hand into the gas sensing system to enable extremely low-power and sustainable gas sensing. Yet, it is challenging to build up a robust system directly utilizing the hand's IR radiation. Practical issues include low IR radiation from the hand, unstable optical path, impact of environmental factors such as ambient temperature, etc. To tackle these issues, we on one hand modulate the IR radiation from the hand leveraging the controllability of the human hand, which improves the hand's IR radiation. On the other hand, we provide a dual-channel IR detector design to filter out the impact of environmental factors and gases in the environment. Extensive experiments show that our system can realize ethanol, gaseous water, and CO2 detection with 96.7%, 92.1% and 94.2%, respectively.

Development of an IoT-Based Alcohol Detector Using Blynk and NodeMCU

Although the consumption of alcoholic beverages has been a part of human society for a very long time, it is also a big contributor to accidents and health problems. Driving under the influence of alcohol is a leading contributor to death and injury on the roads across the globe. Individuals continue to operate motor vehicles while under the influence of alcohol, despite the existence of stringent regulations and awareness efforts, which poses a risk to both themselves and others who are on the road. With the intention of preventing drunk driving and promoting road safety, an Internet of Things (IoT)-based alcohol detector has been developed utilising Blynk and NodeMCU. This detector was developed to address this issue. The amount of alcohol that is present in a person's blood, urine, or breath can be determined with the help of a device known as an alcohol detector. Using a gas sensor, the device is able to determine the amount of alcohol that is present in the breath of the user. A connection has been made between the sensor and the NodeMCU, which is tied to the Blynk application. The NodeMCU receives the information that is gathered by the gas sensor that is incorporated within the device. This sensor monitors the amount of alcohol that is breathed by the user. Through the use of a smartphone application, Blynk is an Internet of Things platform that gives consumers the ability to control and monitor connected devices. This alcohol detector that is based on the Internet of Things is extremely portable, which is one of its most significant features. A cost-effective solution has been developed through the utilisation of open-source technologies such as Blynk and NodeMCU. This has enabled the device to maintain its accuracy and quality without compromising its functionality.

Pd and Pd-B modified g-CN monolayer as innovative sensor and scavenger for CO, NO2, C2H2 and C2H4: A DFT study

The prompt detection of toxic gases like CO, NO2, C2H2, and C2H4 is crucial for both environmental preservation and industrial safety. In this work, we investigate the adsorption behavior and sensing performance of these four gases on both intrinsic g-CN and g-CN modified with single Pd atom and Pd-B pair, denoted as Pd-g-CN and Pd-B-g-CN, respectively, utilizing the first-principles calculations. The results indicate that the introduction of Pd atom and Pd-B pair significantly enhances the adsorption effect of g-CN monolayer toward these gases, with adsorption energies ranging from − 0.44 eV to − 1.43 eV. Furthermore, all adsorptions are identified as chemisorption, with the adsorption strength following the order of NO2 > CO > C2H4 > C2H2. Additionally, the adsorption of NO2, C2H2, and C2H4 induces notable changes in the work function of Pd-g-CN and Pd-B-g-CN, as well as approximately a 20-time increase in the bandgap of Pd-g-CN. Moreover, the Pd-g-CN and Pd-B-g-CN monolayers exhibit reasonable recovery times for C2H2 and C2H4, measured as 0.04 s and 6.40 μs at 298 K, respectively. Furthermore, NO2 can be rapidly desorbed from the Pd-B-g-CN surface at 398 K, with a short recovery time of 8.17 s. Therefore, the Pd-g-CN and Pd-B-g-CN monolayers can be regarded as potential gas-sensitive materials for the detection of C2H2 and C2H4 at room temperature, respectively. These research findings provide robust theoretical support for the design of novel gas sensors and efficient toxic gas adsorbents.

Green Valorization of Waste Plastics to Graphene as an Upcycled Eco-Friendly Material for Advanced Gas Sensing

The valorization technique successfully transformed waste polyethylene terephthalate (PET) into valuable carbon nanomaterial (CN)/graphene, while doped and undoped ZnO nanopowders were synthesized via sol–gel methods. Utilizing XRD, BET, TEM, EDX, FTIR, and TGA analyses, the synthesis of sp2 2D sheet, pristine, and doped ZnO nanostructures was confirmed. Solid-state gas sensor devices, tested under 51% relative humidity (RH), 30 °C ambient temperature, and 0.2 flow rate, exhibited a 3.4% enhanced response to H2 gas compared to CO2 at 50 ppm concentrations over time. Notably, the ZnO/CN sensor surpassed CN and ZnO alone, attributed to CN dopant integration with decreasing order of response performance as ZnO/CN > CN > ZnO. This study underscores the efficacy of valorization techniques in generating high-value carbon nanomaterials and their efficacy in bolstering gas sensor performance, with ZnO/CN demonstrating superior response capabilities.

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

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

Surface phonon resonance: A new mechanism for enhancing photonic spin Hall effect and refractive index sensor

Abstract Metal-based surface plasmon resonance (SPR) plays an important role in enhancing the photonic spin Hall effect (SHE) and developing sensitive optical sensors. However, the very large negative permittivities of metals limit their applications beyond the near-infrared regime. In this work, we theoretically present a new mechanism to enhance the photonic SHE by taking advantage of SiC-supported surface phonon resonance (SPhR) in the mid-infrared regime. The transverse displacement of photonic SHE is very sensitive to the wavelength of incident light and the thickness of SiC layer. Under the optimal parameter setup, the calculated largest transverse displacement of SiC-based SPhR structure reaches up to 163.8 μm, which is much larger than the condition of SPR. Moreover, a NO2 gas sensor based on the SPhR-enhanced photonic SHE is theoretically proposed with the superior sensing performance. Both the intensity and angle sensitivity of this sensor can be effectively manipulated by varying the damping rate of SiC. The results may provide a promising paradigm to enhance the photonic SHE in the mid-infrared region and open up new opportunity of highly sensitive refractive index sensors.

Properties investigation of ZnS/porous silicon heterojunction for gas sensing

In this work, the gas sensing properties of ZnS/Porous silicon heterostructures have been investigated. . Zinc sulfide(ZnS) with high gas sensing performance is successfully synthesized over the Porous silicon substrate by the spray pyrolysis method. The properties of the as-prepared samples were characterized X-ray diffraction (XRD), scanning electron microscope (SEM), Fourier transform spectrum (FTIR) and optical properties. The results reveal that the properties of the ZnS/Porous silicon heterostructures enhanced when the when the ZnS concentration is increased. The performance ZnS/Porous silicon as a gas-sensing show that the maximum sensitivity is found to be 5.11 at ZnS concentration of 0.5 M and etching time of 15 min compared to the other sensitivities. The ZnS-PSi heterojunction based gas sensor may be used for UV-light photo-detectors due to a valuable properties such as high sensitivity and fast response.

Wavelength-modulated photoacoustic spectroscopic instrumentation system for multiple greenhouse gas detection and in-field application in the Qinling mountainous region of China

We present a sensitive and compact quantum cascade laser-based photoacoustic greenhouse gas sensor for the detection of CO2, CH4 and CO and discuss its applicability toward on-line real-time trace greenhouse gas analysis. Differential photoacoustic resonators with different dimensions were used and optimized to balance sensitivity with signal saturation. The effects of ambient parameters, gas flow rate, pressure and humidity on the photoacoustic signal and the spectral cross-interference were investigated. Thanks to the combined operation of in-house designed laser control and lock-in amplifier, the gas detection sensitivities achieved were 5.6 ppb for CH4, 0.8 ppb for CO and 17.2 ppb for CO2, signal averaging time 1 s and an excellent dynamic range beyond 6 orders of magnitude. A continuous outdoor five-day test was performed in an observation station in China’s Qinling National Botanical Garden (E longitude 108°29’, N latitude 33°43’) which demonstrated the stability and reliability of the greenhouse gas sensor.

Improving carrier transport and catalytic oxidation of Bi2O3/Co3O4 hybrid structures to enhance gas sensing selectivity and response toward n-butanol gas

N-butanol is a widely used and multifunctional raw material, but is harmful to the environment and human health. Developing gas sensing materials with high selectivity toward n-butanol is the foundation and crux to achieve n-butanol precise detection. Herein, Bi2O3/Co3O4 hybrid structures have been constructed by a feasible two-step strategy and employed as gas sensing materials. The gas sensor based on Bi2O3/Co3O4 composites treated at 400 °C (BCO-400) presents a high selectivity, a superior stability/reliability, a well humidity resistance and a low detection limit of 1 ppm toward n-butanol at a relatively low operating temperature of 130 °C. The response and response/recovery time of BCO-400 to 1 ppm n-butanol is 14.9 % and 51 s/53 s, respectively. In the hybrid structures, p-type Bi2O3 semiconductor as a catalyst promotes the catalytic selectivity of Bi2O3/Co3O4 toward n-butanol gas while the semiconductor Co3O4 significantly improves carrier transport and further decreases baseline resistance in air of hybrid structures, which collaboratively brings about the improved gas sensing performance to n-butanol. This work provides a preferable strategy for bismuth based semiconductor materials in the field of the low temperature volatile organic gas detection.

Effects of Concentration on the Sensing Properties of Rgo/ Cuo Nanoparticles as Ammonia Gas Sensor

Effects of Concentration on the Sensing Properties of Rgo/ Cuo Nanoparticles as Ammonia Gas Sensor

A feature extractor for temporal data of electronic nose based on parallel long short-term memory network in flavor discrimination of Chinese vinegars

Volatile flavor is a key indicator of food quality which can directly affect consumer preference and purchase intention. Electronic nose is considered as a promising intelligent sensory analysis tool for food flavor assessment, however, extracting effective features from the gas sensor array is still a major challenge, which largely determines the performance of subsequent classifiers. Here, a parallel long short-term memory (LSTM) network is proposed as a feature extractor for automatically extracting features from the whole time series of sensor responses in flavor discrimination of five Chinese vinegars. The network was trained by the temporal data from the sensor array and yielded different feature patterns corresponding to different vinegars, which were then fed to other conventional classifiers for pattern recognition. We also evaluated the influence of the extracted feature dimension that is related to the dimension of the hidden state of the LSTM layer on the classification performance. The results indicate that a larger dimension of extracted feature is unnecessary for promoting classification accuracy, instead, the optimum dimension 4 of the hidden state gives the highest accuracy of 95.8% in this application under the softmax evaluator. Moreover, much higher accuracies were obtained when combined with other sophisticated classifiers such as support vector machine. The results demonstrate that the proposed network is competent to extract features directly and automatically from the temporal data of the electronic nose.

Enhanced performance of 1D rod shaped Zirconium (Zr) decorated over spherical shaped Zinc oxide (ZnO) nanostructures for NO2 gas

1D rod shaped Zirconium (Zr) was decorated over spherical shaped Zinc (ZnO) nanostructures, using hydrothermal technique. The as-synthesized nanocomposites were discovered to be discriminatory in terms of sensing nitrogen dioxide (NO2) gas. The impact of doping on structural parameters, optical properties, thermal stability, morphology, and shape were explored using HR-TEM, XRD, SAED, UV–Visible, FE-SEM, FT-IR, and EDS. The doping concentration was optimized for maximal reactivity with gas. ZZ@5 nanocomposites have the highest response (70.9 %) when compared to pure ZnO, ZZ@2, ZZ@7, ZZ@5, and ZZ@10 nanocomposites. NO2 gas reaction and recovery times were 24.5 and 27.8 s, respectively. As the concentration of NO2 gas climbs from 20 to 200 ppm, the reaction to gas sensors increases. NO2 gas has higher selectivity for ZZ@5 nanocomposite than other gases (ethanol, ammonia, hydrogen, carbon monoxide, and liquefied petroleum gas). The repeatability of ZZ@5 nanocomposite was researched for 50 days and discovered to be impressive.

Photovoltaic induced self-powered gas sensor based on 2D MoS2 incorporated NbSe2 nanorods heterostructure for NH3 gas sensing at room temperature

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted significant attention for their optical and gas-sensing applications due to their exceptional sensitivity. Reliable selectivity and low power consumption are two major requirements for photodetector and gas sensor applications in next-generation electronic devices and the Internet of Things. Self-powered sensors (especially photovoltaic gas sensors) can solve these problems. In this study, for the first time, we report 2D TMDs (NbSe2-MoS2 hybrid) on a SiO2/Si substrate to fabricate photovoltaic self-powered gas sensors. The gas sensors are operated by the photovoltaic effect of the NbSe2-MoS2 nanostructure, which is prepared using the liquid phase exfoliation process. Initially, it was revealed that the present hybrid material exhibits photovoltaic properties under light illumination, with a circuit current of 0.25 µA and a circuit voltage of 34 mV. The NbSe2-MoS2 nanostructure characteristics were then used for NH3 gas sensing at different concentrations, and the gas sensing response was detected from low (8.8 % at 10 ppm) to high (28.8 % at 500 ppm) concentrations. The built-in electric field occurred between the NbSe2-MoS2 junction and eventually operated as a driving force for NbSe2-MoS2 gas sensing without an external bias voltage. The physisorption of gas molecules on their surface prompts a charge-transfer mechanism that improves the gas sensor response. The combined outcome of NbSe2-MoS2 heterostructures could pave way to next-generation gas sensing device fabrications.

Highly stable flexible ozone gas sensors using Mn3O4 nanoparticles-decorated IGZO thin films through the SILAR method

Metal or semiconductor nanoparticles (NPs) can enhance the device performance of gas sensors due to their high surface area and unique electronic properties. However, this method requires additional thermal bonding with high temperatures to secure the NPs, making it unsuitable for flexible substrates. In this study, we develop a room-temperature flexible ozone (O3) gas sensor using Mn3O4 NPs on an amorphous InGaZnO (a-IGZO) film. The Mn3O4 NPs are synthesized by a successive ionic layer adsorption and reaction (SILAR) method at low temperatures. The sensor with Mn3O4 NPs exhibits a gas response of 4.06 against 5 ppm O3 gas at room temperature, which is much higher than that of its pristine counterpart. It also shows excellent selectivity for O3 over toxic gases and maintains stability over 60 days, with reproducible folding even after 500 bending cycles. Thus, the combination of SILAR-synthesized Mn3O4 NPs and a-IGZO films provides an effective way to fabricate flexible O3 gas sensors.

Research on the application of WO3 gas sensor based on noble metals modification in cable fire detection

In the monitoring of cable fire, it is of great significance to be able to conduct early fire warning, which requires that sensors have high sensitivity to the cable vapors at low temperatures. In this study, WO3 thick films modified with Au, Pd, and Pt single or bimetallic nanoparticles were fabricated via magnetron sputtering. Gas sensors utilizing these materials exhibited significant response to the low-temperature vapor emitted by YJV22 and YJY23 cables. Among them, Pd/WO3-20 demonstrated a response value of 20 to the vapor of 81∘C YJV22 cables, marking a remarkable 6.3-fold increase compared to pure WO3, and the response time was 13 s. Similarly, Pt/WO3-10 exhibited a response value of 11.11 to the vapor of 104∘C YJY23 cables, representing a 6.9-fold enhancement over pure WO3 with a response time of 12 s. The heating temperature of cables remains below their critical threshold, indicating that it is in the early stages of cable fires, the findings hold significant implications for early warning systems designed to detect cable fires promptly.

High versatility of polyethylene terephthalate (PET) waste for the development of batteries, biosensing and gas sensing devices

Continuously growing adoption of electronic devices in energy storage, human health and environmental monitoring systems increases demand for cost-effective, lightweight, comfortable, and highly efficient functional structures. In this regard, the recycling and reuse of polyethylene terephthalate (PET) waste in the aforementioned fields due to its excellent mechanical properties and chemical resistance is an effective solution to reduce plastic waste. Herein, we review recent advances in synthesis procedures and research studies on the integration of PET into energy storage (Li-ion batteries) and the detection of gaseous and biological species. The operating principles of such systems are described and the role of recycled PET for various types of architectures is discussed. Modifying the composition, crystallinity, surface porosity, and polar surface functional groups of PET are important factors for tuning its features as the active or substrate material in biological and gas sensors. The findings indicate that conceptually new pathways to the study are opened up for the effective application of recycled PET in the design of Li-ion batteries, as well as biochemical and catalytic detection systems. The current challenges in these fields are also presented with perspectives on the opportunities that may enable a circular economy in PET use.

Two-Step Conversion of Metal and Metal Oxide Precursor Films to 2D Transition Metal Dichalcogenides and Heterostructures.

The widely studied class of two-dimensional (2D) materialsknown as transition metal dichalcogenides (TMDs) are now well-poised to be employed in real-world applications ranging from electronic logic and memory devices to gas and biological sensors. Several scalable thin film synthesis techniques have demonstrated nanoscale control of TMD material thickness, morphology, structure, and chemistry and correlated these properties with high-performing, application-specific device metrics. In this review, the particularly versatile two-step conversion (2SC) method of TMD film synthesis is highlighted. The 2SC technique relies on deposition of a solid metal or metal oxide precursor material, followed by a reaction with a chalcogen vapor at an elevated temperature, converting the precursor film to a crystalline TMD. Herein, the variables at each step of the 2SC process including the impact of the precursor film material and deposition technique, the influence of gas composition and temperature during conversion, as well as other factors controlling high-quality 2D TMD synthesis are considered. The specific advantages of the 2SC approach including deposition on diverse substrates, low-temperature processing, orientation control, and heterostructure synthesis, among others, are featured. Finally, emergent opportunities that take advantage of the 2SC approach are discussed to include next-generation electronics, sensing, and optoelectronic devices, as well as catalysis for energy-related applications.

Enhancing the Responsiveness of Thermoelectric Gas Sensors with Boron-Doped and Thermally Annealed SiGe Thin Films via Low-Pressure Chemical Vapor Deposition.

Thermoelectric gas sensor (THGS) devices with catalysts and Si0.8Ge0.2 thin films of different boron doping levels of 1018, 1019, and 1020 cm-3 were fabricated, and their transport properties are investigated. SiGe films were deposited on Si3N4/SiO2 multilayers on Si substrates using low-pressure chemical vapor deposition (LPCVD) and thermally annealed at 1050 °C. The Seebeck coefficients of the SiGe films were increased after thermal annealing, ranging from 191 to 275 μV/K at temperatures of 74 to 468 °C in air, and reaching the highest power factor of 6.78 × 10-4 W/mK2 at 468 °C. The thermal conductivity of the SiGe films varied from 2.4 to 3.0 W/mK at 25 °C. The THGS detection performance was tested for the H2 gas in air from 0.01 to 1.0%, and compared to the thermoelectric properties of the SiGe films. The high-temperature annealing treatment process was successful in enhancing the thermoelectric performance of both the SiGe films and sensor devices, achieving the best THGS performance with the sensor device fabricated from the annealed SiGe film with 1018 cm-3 boron-doped Si0.8Ge0.2.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.