Gas Sensors Research Articles

Enhanced Ethanol Gas Sensor Performance through Adsorption Energy Analysis of Gd-Doped LaFeO3 with rGO Coating: A Density Functional Theory Study

LaFeO3 (LFO) is commonly used as a material for gas sensor applications. However, the LFO material in ethanol gas sensor applications can still improve sensitivity and selectivity parameters. Gadolinium (Gd) doping is widely used in gas sensor applications to increase the sensitivity of gas sensors. In addition, reduced graphene oxide (rGO) materials are commonly used in gas sensor applications to increase gas sensors' selectivity, sensitivity, and working temperature. This study analyzed the effect of Gd doping (LGFO) and the addition of an rGO single layer on LFO material (LGFO@rGO) on sensitivity and selectivity based on the adsorption energy of the system with ethanol gas molecules. Density Functional Theory studies were conducted to yield insight into the LGFO or LGFO@rGO – ethanol gas interactions and the sensitivity and selectivity improvement by changing adsorption energy. Based on the analysis, the presence of Gd doping and single-layer rGO could increase the adsorption energy. The addition of the rGO layer showed an escalation of the adsorption energy of about 9.45%, 2.49 eV in the LGFO to -2.75 eV LGFO@rGO. This improved adsorption capacity translates to a higher sensitivity for detecting lower concentrations of ethanol gas. This result shows the potential of LGFO and LGFO@rGO as ethanol gas sensor materials.

Silver nanoparticle functionalized heterojunction NiO/SnO2 nanotubes for comprehensive sensitization of acetone sensor

Acetone detection with a good sensitivity and a low operating temperature is crucial owing to the serious harm that acetone can cause to the environment and humans and its importance as a marker of diabetes in human exhalation. One-dimensional (1D) semiconductor metal oxides with easily accessible active sites are potential candidates for use in gas sensors; however, the facile synthesis of 1D nanotubes (NTs) remains a challenge. Herein, Ag–NiO/SnO2 NTs are synthesized via an electrospinning process, followed by spin-coating permeation and subsequent annealing and chemical reduction; furthermore, the gas-sensing properties of the Ag–NiO/SnO2 NTs are investigated. The Ag–NiO/SnO2 NT-based sensor exhibits a rapid response to acetone (12 s) with excellent sensitivity and selectivity at a low operating temperature (190 °C). The lower acetone detection limit of the Ag–NiO/SnO2 NT-based sensor is 50 ppb. The 1D porous NTs efficiently promote gas diffusion during reversible resistance signal cycling and provide numerous active sites. Furthermore, the conjunction of NiO nanograins with SnO2 nanograins forms a heterogeneous construction, enhancing the gas sensitivity of the sensing layers. In addition, Ag nanoparticles homogeneously incorporated onto the surface of the 1D NTs enhance substantially the acetone detection capability based on noble-metal sensitization.

Fiber Optic Sensor Coated with Multiple Layers of Hexagonal Boron Nitride Nanosheets (BNNS) for the Detection of Volatile Organic Compounds.

Nowadays, volatile organic compound (VOC) detection is imperative to ensure environmental safety in industry and indoor environments, as well as to monitor human health in medical diagnosis. Gas sensors with the best sensor response, selectivity, and stability are in high demand. Simultaneously, the advancement of nanotechnology facilitates novel nanomaterial-based gas sensors with superior sensor characteristics and low power consumption. Recently, boron nitride, a 2D material, has emerged as an excellent candidate for gas sensing and demonstrated exceptional sensing characteristics for new-generation gas sensing devices. Herein, ultrathin porous boron nitride nanosheets (BNNSs) with large lateral sizes were synthesized using a facile synthesis approach, and their material characteristics were investigated utilizing a variety of analytical techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, ultraviolet spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. A BNNS-coated cladding-modified fiber optic sensor (FOS) probe was prepared and employed for VOC (ammonia, ethanol, and acetone) sensing across concentrations varying from 0 to 300 ppm. The BNNSs-coated FOS demonstrated better selectivity toward 300 ppm ammonia, and specifically annealed BNNSs displayed a maximum sensor response of 55% along with a response/recovery times of 15 s/34 s compared to its counterparts. The superior ammonia sensing performances could be attributed to the formation of ultrathin nanosheets and a porous surface with slit-like features in hexagonal boron nitride.

Sub-ppm short-chain fatty acids detection with La(III) modified SnO2 gas sensors

La(III) modified nanocrystalline SnO2 with lanthanum content [La]/([Sn] +[La]) = 0; 0.5; 1 and 2 mol. % were prepared by flame spray pyrolysis and characterized by X-ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) analysis, Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Identification of surface segregation of La(III) containing phases was performed via Raman spectroscopy. Acidic properties of pure and La(III) modified SnO2 were characterized by temperature-programmed desorption of ammonia (TPD-NH3) with mass spectral determination of desorption products. It was demonstrated that decrease in the Lewis acidity of the SnO2 surface leads to the specific increase in sensor response when detecting gases with acidic properties like short-chain fatty acids (SCFAs) and H2S. The obtained materials are capable to detect SCFAs at ppb concentration level, including high air humidity conditions (RH = 80 %).

Research on Wearable Devices Based on the Internet of Things

Wearable devices are closely related to people's lives. This project is based on WIFI wireless communication transmission technology, supplemented by Cortex-M3 microprocessor and MQ-7 gas sensor to design a wearable device. This wearable device can not only achieve basic clock timing, but also achieve functions such as CO concentration detection, voice intelligent recognition, and automatic alarm. In addition, the device adopts an embedded design and adds blockchain technology to achieve data information security and privacy protection.

Development of High-Precision NO2 Gas Sensor Based on Non-Dispersive Infrared Technology.

Increasing concerns about air quality due to fossil fuel combustion, especially nitrogen oxides (NOx) from marine and diesel engines, necessitate advanced monitoring systems due to the significant health and environmental impacts of nitrogen dioxide (NO2). In this study, a gas detection system based on the principle of the non-dispersive infrared (NDIR) technique is proposed. Firstly, the pyroelectric detector was developed by employing an ultra-thin LiTaO3 (LT) layer as the sensitive element, integrated with nanoscale carbon material prepared by wafer-level graphics technology as the infrared absorption layer. Then, the sensor was hermetically sealed using inert gas through energy storage welding technology, exhibiting a high detectivity (D*) value of 4.19 × 108 cm·√Hz/W. Subsequently, a NO2 gas sensor was engineered based on the NDIR principle employing a Micro Electro Mechanical System (MEMS) infrared (IR) emitter, featuring a light path chamber length of 1.5 m, along with integrated signal processing and software calibration algorithms. This gas sensor was capable of detecting NO2 concentrations within the range of 0-500 ppm. Initial tests indicated that the gas sensor exhibited a full-scale relative error of less than 0.46%, a limit of 2.8 ppm, a linearity of -1.09%, a repeatability of 0.47% at a concentration of 500 ppm, and a stability of 2% at a concentration of 500 ppm. The developed gas sensor demonstrated significant potential for application in areas such as industrial monitoring and analytical instrumentation.

Rare Earth Material for Hydrogen Gas Sensing: PtGd Alloy Thin Films as a Promising Frontier.

At the focus of our investigation lies the precision fabrication of ultrathin platinum-gadolinium (PtGd) alloy films, with the aim to use these films for resistive hydrogen gas sensing. The imperative for sensitive and selective sensors to harness hydrogen's potential as an alternative energy source drives our work. Applying rare earth materials, we enhance the capabilities of hydrogen gas sensing applications. Our study pioneers PtGd alloy thin films for hydrogen gas sensing, addressing a gap in existing literature. Here, we demonstrate the functional characteristics of 2 nm thick PtxGd100'x (x = 25, 50 and 75) alloy films, analyzing their hydrogen gas sensing properties, comprehensively examining the interplay between alloy composition, temperature fluctuation and hydrogen concentration. The effect of composition and structural properties on the sensing response were assessed using EDX and XPS. The films are tested at a temperature range between 25 °C and 150 °C with hydrogen gas concentrations ranging from 10 ppm to 5%. Hydrogen gas sensing mechanisms in PtGd alloy ultrathin films are explained by surface scattering. The unique combination of Pt and Gd offers promising characteristics for gas sensing applications, including high reactivity with hydrogen gas and tunable sensitivity based on the alloy composition.

High-performance plasmonics nanostructures in gas sensing: a comprehensive review

Plasmonic nanostructures have emerged as indispensable components in the construction of high-performance gas sensors, playing a pivotal role across diverse applications, including industrial safety, medical diagnostics, and environmental monitoring. This review paper critically examines seminal research that underscores the remarkable efficacy of plasmonic materials in achieving superior attributes such as heightened sensitivity, selectivity, and rapid response times in gas detection. Offering a synthesis of pivotal studies, this review aims to furnish a comprehensive discourse on the contemporary advancements within the burgeoning domain of plasmonic gas sensing. The featured investigations meticulously scrutinize various plasmonic structures and their applications in detecting gases like carbon monoxide, carbon dioxide, hydrogen and nitrogen dioxide. The discussed frameworks encompass cutting-edge approaches, spanning ideal absorbers, surface plasmon resonance sensors, and nanostructured materials, thereby elucidating the diverse strategies employed for advancing plasmonic gas sensing technologies.

A single gas sensor assisted by machine learning algorithms for breath-based detection of COPD: A pilot study

Chronic obstructive pulmonary disease (COPD) is a widespread respiratory disorder with a high mortality rate. Current diagnostic methods have limitations, necessitating innovative diagnostic approaches. Breath analysis using electronic noses (e-noses) is promising but still faces challenges. The single temperature-modulated sensor (STMS) is one of the optimized models of e-noses. It uses a single sensor instead of an array of multiple sensors, which enhances commercial viability and ease of use. This pilot study explores the feasibility of using a STMS device for COPD detection. Breath samples were collected from 34 healthy individuals and 33 COPD patients. Features extracted from the sensor's response were analyzed, and a novel feature selection method was developed to discriminate between the two groups. Using this method and a linear support vector machine (SVM) classifier, a single commercial sensor, driven with half-sine-staircase and staircase heater waveforms, achieved 80.60 % accuracy, 78.79 % sensitivity, and 82.35 % specificity in differentiating between COPD patients and healthy controls. These results demonstrate the potential of an appropriately configured STMS to capture comprehensive breath information for COPD detection. Furthermore, our study highlights how advancements in machine learning can maximize the effectiveness of this approach. These promising pilot study results warrant further investigation with larger, more diverse cohorts to validate this approach for broader clinical implementation.

Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study

Abstract. In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often “black boxes”, which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution.

Luminescence efficiency and carrier dynamics for InGaAs/GaAs surface quantum dots in coupled heterostructures

InGaAs/GaAs surface quantum dot (SQD) heterostructures have long been viewed as having great potential for realizing environmental gas detection devices. The research has recently been steered into discovering ways to inject carriers into the available SQD energy states to facilitate readable devices. In the present research, carrier injection from buried QDs (BQDs) to SQDs was controlled by changing the interlayer coupling. Photoluminescence (PL) measurements show that, by increasing the stacking period of BQDs, the carrier collection efficiency and subsequently the luminescence intensity for SQDs can be effectively improved. A rate equation simulation of the carrier recombination process is used to quantitatively describe the luminescent quantum efficiency (IQE) for the SQDs, indicating an increase with additional BQD layers due to an increase in carrier injection. The PL spectra along with the rate equation simulations further indicated that for these SQD hybrid structures nonradiative recombination dominates, with Auger recombination becoming significant at high excitation intensities. These results may help to understand the carrier dynamics in coupled SQDs hybrid structures. In addition, they provide a path to manipulating the properties of InGaAs SQDs for the development of gas sensors.

Differential detection of NH3 and NO2 by polymer non-covalently encapsulated semiconducting single-walled carbon nanotubes

Semiconductor single-walled carbon nanotubes (sc-SWCNTs) exhibit exceptional optoelectronic properties and possess a substantial specific surface area, rendering them highly promising materials for the development of gas sensors with increasing the sensitivity and portability. In this study, we synthesized polymer bearing two Schiff base side chains (PF2L) via the Suzuki coupling reaction with enhanced polymer solubility. The sc-SWCNTs were selectively sorted using PF2L from commercially available SWCNTs to prepare PF2L/sc-SWCNTs composites. The incorporation of side chains enhanced the π-π interactions within the polymers and facilitated the interactions between the polymers and SWCNTs, thereby promoting superior dispersion of sc-SWCNTs. The PF2L/sc-SWCNTs-based sensors were fabricated using the drop-coating method, and their gas sensing performance was evaluated for gases with distinct properties (reducing: NH3 and oxidizing: NO2). The sensor exhibits a response of 2.415 % and 14.85 % for 1 ppm NH3 vapor and 1 ppm NO2, respectively. The sensor shows response and recovery times of 50 s and 220 s, respectively, for the detection of 1 ppm NH3 vapor, while it demonstrated response and recovery times of 59 s and 733 s for the detection of 1 ppm NO2. Subsequently, we developed an LED-based detection device for the selective discrimination of NH3 vapor and NO2, thereby offering valuable insights into the potential practical applications of a single material for dual gas sensing.

E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction.

In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.

Exploring the diverse performance of nickel and cobalt spinel ferrite nanoparticles in hazardous pollutant removal and gas sensing performance.

A simple sol-gel combustion process was employed for the creation of MFe2O4 (M=Ni, Co) nanoparticles. The synthesized nanoparticles, acting as both photocatalysts and gas sensors, were analyzed using various analytical techniques. MFe2O4 (M=Ni, Co) material improved the degradation of methylene blue (MB) under UV-light irradiation, serving as an enhanced electron transport medium. UV-vis studies demonstrated that NiFe2O4 achieved a 60% degradation, while CoFe2O4 nanostructure exhibited a 76% degradation efficacy in the MB dye removal process. Furthermore, MFe2O4 (M=Ni, Co) demonstrated chemosensitive-type sensor capabilities at ambient temperature. The sensor response and recovery times for CoFe2O4 at a concentration of 100ppm were 15 and 20, respectively. Overall, the synthesis of MFe2O4 (M=Ni, Co) holds the potential to significantly improve the photocatalytic and gas sensing properties, particularly enhancing the performance of CoFe2O4. The observed enhancements make honey MFe2O4 (M=Ni, Co) a preferable choice for environmental remediation applications.

Ultrathin WO3 Nanosheets/Pd with Strong Metal-Support Interactions for Highly Sensitive and Selective Detection of Mustard-Gas Simulants.

Exposure to mustard gas can cause damage or death to human beings, depending on the concentration and duration. Thus, developing high-performance mustard-gas sensors is highly needed for early warning. Herein, ultrathin WO3 nanosheet-supported Pd nanoparticles hybrids (WO3 NSs/Pd) are prepared as chemiresistive sulfur mustard simulant (e.g., 2-chloroethyl ethyl sulfide, 2-CEES) gas sensors. As a result, the optimal WO3 NSs/Pd-2 (2 wt % of Pd)-based sensor exhibits a high response of 8.5 and a rapid response/recovery time of 9/92 s toward 700 ppb 2-CEES at 260 °C. The detection limit could be as low as 15 ppb with a response of 1.4. Moreover, WO3 NSs/Pd-2 shows good repeatability, 30-day operating stability, and good selectivity. In WO3 NSs/Pd-2, ultrathin WO3 NSs are rich in oxygen vacancies, offer more sites to adsorb oxygen species, and make their size close to or even within the thickness of the so-called electron depletion layer, thus inducing a large resistance change (response). Moreover, strong metal-support interactions (SMSIs) between WO3 NSs and Pd nanoparticles enhance the catalytic redox reaction performance, thereby achieving a superior sensing performance toward 2-CEES. These findings in this work provide a new approach to optimize the sensing performance of a chemiresistive sensor by constructing SMSIs in ultrathin metal oxides.

Elucidation of structural, electromagnetic, and optical properties of Cu–Mg ferrite nanoparticles

Copper doped magnesium ferrite, Mg1-xCuxFe2O4(x = 0.0–1.0) nanomaterials were synthesized via. sol-gel method sintered at 600 °C for 2 h. The synthesized materials were characterized using modern sophisticated techniques viz. X-ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy, Energy dispersive x-ray spectroscopy (EDS), Vibrating sample magnetometer, UV–visible diffuse reflectance spectra and Impedance analyzer. XRD analysis revealed that all the samples were single phase cubic spinel structure with Fd3m space group and investigated the change in structural parameters with copper concentration. The average crystallite size in the range of 11–23 nm and lattice parameters decrease with increasing Cu doping, due to the cationic distribution and ionic radius. The SEM images show the agglomeration of the particles with spherical like shape and elemental percentage were obtained from EDX. The saturation magnetization showed an increasing trend with increasing Cu concentration at a certain level and then decreases due to the rearrangement of cations at tetrahedral and octahedral sites. The Coercivity, Retentivity and magnetic crystalline anisotropy increase with changing dopant concentration. The magnetic measurements showed enhanced saturation magnetization at certain level (28.96emu/gm) and increase in coercivity up to 1102 Oe with changing dopant concentration. The estimated band gap energy is found to increase with Cu content. The dielectric constant, dielectric loss and impedance show normal behavior of ferrite. The frequency dependent dielectric constant decrease and tan delta shows a relaxation behavior at low frequencies. The synthesized nano Mg–Cu nanoparticles will be applied as humidity sensor, gas sensor, microwave devices and photocatalyst.

High-Precision Low-Cost Mid-Infrared Photoacoustic Gas Sensor Using Aspherical Beam Shaping for Rapidly Measuring Greenhouse Gases

A high-precision low-cost mid-infrared photoacoustic sensor for greenhouse composite gases based on aspherical beam shaping is proposed and demonstrated. The assembled optical source module and luminous characteristics of infrared source are innovatively investigated and analyzed with aspherical beam shaping. The proposed aspherical-beam-shaping-technique could effectively reduce optical loss and enhance system sensitivity, achieving an effective power utilization ratio of a radiation source of 91% and sidewall noise ratio of 8.9%. Experiments verify the 1.7 times improvement in responsivity and 50% enhancement in minimum detection limit (MDL) on average. In terms of comprehensive greenhouse gas composites and with short integration time of 1 s, MDLs of CO2, CH4, N2O, NF3, SF6, PFC-14, and HFC-134a are 73 ppb, 267 ppb, 72 ppb, 81 ppb, 14 ppb, 9 ppb and 115 ppb, respectively. Furthermore, a 48 h continuous monitoring of H2O, CO2 and CH4 in the atmosphere is conducted and verifies the performance of the gas sensor. The developed sensor allows for the rapid route of low-cost and high-precision detection of multiple greenhouse gases.

IMPLEMENTATION OF BIOGAS EQUIPMENT USING ARDUINO MEGA 2560 WITH SENSOR MQ-4

The biogas in the research that the authors do is a mixture of cow dung with 1: 2 water, where the biogas in this study is carried out by anaerobic fermentation or without air, a mixture of cow dung and water is put into the reactor and the fermentation process is carried out for 12 days to prove whether biogas In this study, gas will be stored in storage (biogas bag 1 and biogas bag 2. The research method used, namely conducting literature studies, exploration, determining equipment specifications, designing and implementing and testing. In designing this tool, it is able to produce gas from the biogas fermentation process, namely methane gas which can be detected by a functioning methane gas sensor. Where there is an increase in PPM testing between the first MQ-4 sensor from 5246.36 and the second MQ-4 sensor detected at 5667.23 PPM with the adjusted difference of 420.87 PPM, so the methane gas is tested with a stove and a lighter. The MQ-4 sensor measurement error results reached 5.63%. Keywords: Reaktor fixed dome, MQ-4, Gas Metana, Monitoring

Simultaneously Engineering Oxygen Defects and Heterojunction into Ho-Doped ZnO Nanoflowers for Enhancing n-Propanol Gas Detection.

Lung cancer poses a serious threat to people's lives and health due to its high incidence rate and high mortality rate, making it necessary to effectively conduct early screening. As an important biomarker for lung cancer, the detection of n-propanol gas suffers from a low response value and a high detection limit. In this paper, flower-like Ho-doped ZnO was fabricated by the coprecipitation method for n-propanol detection at subppm concentrations. The gas sensor based on the 3% Ho-doped ZnO showed selectivity to n-propanol gas. Its response value to 100 ppm n-propanol was 341 at 140 °C, and its limit of detection (LOD) was about 25.6 ppb, which is lower than that of n-propanol in the breath of a healthy person (150 ppb). The calculation results show that the adsorption of n-propanol on a Ho-doped ZnO surface releases more energy than isopropanol, ethanol, formaldehyde, acetone, and ammonia. The enhanced gas-sensing properties of the Ho-doped ZnO material can be attributed to the fact that the Ho-doping distorts the crystal lattice of the ZnO, increases the specific surface area, and generates a large amount of oxygen defects. In addition, the doped Ho partially forms a Ho2O3/ZnO heterojunction in the material and improves the gas-sensing properties. The 3% Ho-doped ZnO material is expected to be a promising candidate for the trace detection of n-propanol gas.

Highly sensitive formaldehyde gas sensor based on SnO2/Zn2SnO4 hybrid structures

Formaldehyde, a common indoor air pollutant emitted from building decoration materials, poses a significant health hazard, potentially leading to cancer. Therefore, developing a gas sensor capable of real-time monitoring of indoor formaldehyde concentration is essential. Herein, a metal oxide semiconductor gas sensor with SnO2/Zn2SnO4 hybrid structure as the sensitive material has been successfully fabricated. The sensor exhibits the ability to detect formaldehyde down to 50 ppb, meeting the safety concentration limit proposed by the World Health Organization (WHO). Additionally, it displays rapid response characteristics, with a response time of 84 s and a recovery time of 46 s for 50 ppb of formaldehyde at 200 °C, respectively. Moreover, the sensor demonstrates good selectivity and long-term stability, making it promising for real-time monitoring of indoor air pollutants. Furthermore, the evolution process of intermediates in the sensing reaction was investigated by in-situ diffuse reflaxions infrared fourier transformations spectroscopy, providing a comprehensive understanding of the surface chemistry. This in-depth comprehension offers valuable insights into the mechanisms underlying formaldehyde sensing.