Next Article in Journal
Analysis of Failure Cause in Steel Wire-Reinforced Thermoplastic Composite Pipes for Sour Gas Field Water Transportation
Previous Article in Journal
Method to Predict Salt Expansion Deformation in Cement-Stabilized Macadam Under Sulfate Attack Based on Pore Evolution
Previous Article in Special Issue
Structural Engineering of Bimetallic CoCe-ZIF Derives Catalysts with Optimized Electronic Structure for Enhanced Oxygen Electrocatalysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Materials
Volume 18
Issue 21
10.3390/ma18214864
materials-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Recent Progress in Low-Power-Consumption Metal Oxide Semiconductor Gas Sensors

by
Yu Zhang
1,
Renbo Li
2,3,
Ruqi Guo
2,3,
Mingzhi Jiao
2,3,*,
Gang Wang
2,3,* and
Zhikai Zhao
2,3,*
1
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
National & Local Joint Engineering Laboratory for Internet Application Technology in Mines, Xuzhou 221116, China
3
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Materials 2025, 18(21), 4864; https://doi.org/10.3390/ma18214864
Submission received: 15 September 2025 / Revised: 14 October 2025 / Accepted: 20 October 2025 / Published: 24 October 2025
(This article belongs to the Special Issue Advanced Applications of Electrochemical Materials for Sensors and Catalysts)
Browse Figures
Versions Notes

Abstract

Metal oxide semiconductor (MOS) gas sensors offer several advantages, including low cost, high accuracy, and ease of miniaturization. Thus, they are excellent candidates for environmental monitoring and food spoilage detection applications, particularly in the safe Internet of Things field or for portable instruments. Typically, there are two general routes for realizing low-power-consumption MOS gas sensors: room-temperature MOS gas sensors or MEMS MOS gas sensors. The review focuses on the detection of four typical gases, namely methane, hydrogen, carbon monoxide, and nitrogen dioxide, systematically summarizing and analyzing the most recent results of low-power-consumption MOS gas sensors. The 2D materials, MOS composites, and 3D structured composites exhibit excellent room-temperature gas detection capabilities. The mechanism of the room-temperature gas sensors is also discussed in detail. Another route is MEMS MOS gas sensors. First, the progress of the micro-hotplate research is introduced. Then, several of the latest reported MEMS MOS gas sensors are shown and compared. The gas sensing mechanism of these MEMS MOS gas sensors is also given. The paper will provide a valuable guide for researchers in the MOS gas sensor field, particularly for those working towards low-power-consumption MOS gas sensors.

1. Introduction

As the economy grows, more hazardous gases can be found in the outdoors or in factories. Methane can be found in coal mines or the kitchen. It can give rise to an explosion or severe combustion in the event of a leak. Furthermore, methane contributes much more to the greenhouse effect than carbon dioxide. Hydrogen is a promising fuel considering its high efficiency and cleanliness. However, it has a relatively wide explosion concentration range (from 4% to 75.6%). Additionally, nitrogen dioxide and carbon monoxide are two highly toxic gases that can cause severe harm to humans when humans are exposed to high concentrations for a short period. Thus, accurate monitoring of the above four gases is necessary to ensure city safety. Several gas sensor technologies, including optical, electrochemical, metal oxide semiconductor resistive, and catalytic combustion gas sensors, have been utilized for gas safety monitoring. Metal oxide semiconductor (MOS) resistive gas sensors offer advantages such as low cost, ease of miniaturization, high accuracy, and a low detection limit, making them widely applicable in safety monitoring [1]. However, MOS gas sensors generally consume high power due to heater integration, casting a challenge for safe Internet of Things applications or portable devices. Low-power-consumption MOS gas sensors are gaining increasing attention, particularly in light of the power constraints in many applications. Core technologies for low-power-consumption gas sensors can be categorized into two main types: room-temperature solutions and MEMS-based designs [2,3,4]. Both approaches achieve energy efficiency through material innovation and structural-material optimization, respectively, accelerating the development of low-power-consumption gas sensors. The rest of the paper is organized in the following way: First, the progress of room-temperature gas sensors is introduced, with focus on the materials innovation and working mechanism discussion; Secondly, the recent development of MEMS gas sensors is described, with emphasis on micro-hotplate design and sensing materials; Thirdly, the challenge and prospects of low-power-consumption MOS gas sensors are discussed. Ultimately, the conclusion is presented regarding the current state of the art of the two solutions for low-power-consumption MOS gas sensors.

2. Room-Temperature Gas Sensors

Traditional metal oxide gas sensors require operation above room temperature, with heaters being the primary source of power consumption. These devices also pose safety risks due to the detection of flammable and explosive gases, as well as issues such as time-consuming preheating and significant material heat loss. In contrast, room-temperature gas sensors operate without heating, offering advantages such as no preheating required, low power consumption, minimal material heat loss, a simple structure, and reduced high-temperature hazards. These outstanding benefits have attracted considerable attention from researchers, who have conducted extensive studies on room-temperature gas sensors, with a particular focus on detectors for methane (CH4), hydrogen (H2), carbon monoxide (CO), and nitrogen dioxide (NO2). NO2 is a typical oxidative gas, while the other three gases are reductive. The four gases are chosen due to their wide existence in environmental and industrial scenarios.

2.1. Room-Temperature CO Sensors

Carbon monoxide is a rather lethal gas, causing severe death in a few minutes when the concentration is higher than 1200 ppm. A review by He et al. has summarized and compared many relevant works before 2024 [5]. Here, we will introduce some excellent work on room-temperature CO sensors published after 2024. Noble metal decoration can decrease the working temperature of metal oxide semiconductors. For example, Pd-CuO-SnSe2 can respond to 200 ppm CO in 13 s [6]. It is reported that 1 wt% Pt-SnO2 nanoceramics can react with 400 ppm CO at room temperature, yielding a response value of 2427 [7]. Wu et al. reported that 0.1Au-ZnO can respond to 100 ppm CO at room temperature in 61 s with a value of 139.75 [8]. The sensing mechanism of Au-ZnO can be explained in two aspects: the Schottky barrier and the catalytic function of Au. Since the work function of Au is lower than that of the conduction band of ZnO, electrons at the conduction band of ZnO will flow into the surface of Au and form Schottky barriers, enriching the number of electrons in Au and enlarging the surface depletion layer of ZnO. Schottky barriers will amplify resistance variations and inhibit electron-hole recombination, as shown in Figure 1a. Au with a higher electron density can enhance oxygen adsorption and dissociation, as shown in Figure 1b,c. Au will facilitate oxygen adsorption when the Au-ZnO is in the air. In CO, Au from the composite enhances the adsorption and dissociation of CO, thereby increasing the CO surface reaction and sensing. CuO-SnO2 nanotubes were synthesized by assembly on the carbon nanotube templates, followed by subsequent calcination [9]. The optimal sensor can respond to 300 ppm in 56 s. The response value can be as high as 1.34, with an ultra-low detection limit of 159 ppb. Xie et al. successfully synthesized SnO2-NiO through the MOF template [10]. The sensor exhibits a response value of 5.48 toward 100 ppm CO in 56 s, with a detection limit as low as one ppm CO. The enhanced sensing performance of SnO2-NiO can be attributed to the p-n heterojunction effect, as seen in Figure 1d. When the material is exposed to air, oxygen will trap electrons from the conduction band, increasing the number of hole carriers and forming a hole stacking layer, which will reduce the resistance of the sensor. When the sensor is exposed to CO, the trapped electrons recombine with the holes, causing the hole stacking layer to become thinner and the depletion layer to become thicker, which in turn increases the sensor resistance, as shown in Figure 1d.
Furthermore, NiO-Ti3C2Tx (MXene) composite was reported to respond to 400 ppm CO in only 8 s, with a response value of 1.3 [11]. The excellent sensing properties of NiO-Ti3C2Tx are due to the superior characteristics of Ti3C2Tx. The outer surface of Ti3C2Tx can be functionalized by different groups such as -F, -OH, or =O [12,13]. These groups can serve as attachment sites for surface-functional molecules, thereby enhancing the selectivity and sensitivity of the gas sensors. Ti3C2Tx with -F and -OH groups have significantly different band gaps as well (0.72 eV for -F and 1.07 eV for -OH). In addition, Ti3C2Tx has a room temperature conductivity of up to 10,000 S cm−1 [14,15].
As shown in Table 1, carbon-based composites exhibit good sensitivity and response speed to CO. For example, MWCNT/SnO2 composites can react to 300 ppm CO in 5 s with a response value of 1.80 [16]. MWCNT and SnO2 will form a p-n junction when the two come in contact, since MWCNT is a p-type material and SnO2 is a typical n-type semiconductor. Furthermore, rGO-wrapped SnS2 nanospheres can respond to 10 ppm CO in 11 s as well, with a response value as high as 10 [17]. On the one hand, a heterojunction between n-type SnS2 and p-type rGO will enhance the CO response; on the other hand, the presence of numerous defect sites on the rGO surface will further increase the CO response.

2.2. Room-Temperature NO2 Sensor

Recently, 2D materials such as reduced graphene oxide composites, Mxene, and transition metal dichalcogenides have been reported to detect NO2 with excellent performance, even at room temperature. For example, rGO/CuO nanoflakes can respond to 5 ppm NO2 at room temperature in only 6.8 s, with a response value of 1.26 [20]. rGO/In2O3 was found to have an ultrahigh response value of 1177 toward one ppm NO2 [21]. In2O3/Ti3C2 nanosheets can respond to 100 ppm NO2 in 18 s, with a response value of 371.19 [22]. The mechanism for room-temperature NO2 sensing from In2O3/Ti3C2 is attributed to the Schottky barrier, as shown in Figure 2.
A detailed comparison of the room-temperature NO2 sensing performance of 2D materials, as recently reported, is presented in Table 2. Even though In2O3/Ti3C2 nanosheets can respond to 100 ppm NO2 in 18 s, the recovery time is as long as 95 s. The prolonged recovery time of the materials is attributed to intense interactions between NO2 and the In2O3/Ti3C2 nanosheets, which pose challenges for the desorption of NO2 gas.
Except for 2D materials, traditional metal oxides and sulfides are being explored for NO2 sensing, offering high and fast responses. Notably, MoO3@CoMoO4@CoMoS3.13 can respond to 40 ppm NO2 in only 1.1 s, with a response value as high as 35.42 [35]. A possible NO2 sensing mechanism was proposed as follows: (1) NO2 is first absorbed onto the surface layer (CoMoO4@CoMoS3.13) of the nanocomposite containing S-modified vacancies to contact with Co sites. (2) The electron is then sucked by the NO2 molecule and reacts with the chemisorbed oxygen (2NO2 (gas) + O2− (ads) + 2e− →2NO3− (ads)). (3) A full recovery of the resistance is accompanied with the removal of NO2 from the sensing system, realizing the return of the electron to material. Recently, Zhang et al. found that ZnInS4 can respond to 10 ppm NO2 in only 2 s, with a response value of 3.28 [36]. The excellent room-temperature NO2 sensing properties of ZnInS4 may result from the presence of rich sulfur vacancies. The breakneck response/recovery speed is due to the open structure of ZnIn2S4/MIL-68(In), which promotes the adsorption and diffusion of NO2 molecules.
Tellurium (Te), a 2D elemental material, is promising for NO2 detection due to its suitable band structure for gas adsorption and charge mobility. However, Pure Te materials have poor stability due to their high activity, which limits their application in gas sensor fields. Core−shell 2D Te@Se heterostructures are prepared using a solvothermal method [37]. The Te@Se heterostructures with a thickness of 4–6 nm of Se demonstrate an exceptionally high response of 7.22 to 1 ppm of NO2 at room temperature, with ultrafast response/recovery times of 10 s and 30 s, respectively. The room-temperature NO2 sensing performance of Te@Se depends strongly on the thickness of the Se shell, as seen in Figure 3a–c. Three cases may happen: (1) When the Se shell is too thin, the electron accumulation layer in Se is smaller than the theoretical thickness, leading to a minimal electron migration effect and resistance modulation ability (as shown in Figure 3a); (2) When the thickness of the Se shell exceeds that of the electron accumulation layer, the electrons in the accumulation layer must pass through the Se shell to bind with NO2 (Figure 3c), resulting in lower electron mobility in Se. Consequently, the efficient transfer of accumulated electrons to NO2 is hindered. (3) The resistance modulation effect from electron migration is maximized only when the thickness of the Se shell matches that of the electron accumulation layer, as seen in Figure 3b. As a result, the highest NO2 response can be achieved in this case. Furthermore, SnO2-decorated Te nanotubes are found to respond to 600 ppb NO2 in 32 s, with a response value of 1.33 [38]. The mechanism for enhanced sensor performance can be attributed to Schottky barriers, as shown in Figure 3d. When the material is exposed to NO2, the adsorbed NO2 can form adsorbed NO2− through either capturing electrons from SnO2 decorated Te or reacting with surface O2−, as illustrated in Figure 3e. A detailed comparison of related materials can be found in Table 3.

2.3. Room-Temperature H2 Sensor

H2 is attracting increasing attention due to its excellent properties, including high efficiency, cleanliness, and renewability. However, it has a relatively wide explosion limit (4~75%), causing a significant threat to human and city safety. Room-temperature H2 sensing is particularly meaningful for avoiding the heating risk associated with the sensor itself [42,43,44]. Three-dimensional (3D) structure nanomaterial composites have been investigated for rapid room-temperature H2 sensing. For example, Chen et al. successfully synthesized a 3D In2O3-rGO-PPy composite aerogel using a hydrothermal method [45]. It can respond to 1000 ppm H2 in 13 s with a response value of 11.6. The mechanism is as follows: The oxygen gas will form chemisorbed oxygen ions (O2−) when oxygen molecules are adsorbed on the surface of the 3D In2O3-rGO-Ppy at room temperature. After H2 exposure, the oxygen ions on the surface react with H2 to form H2O, as shown in Figure 4a.
Furthermore, Cactus-like ZnO@three-dimensional reduced graphene oxide aerogels (Cactus-like ZnO@3D rGA) respond to 10,000 ppm H2 in 15 s, achieving a response value of 81.07 [46]. The enhanced sensing performance is attributed to the p-n heterojunction between ZnO (n-type with a work function of 4.25 eV) and rGO (p-type with a work function of 4.86 eV), as shown in Figure 4b. When ZnO is in contact with rGO, electrons from ZnO will be transferred to rGO. In contrast, the holes in rGO will be transferred to ZnO in the opposite direction. The superior room temperature sensing behavior of ZnO-rGO toward H2 can be attributed to two reasons: on the one hand, rGO can increase the conductivity of the composites and form a heterojunction with ZnO; on the other hand, 3D rGO possesses a pore structure, which facilitates the adsorption and desorption of gases. Additionally, the porous structure can increase the number of active sites for surface gas reactions. A summary of the performance of related materials is shown in Table 4.

2.4. Room-Temperature CH4 Sensor

Li et al. created Pt-doped SnO2/ZnO bilayer structures showing CH4 response at room temperature [52], attributed to the combined effects of n-n heterojunction and Schottky barrier, as depicted in Figure 5a. Optimal sensitive materials: 1.0Pt-SnO2/ZnO exhibits prolonged response and recovery times exceeding 100 s for a 2000 ppm CH4 concentration. Yang et al. designed In2O3-ZnO/laser-induced graphene composites for room-temperature CH4 sensors, achieving a 27.48% response at 500 ppm CH4 with response/recovery times of 48/169 s, respectively, and a detection limit as low as 3 ppm [53]. Li et al. prepared Ag-Ru co-doped ZnO nanorod arrays, enabling CH4 detection at room temperature [54]. As seen in Figure 5b, the work function of ZnO is 4.44 eV, while that of Ag/ZnO and AuRu0.025/ZnO is 4.51 and 5.03 eV, respectively. The Fermi level in Ag-ZnO contributes to the enhanced oxygen vacancy density, and that in AuRu0.025-ZnO proves the role of p-type lattice defects. The localized contact between Ag-ZnO/AuRu0.025-ZnO supports the experimental result that the electrons are transferred from the Ag−Ru co-doping site to the nearby Ag-doping site, reducing Ag+ to Ag0 in AgRu0.025-ZnO. The localized contact between ZnO/Ag-ZnO reveals that more electrons accumulate in the Ag-doping sites, which act as activity sites for O2 chemical adsorption.
Detailed sensing information of related materials is presented in Table 5. To be noteworthy, UV light activation can be an effective route to enhance the methane sensing performance at room temperature [55,56]. For example, AuAg/ZnO can respond to 5000 ppm CH4 in 5 s, with a response of 62.61 [56]. The conductivity of ZnO will increase under UV illumination since the electrons of ZnO will be excited from the valence band to the conduction band. Oxygen molecules will trap photogenerated electrons adsorbed on the ZnO surface, forming reactive oxygen species and creating an electron-depletion layer. Upon CH4 exposure, CH4 molecules react with oxygen ions, causing electrons to return to ZnO and reducing resistance.
To summarize, despite significant advancements in related research, several practical application shortcomings persist for this type of sensor, including susceptibility to humidity, prolonged response times, reduced sensitivity to low gas concentrations, and poor selectivity for target gases. It is not competitive enough in scenarios with high requirements on stability and sensitivity.

3. MEMS Gas Sensor

MEMS sensors, based on microelectromechanical systems (MEMS) technology, are typically compact (1 μm to 1 mm in size) and feature unique manufacturing processes that enable the easy construction of gas sensing arrays or grids. These sensors offer several significant advantages, including high sensitivity, rapid response times, low power consumption, and easy integration. By developing and refining sensitive materials with varying characteristics, they can effectively detect different gases. Hadano et al. developed a MEMS catalytic combustion CH4 gas sensor featuring platinum-loaded catalysts as reference elements to enhance methane selectivity, while improving CH4 sensitivity through structural modifications, such as enhancements to the micro-thermal plate design [62]. Yang et al. fabricated a quadri-lateral MEMS MOS gas sensor cell with four different sensitive materials based on a complete semiconductor process, as seen in Figure 6a–d. Jang et al. designed a MEMS catalytic combustion micro-gas sensor for leak detection [63]. Measuring just 3 mm × 3 mm, this sensor achieves an output voltage of 0.29 mV at 1158 ppm CH4 and 104 mW of input power, demonstrating both low power consumption and excellent selectivity.
MEMS catalytic combustion sensors exhibit relatively high power consumption and are susceptible to poisoning effects. In contrast, MEMS metal oxide sensors offer several advantages, including lower-power consumption, higher sensitivity, lower detection limits, greater material modification potential, and improved stability. Yan et al. designed an intelligent MEMS sensor based on FeCoNi oxidation of an entropy alloy, which reduces power consumption via a pulse-heating mode [64]. At 150 °C, it shows excellent selectivity for H2 and CO with detection limits of 0.3 ppm and 0.29 ppm, respectively. Yang et al. made a quadrilateral MEMS metal oxide gas sensor cell with four different sensitive materials based on a complete semiconductor process, as shown in Figure 6a–d [65]. Li et al. proposed a MEMS CO sensor based on SnO2 nanosheets, as shown in Figure 6e,f [66]. Figure 6g compares the sensor’s response to CO under constant-temperature and pulse-heating modes, revealing a response of 34.3 at 200 ppm CO in pulse mode—1.56 times that in the constant-temperature mode—with approximately 19.8 mW of power consumption. Luo et al. developed a single MEMS sensor based on SnO2 nanosheets [67]. At 370 °C, this sensor achieves a response of 297.7 ppm to H2, with power consumption as low as 26.6 mW in pulse-heating mode. Wu et al. proposed a H2 sensor based on Pd-loaded SnO2 nanofibers, demonstrating an excellent response to 200 ppm H2 at 185 °C (Figure 6h), with a power consumption of only 9.1 mW [68]. Figure 6i compares the response values of self-made S1, S2, and S3 sensors with those of the commercial MICS-5524 sensor at 185 °C for H2 concentrations ranging from 200 to 8000 ppm, showing that the self-made sensors outperform the commercial sensor at high concentrations. Li et al. proposed a MEMS differential thermoelectric stack hydrogen sensor with response and recovery times of 14 s and 10 s, respectively, at 400 ppm H2, and a power consumption of only 61 mW [69].
In summary, MEMS metal oxide gas sensors achieve rapid response to target gases with low power consumption in gas detection by integrating micro-structured designs with suitable sensitive materials. Current research on low-power-consumption MEMS metal oxide gas sensors primarily focuses on two aspects: First, optimizing the sensor’s micro-hotplate structure—high thermal conductivity silicon-based microstructures reduce heat loss and lower power consumption while maintaining appropriate temperatures. Second, improving sensitive materials through nanostructuring to enhance specific surface area for gas adsorption efficiency, or constructing composite metal oxide systems to optimize selectivity and stability while reducing baseline drift during prolonged operation. These synergistic advancements collectively reinforce the core advantages of MEMS sensors—“low power consumption, high responsiveness, and miniaturization”—forming an essential foundation for functional and performance breakthroughs.
Materials 18 04864 g006
Figure 6. (a–d) Fabrication steps of the proposed gas sensor cell [65]: (a) Supporting layer by LPCVD; (b) Four sensing material deposition and patterning; (c) Heater and detecting electrodes patterning; (d) Dry-etching processes for releasing suspended structures. (e) Optical image of the SnO2 nanosheet on the MEMS hotplate [66]; (f) SEM of the SnO2 nanosheet on the MEMS hotplate [66]; (g) Sensor response to CO under different modes (constant temperature vs. pulse heating [66]). (h) Response of Pd-loaded SnO2 nanofiber sensors at 2000 ppm H2 exposure under varying temperatures [68]; (i) Dynamic response curves of three S1, S2, and S3 labeled SnO2 nanofiber MHP gas sensors to H2 [68].
Figure 6. (a–d) Fabrication steps of the proposed gas sensor cell [65]: (a) Supporting layer by LPCVD; (b) Four sensing material deposition and patterning; (c) Heater and detecting electrodes patterning; (d) Dry-etching processes for releasing suspended structures. (e) Optical image of the SnO2 nanosheet on the MEMS hotplate [66]; (f) SEM of the SnO2 nanosheet on the MEMS hotplate [66]; (g) Sensor response to CO under different modes (constant temperature vs. pulse heating [66]). (h) Response of Pd-loaded SnO2 nanofiber sensors at 2000 ppm H2 exposure under varying temperatures [68]; (i) Dynamic response curves of three S1, S2, and S3 labeled SnO2 nanofiber MHP gas sensors to H2 [68].
Materials 18 04864 g006

3.1. Research Progress of MEMS Micro Hotplate

A micro hotplate (MHP) is a crucial component in MEMS gas sensors. It is based on silicon microfabrication technology, and its basic structure includes a silicon substrate, a suspended distributed support film, and a heater electrode. The power consumption of MHP is mainly affected by its materials and structure.
Yang et al. proposed a novel multi-heater MHP design based on thermal coupling [70]. Figure 7a shows the planar schematic of a dual-heater MHP, where heaters with a 100 μm radius achieve excellent temperature uniformity at 300 °C. The thermal coupling effect significantly reduces MHP power consumption, saving approximately 4.68 mW compared to single-heater configurations. Figure 7b illustrates the temperature distribution in MHPs with and without etched windows. The etched window design leverages air’s superior thermal insulation, increasing peak temperatures by about 40 °C while effectively reducing heat loss. Zhang et al. developed an MHP for wafer-level packaging of MEMS gas sensors using an ONO composite layer as support [71]. By analyzing different heater electrode shapes and cantilever beam materials, they found that serpentine electrodes produced the most uniform temperature distribution. The optimized MHP exhibits lower power consumption and enables the high-density integration of multiple independent, temperature-controlled MHPs.
Wei et al. introduced a planar MHP design combining Si3N4-SiO2 lateral composite dielectric layers with annular heaters (Figure 7c) [72]. The composite dielectric layer effectively reduces lateral heat conduction losses and mitigates mechanical deformation in planar microheat plates. Zhang et al. created a novel MHP array structure integrating three MHPs per suspended region (Figure 7d) [73]. This configuration enhances integration density and achieves independent temperature control. Using COMSOL simulations, they optimized the platinum heater thickness and adjusted the cantilever and electrode widths, effectively reducing crosstalk interference. We conducted finite-element analysis of two serpentine MHP platinum electrode configurations. Structure 1 features identical heating electrode width and spacing, while Structure 2 maintains uniform spacing but exhibits a gradually increasing micro-heater width from both sides toward the center. Figure 7e,f display temperature variations under different heating voltages for structures 1 and 2. Through COMSOL simulation analysis, it was found that under equivalent power consumption, Structure 2 demonstrates slightly higher maximum central temperature than Structure 1, while achieving lower power consumption at the same temperature levels [74].
Multiple researchers have optimized MHP structures by integrating heaters, selecting appropriate support layers and electrode configurations, introducing composite dielectric layers, and designing arrays. These optimizations have demonstrated significant improvements in power efficiency, stability, selectivity, and integration density, providing diverse approaches for related optimization and applications. However, limitations remain: precise control of multi-heater thermal crosstalk may compromise detection accuracy; etching windows and composite dielectric layers could reduce structural strength, increasing fracture risk; cross-interference in arrays remains challenging to eliminate; and some designs involve complex processes that increase mass-production costs. These issues highlight the need to strike a better balance between structural stability, thermal interference control, and process simplification.

3.2. MEMS MOS Sensors Based on Various Sensitive Materials

In view of the problems encountered by MEMS gas sensors in practical applications, such as low response values and poor selectivity, this paper will summarize and analyze a series of MEMS gas sensors with excellent performance by further optimizing the sensitive materials for CO, NO2, CH4, and H2.

3.2.1. MEMS MOS CO Sensors

Carbon monoxide (CO), a primary hazardous gas in coal mines, originates from the spontaneous combustion of coal in mined-out areas and the incomplete burning of coal dust during mine gas explosions. This highly toxic, combustible substance can rapidly cause fatal consequences. Low-power-consumption CO sensors serve as critical safeguards for safe coal mining operations and form the essential foundation for distributed wireless sensing technologies. Comparison of the sensing performance of various MEMS MOS CO sensors can be found in Table 6.
In recent years, MEMS-based CO sensors have garnered significant attention in the field. Kim et al. synthesized Pd-SnO2 nanoscale powders as CO gas sensors using the sol–gel method, as shown in Figure 8a [75]. In CO-NH3 mixed gases, CO exhibits significantly higher sensitivity than NH3, with Pd catalyzing CO adsorption and reactions that dominate the changes in sensitivity. The CO response is highly humidity-dependent, as illustrated in Figure 8b. Moon et al. developed a MEMS CO gas sensor by co-precipitating 10 wt% WO3 and 0.5 wt% Pt-doped SnO2 nanosheets [76]. This sensor consumes approximately 15 mW of power and detects CO at concentrations as low as 2 ppm. It shows a response value of about 1.31 at 20 ppm CO, with response and recovery times of 91 s and 134 s, respectively. Egger et al. developed a MEMS gas sensor based on SnO2 films, utilizing Ag, Pd, Ru, and their combinations as catalysts to enhance CO sensitivity while mitigating humidity-induced cross-sensitivity [77]. The sensor operates effectively at 200 °C and responds to CO and hydrocarbon mixtures ranging from 5 to 50 ppm. As shown in Figure 8c, the AgRu- and AgPd-functionalized sensors exhibit high CO sensitivity with low humidity cross-sensitivity. In contrast, the tri-metal PdAgRu-functionalized sensor exhibits the strongest CO response (Figure 8d). Vanmathi et al. optimized the fabrication parameters for Al-doped TiO2 films via radio-frequency magnetron sputtering [78]. The increased surface area provides more active sites for gas absorption, significantly altering the material’s resistivity. The sensor has a detection limit of 55% at a CO concentration of 200 ppm.
Table 6. Comparison of the sensing performance of various MEMS MOS CO sensors.
Table 6. Comparison of the sensing performance of various MEMS MOS CO sensors.
Sensing MaterialWorking Temperature
(°C)		Conc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/
Recovery
Time (s)		Reference
10 wt%WO3/0.5 wt%Pt/SnO2 NS400201.3191/134[76]
Ag/Pd/Ru/SnO2200501.67-/-[77]
Al doped TiO24002002.22-/-[78]
Pd/SnO2 nanopowder300600.23-/-[75]

3.2.2. MEMS MOS NO2 Sensors

Recently, MEMS MOS sensors have been explored for NO2 sensing, due to their high response, fast detection speed, and ease of integration. For example, Hsueh first synthesized Co3O4 nanoparticles (NPs) through ultrasonic wave grinding and then decorated the Co3O4 with Au NPs [79]. The Au/Co3O4-NPs MEMS gas sensor exhibits higher sensitivity to NO2 than to other gases (SO2, NH3, CO) at an optimal operating temperature of 136 °C. Comparison of the sensing performance of various MEMS MOS NO2 sensors can be found in Table 7.
CuO nanowires (NWs) can be produced through RF sputtering and integrated with a MEMS structure to form a MEMS sensor [80]. The sensor can detect 500 ppb NO2, with a response value of 1.63 at 131 °C. ZnO nanomaterial was deposited on a ceramic MHP by a microextruder [81]. The MEMS sensor has a response value of 41.6 toward 10 ppm NO2 at 200 °C. Meanwhile, Xu et al. obtained hierarchically porous ZnO by annealing Zeolitic Imidazolate Frameworks (ZIF-90) at 450 °C [82]. The optimal MEMS sensor can detect 10 ppm NO2 in 9 s with a high response. In addition, tiny SnO2 NPs with enriched oxygen vacancies are synthesized through the hydrothermal method assisted by PVP. The obtained SnO2 NPs are employed in MEMS gas sensors, which can have a response value of 14.5 toward 500 ppb NO2 at 102 °C in 45.9 s [83]. The superior gas-sensing performance of the SnO2 NO2 sensor can be attributed to the effect of oxygen vacancies, which will introduce multiple midgap states or donor energy levels between the valence and conduction bands of the sensing material, accelerating the excitation of hot electrons into the conduction band. This process facilitates the adsorption of more NO2 molecules onto the surface, where they rapidly capture electrons and subsequently participate in redox reactions, ultimately enhancing the gas-sensing performance, as shown in Figure 9a,b. Furthermore, doping with an appropriate amount of N not only increases the number of O2− species, providing additional active sites for NO2 adsorption, but also enhances the electron-transfer rate, thereby improving carrier mobility in tin dioxide.

3.2.3. MEMS MOS H2 Sensors

H2 is a highly flammable gas with an extensive explosive limit (4–75% in air). A leak mixed with air can easily ignite upon exposure to open flames or static electricity, potentially causing explosions or fires. H2 sensors enable real-time concentration monitoring and trigger alarms when concentrations approach hazardous thresholds, preventing safety incidents. In recent years, MEMS hydrogen sensors have achieved remarkable advancements in research.
Comparison of the sensing performance of various MEMS MOS H2 sensors can be found in Table 8.
Zhang et al. developed a Pt-modified 0.4% Nb-doped TiO2-nanoplate MEMS hydrogen sensor that achieves 12.3 Hz response at room temperature for 1000 ppm H2, with response and recovery times of 31 s and 270 s, respectively, demonstrating excellent selectivity, stability, and repeatability [84]. He et al. created a reduced graphene oxide (rGO)-modified Nb-doped TiO2-nanoplate MEMS H2 sensor, where 10% rGO and 0.8% Nb-doped TiO2-nanoplates showed a 2.5 Hz response at 100 °C for 1000 ppm H2 with response/recovery times of 32.5/58 s [85]. Luo et al. [86] introduced an oxygen vacancy-enhanced SnO2 (SnO2-D) MEMS H2 sensor, where D4 modification altered the SnO2 microstructure while regulating electronic states and gas adsorption reactions, optimizing target gas adsorption, dissociation, and electron transfer to enhance gas-sensitive response performance, as shown in Figure 10a. At the optimal operating temperature of 250 °C, the SnO2-D4 sensor demonstrates a response value of 2.3 to 6 ppm H2, surpassing the original SnO2 (1.13). The response time and recovery time for six ppm H2 are approximately 7 s and 12 s, respectively, as shown in Figure 10b. Li et al. proposed a gas sensor array by integrating Pd-SnO2 nanoflower clusters with F-WO3 microparticles on low-power-consumption microthermal plates, as illustrated in Figure 10c,d [87]. Operating at 300 °C (17 mW power consumption), it exhibits high sensitivity and low cross-sensitivity to eight gases, including H2 and ammonia, as well as their mixtures. The Pd-SnO2 nanoflower cluster exhibits an excellent linear response across H2 concentrations ranging from 15 to 500 ppm, as shown in Figure 10e.

3.2.4. MEMS MOS CH4 Sensors

CH4, being a flammable and explosive gas, can easily cause fires or explosions, making real-time concentration monitoring crucial. The research on MEMS CH4 sensors has attracted increasing attention. Sagitova et al. synthesized Nb and Cr-doped SnO2 gas sensing materials via flame spray pyrolysis [88]. Figure 11a shows the TEM image of SnO2-Cr1Nb1 material, where hetero-doping reduced the response attenuation by half compared to pure SnO2, with a response range of 100–400 °C for CH4 pairs, as seen in Figure 11b. Niu et al. [89] fabricated MOS gas sensors based on Si/SiO2-doped flexible fiber substrates, achieving CH4 detection through the in situ synthesis of Co-doped ZnO nanorods on the fiber surfaces. The sensor exhibited a maximum response of 16% at 1000 ppm CH4 under an optimal operating temperature of 50 °C, with a power consumption of 3.2 mW/mm2 and response/recovery times of approximately 350 s/106 s, respectively. Li et al. modified SnO2 nanosheets with PdPt bimetallic nanoparticles enriched in Pd shells and Pt cores to construct MOS gas sensors [90]. Figure 11c displays the TEM image of the 1P-PdPt/SnO2-A material, which exhibits temperature-dependent dual selectivity, responding at 320 °C, with a peak response value of 5.2 for 1000 ppm CH4. Figure 11d shows the gas response of 1P-PdPt/SnO2 at 100/320 °C, demonstrating high selectivity for CH4. Murata et al. developed MEMS gas sensors containing 7.5 at% Pt/SnO2 thin-film catalysts [91]. The dispersed Pt atoms in this sensor maintain a stable SnO2 lattice configuration. Under reducing gas and 703 K reaction conditions, minimal oxygen loss from the surrounding lattice occurs without Pt aggregation. The oxygen deficit is readily restored by air oxidation, ensuring high methane sensitivity and a long service life, with detection capability up to 12,500 ppm CH4. A detailed comparison of different MEMS CH4 sensors is presented in Table 9.

4. Challenges and Prospects

Room-temperature gas sensors have attracted increasing attention in recent years, particularly due to their safety requirements and low power consumption. However, humidity and response speed are the primary concerns. Humidity effect can be alleviated through several routes: (1) surface decoration through functional materials, such as a catalytic filter or graphite [92,93]; (2) temperature modulation method [94]; (3) algorithm correction [95]. Surface decoration can provide reasonable protection against water vapor, though it may affect response speed if the coating is too thick. It will incur additional costs due to the addition of a functional substance. Algorithm correction has now become a hot topic in the humidity suppression area [96]. It has been shown in the previous section that fast gas response dynamics can be achieved by carefully tuning the material component and structure, as shown in [41,45].
The selectivity problem appears to be a universal issue for all MOS sensors, whether they operate at room or elevated temperatures, regardless of whether they use MEMS technology. It is due to the sensor’s surface reaction-based principle. Advanced algorithms, combined with sensor array design, can be a suitable solution for this issue [97]. There are two general routes for data processing algorithms: machine learning and deep learning. Machine learning algorithms have better Interpretability than deep learning ones, but they require manual feature extraction and are easily influenced by environmental noise and fluctuations. Deep learning algorithms have high tolerance to environmental fluctuations and high recognition accuracy. It can automatically extract deep features from the data, despite typically having more complex network structures and poor interpretability [98]. Recently, Convolutional neural networks, ResNet, LSTM, and Transformer algorithms have been applied to gas recognition, achieving ideal results [99,100,101]. Furthermore, multi-task learning frameworks have been investigated for the dual tasks of gas classification and concentration regression [102,103,104,105]. Temperature modulation is another way to address the problem of selectivity [106,107]. Recently, our group also investigated the temperature modulation method and combined it with an FPGA to achieve the classification of seven different types of gases [108].
The stability of MOS gas sensors is very critical for their practical application. A recent review provides several routes for improving the stability of MOS gas sensors [109]. Firstly, p-type material may be superior in humidity resistance [110]. It is due to the surface metal cations’ effect, i.e., facilitating strong oxygen adsorption and redox cycles, thereby inhibiting interference from water molecules. Secondly, hydrophobic modification with PDMS or rare-earth oxides can improve the interaction of water molecules [111,112].

5. Conclusions

This paper focuses on low-power-consumption metal oxide gas sensors for secure IoT applications, presenting research advancements in both room-temperature and MEMS gas sensors. Room-temperature gas sensors that operate without heating feature low power consumption and simple structures; however, they suffer from humidity sensitivity, slow response times, limited sensitivity to low-concentration gases, and limited selectivity. MEMS gas sensors excel in high sensitivity, rapid response, low power consumption, and ease of integration. Their low-power-consumption operation is achieved through optimized microthermal plate structures and improved sensitive materials, demonstrating outstanding performance in detecting CH4, H2, NO2, and CO. However, MEMS sensors face several challenges, including difficulties controlling thermal crosstalk, risks to structural integrity, and high production costs due to the complexity of their manufacturing processes. Future efforts should focus on enhancing room-temperature sensor performance (especially in selectivity and speed), promoting collaborative innovation in MEMS sensor structures and materials, and advancing intelligent, integrated solutions tailored for practical applications. A selectivity problem exists in both types of MOS gas sensors due to their working principles. Recent progress in advanced algorithms can provide an ideal solution for the selectivity problem.

Author Contributions

Conceptualization, Y.Z. and M.J.; methodology, M.J.; software, R.L.; validation, R.L., G.W. and Z.Z.; formal analysis, M.J.; investigation, R.L.; resources, Z.Z.; data curation, R.G.; writing—original draft preparation, Y.Z.; writing—review and editing, M.J.; visualization, R.L.; supervision, M.J.; project administration, G.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Foundation Council (52404266, 62204260), the Basic Research Program of Jiangsu (No. BK20241645), China Postdoctoral Science Foundation (2025T180510, 2024M753532), and Jiangsu Postdoctoral Science Foundation (2024ZB449).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Gao, D.H.; Yu, Q.C.; Kebeded, M.A.; Zhuang, Y.Y.; Huang, S.; Jiao, M.Z.; He, X.J. Advances in Modification of Metal and Noble Metal Nanomaterials for Metal Oxide Gas Sensors: A Review. Rare Met. 2025, 44, 1443–1496. [Google Scholar] [CrossRef]
  2. Shooshtari, M.; Vollebregt, S.; Vaseghi, Y.; Rajati, M.; Pahlavan, S. The Sensitivity Enhancement of TiO2-Based VOCs Sensor Decorated by Gold at Room Temperature. Nanotechnology 2023, 34, 255501. [Google Scholar] [CrossRef]
  3. Shooshtari, M.; Salehi, A. An Electronic Nose Based on Carbon Nanotube -Titanium Dioxide Hybrid Nanostructures for Detection and Discrimination of Volatile Organic Compounds. Sens. Actuators B Chem. 2022, 357, 131418. [Google Scholar] [CrossRef]
  4. Jiao, M.; Zhao, X.; He, X.; Wang, G.; Zhang, W.; Rong, Q.; Nguyen, D.H. High-Performance MEMS Oxygen Sensors Based on Au/TiO2 Films. Chemosensors 2023, 11, 476. [Google Scholar] [CrossRef]
  5. He, Y.; Jiao, M. A Mini-Review on Metal Oxide Semiconductor Gas Sensors for Carbon Monoxide Detection at Room Temperature. Chemosensors 2024, 12, 55. [Google Scholar] [CrossRef]
  6. Zhu, L.; Wang, H.; Qiu, C.; Li, M.; Li, Q.; An, F.; Song, Y.; Wang, T.; Jiao, G.; Ding, Z.; et al. Carbon Monoxide Gas Sensor Based on Hydrothermally Synthesized Pd-CuO Nanorods/SnSe2 Nanoflower Heterojunctions. ACS Appl. Nano Mater. J. 2024, 7, 25847–25856. [Google Scholar] [CrossRef]
  7. Song, J.; Zhao, J.; Wu, M.; Hu, Y.; Chen, W. Greatly Improved Room-Temperature CO-Sensing Capability of Pt–SnO2 Composite Nanoceramics Prepared Using Solution Reduction Pt-Loading Method. J. Mater. Sci. Mater. Electron. 2025, 36, 223. [Google Scholar] [CrossRef]
  8. Liu, L.; Zhao, J.; Li, S.; Liu, F.; Jin, Z.; Liu, J.; Du, W.; Wang, F.; Wang, Z.; Wu, L. Room Temperature CO Sensor Based on Au-Decorated ZnO Nanosheets. Colloids Surf. A Physicochem. Eng. Asp. 2025, 717, 136799. [Google Scholar] [CrossRef]
  9. Wang, W.; Cao, J.; Wang, S.; Zhang, R.; Zhang, Y. CuO–SnO2 Sensor for Room-Temperature CO Detection: Experiments and DFT Calculations. Sens. Actuators B Chem. 2024, 420, 136427. [Google Scholar] [CrossRef]
  10. Xie, R.; Lu, J.; Liu, Y. Carbon Monoxide Gas Sensing Properties of SnO2 Modified Metal-Organic Skeleton Derived NiO. Sens. Actuators A Phys. 2024, 367, 115038. [Google Scholar] [CrossRef]
  11. Zhang, Y.; Xiong, H.; Chen, Q.; Tang, M.; Wang, Z.; Bian, J.; Zhang, D. Hollow-Sphere NiO/Ti3C2Tx-Based Gas Sensor for CO Sensing. ACS Appl. Nano Mater. J. 2025, 8, 4832–4840. [Google Scholar] [CrossRef]
  12. Chen, Y.; Li, X.; Zhu, C.; Fan, G.; Khademolqorani, S.; Banitaba, S.N. Recent Insights on MXene-Based Architectures for Monitoring and Sensing of Gaseous Pollutants: A Review. Talanta 2024, 280, 126700. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, Y.; Wang, Y.; Jian, M.; Jiang, Q.; Li, X. MXene Key Composites: A New Arena for Gas Sensors. Nanomicro Lett. 2024, 16, 209. [Google Scholar] [CrossRef]
  14. Berdiyorov, G.R. Effect of Surface Functionalization on the Electronic Transport Properties of Ti3C2 MXene. EPL 2015, 111, 67002. [Google Scholar] [CrossRef]
  15. Zhang, C.J.; Anasori, B.; Seral-Ascaso, A.; Park, S.H.; McEvoy, N.; Shmeliov, A.; Duesberg, G.S.; Coleman, J.N.; Gogotsi, Y.; Nicolosi, V. Transparent, Flexible, and Conductive 2D Titanium Carbide (MXene) Films with High Volumetric Capacitance. Adv. Mater. 2017, 29, 1702678. [Google Scholar] [CrossRef]
  16. Madbouly, A.; Elzwawy, A.; Morsy, M. Rapid CO Detection Using MWCNTs/SnO2 Hierarchical Structure: Synthesis, Characterization, and RT Gas Sensing Features. Ceram. Int. 2024, 50, 25473–25483. [Google Scholar] [CrossRef]
  17. Amith, S.L.; Piraman, S. Effect of Synthesis Technique on the Formation of Surface-Active Reduced Graphene Oxide (RGO) Wrapped SnS2 Nanosphere Modified Electrode: A Comparative Study on CO Sensing Behaviours. Surf. Interfaces 2025, 72, 106944. [Google Scholar] [CrossRef]
  18. Shakin, M.S.A.B.; Rahman, M.T.; Shanto, S.N.; Rana, M.M. Au-Loaded WS2/SnO2 Heterostructure for Room Temperature Detection of CO at Ppb-Level. IEEE Sens. J. 2024, 24, 36386–36392. [Google Scholar] [CrossRef]
  19. Yang, Z.; Hu, X.; Wu, M.; Mao, J.; Yang, N.; Wang, X.; Zhang, Q. Mining PANI/Ti3AlC2/CeO2 Ternary Composite CO Sensor: Material Synthesis, Gas Sensing Performance and Mechanism Research. J. Mol. Struct. 2025, 1322, 140505. [Google Scholar] [CrossRef]
  20. Bai, H.; Guo, H.; Wang, J.; Dong, Y.; Liu, B.; Xie, Z.; Guo, F.; Chen, D.; Zhang, R.; Zheng, Y. A Room-Temperature NO2 Gas Sensor Based on CuO Nanoflakes Modified with RGO Nanosheets. Sens. Actuators B Chem. 2021, 337, 129783. [Google Scholar] [CrossRef]
  21. Cao, P.J.; Xu, X.H.; Jia, F.; Zeng, Y.X.; Liu, W.J.; Wang, C.F.; Han, S.; Fang, M.; Liu, X.K.; Zhu, D.L.; et al. Ultrasensitive Room Temperature Chemiresistive NO2 Gas Sensing down to Ppb Levels Using In2O3/RGO Heterostructures. Appl. Surf. Sci. 2025, 688, 162424. [Google Scholar] [CrossRef]
  22. Amu-Darko, J.N.O.; Hussain, S.; Wang, M.; Lei, S.; Alothman, A.A.; Mohammad, S.; Qiao, G.; Liu, G. Advanced 2D Nanosheet-Based Gas Sensor for Sensitive Detection of Low Concentration NO2 Gas Using In2O3/Ti3C2 Layers. Sens. Actuators B Chem. 2024, 407, 135464. [Google Scholar] [CrossRef]
  23. Yang, Y.; Zhu, M.; Zhang, H.; Wang, B.; Chen, C.; Li, J.; Wang, Y.; Hao, J. Room Temperature Gas Sensor Based on RGO/Bi2S3 Heterostructures for Ultrasensitive and Rapid NO2 Detection. Chem. Eng. J. 2024, 490, 151872. [Google Scholar] [CrossRef]
  24. Govind, A.; Bharathi, P.; Harish, S.; Krishna Mohan, M.; Archana, J.; Navaneethan, M. Interface Engineering of a Highly Sensitive Porous CuO Modified RGO Layers for Room Temperature NO2 Gas Sensor. Appl. Surf. Sci. 2024, 657, 159604. [Google Scholar] [CrossRef]
  25. Zhang, Y.; Xing, Y.; Yang, Z.; Zhao, L.; Xin, C.; Wei, Z.; Fei, T.; Liu, S.; Zhang, T. Highly Responsive Room-Temperature NO2 Sensor Based on Oxygen Vacancies-Rich SnO2-RGO Hybrids. Mater. Today Commun. 2024, 38, 108090. [Google Scholar] [CrossRef]
  26. Han, J.; Wang, Y.; Zhou, W.; Kong, D.; Gao, Y.; Gao, Y.; Lu, G. Graphene Oxide-Mediated Polymorphic Engineering of In2O3 for Boosted NO2 Gas Sensing Performance. Sens. Actuators B Chem. 2025, 422, 136613. [Google Scholar] [CrossRef]
  27. Wang, Y.; Fu, J.; Xu, J.; Hu, H.; Ho, D. Atomic Plasma Grafting: Precise Control of Functional Groups on Ti3C2Tx MXene for Room Temperature Gas Sensors. ACS Appl. Mater. Interfaces 2023, 15, 12232–12239. [Google Scholar] [CrossRef]
  28. Guo, F.; Feng, C.; Zhang, Z.; Lili, Z.; Xu, C.; Zhang, C.; Lin, S.; Wu, H.; Zhang, B.; Tabusi, A.; et al. A Room-Temperature NO2 Sensor Based on Ti3C2Tx MXene Modified with Sphere-like CuO. Senosrs Actuators B Chem. 2023, 375, 132885. [Google Scholar] [CrossRef]
  29. Quan, W.; Shi, J.; Luo, H.; Fan, C.; Lv, W.; Chen, X.; Zeng, M.; Yang, J.; Hu, N.; Su, Y.; et al. Fully Flexible MXene-Based Gas Sensor on Paper for Highly Sensitive Room-Temperature Nitrogen Dioxide Detection. ACS Sens. 2023, 8, 103–113. [Google Scholar] [CrossRef]
  30. Hilal, M.; Yang, W.; Hwang, Y.; Xie, W. Tailoring MXene Thickness and Functionalization for Enhanced Room-Temperature Trace NO2 Sensing. Nanomicro Lett. 2024, 16, 84. [Google Scholar] [CrossRef]
  31. Zhang, Y.; Li, Y.; Jiang, Y.; Duan, Z.; Yuan, Z.; Liu, B.; Huang, Q.; Zhao, Q.; Yang, Y.; Tai, H. Synergistic Effect of Charge Transfer and Interlayer Swelling in V2CTx/SnS2 Driving Ultrafast and Highly Sensitive NO2 Detection at Room Temperature. Sens. Actuators B Chem. 2024, 411, 135788. [Google Scholar] [CrossRef]
  32. Kodan, S.; Kumar, A.; Sanger, A.; Arora, A.; Malik, V.K.; Chandra, R. Vertically Aligned MoSe2-WS2 Nanoworms Heterojunction towards Room Temperature NO2 Gas Sensors. Sens. Actuators B Chem. 2024, 407, 135481. [Google Scholar] [CrossRef]
  33. Tang, T.; Li, Z.; Cheng, Y.F.; Xie, H.G.; Wang, X.X.; Chen, Y.L.; Cheng, L.; Liang, Y.; Hu, X.Y.; Hung, C.M.; et al. In-Situ Mechanochemically Tailorable 2D Gallium Oxyselenide for Enhanced Optoelectronic NO2 Gas Sensing at Room Temperature. J. Hazard. Mater. 2023, 451, 131184. [Google Scholar] [CrossRef]
  34. Zhang, X.; Hou, Y.; Wang, J.; Liu, T.; Xu, Z.; Hussain, S.; Qiao, G.; Liu, G. Computational Screening and Experimental Fabrication of MoSe2/3d–TMOS Heterojunction for Room-Temperature Gas Sensor. Appl. Surf. Sci. 2024, 654, 159465. [Google Scholar] [CrossRef]
  35. Liu, Z.; Lv, H.; Xu, Y.; Shi, K. Sulfur Filling in Oxygen Vacancy and Modulating Surface Active Site on Molybdenum-Based Transition Metal Oxides/Sulfides Heterostructure for Room-Temperature Chemiresistive NO2 Detection. Sens. Actuators B Chem. 2024, 407, 135479. [Google Scholar] [CrossRef]
  36. Wang, C.; Chang, X.; Liu, X.; Zhang, J. ZnIn2S4 Nanosheets for Efficient NO2 Detection at Room Temperature: Insights into the Role of Sulfur Vacancies. J. Phys. Chem. Lett. 2024, 15, 5875–5882. [Google Scholar] [CrossRef] [PubMed]
  37. Cheng, X.; Yao, Y.; Zheng, S.; Wan, Y.; Wei, C.; Yang, G.; Yuan, Y.; Tsai, H.S.; Wang, Y.; Hao, J. Te@Se Core-Shell Heterostructures with Tunable Shell Thickness for Ultra-Stable NO2 Detection. ACS Sens. 2025, 10, 283–291. [Google Scholar] [CrossRef]
  38. Sun, S.; Li, X.; Wang, N.; Huang, B.; Li, X. A Sensitive Ppb-Level NO2 Sensor Based on SnO2 Decorated Te Nanotubes. Sens. Actuators B Chem. 2025, 428, 137238. [Google Scholar] [CrossRef]
  39. Fan, Y.; Li, L.; Song, B.; Wu, H.; Qi, L.; Khan, M.; Wu, H.; Shi, K. Heterostructures of Hollow Co3O4 Nanocages Wrapped in NiO Cilia for Conductometric NO2 Sensing at Room Temperature. Sens. Actuators B Chem. 2024, 404, 135299. [Google Scholar] [CrossRef]
  40. Lin, Y.F.; Tseng, Z.C.; Hsueh, W.J.; Chen, J.H.; Huang, C.Y. Room-Temperature Gas Identification via Photochemically Activated p-Type CuCrO2 Nanostructured Films Using Machine Learning. ACS Appl. Nano Mater. 2025, 8, 189–199. [Google Scholar] [CrossRef]
  41. Wang, B.; Sun, Y.; Dong, Y.; Hou, Y.; Lu, Z.; Wei, Z.; Zhang, W.; Suematsu, K.; Hu, J. Chemiresistive Flexible Gas Sensor for NO2 Sensing at Room-Temperature Using in Situ Constructed Au@In2S3/In2O3 Hybrid Microflowers. Sens. Actuators B Chem. 2025, 422, 136666. [Google Scholar] [CrossRef]
  42. Thathsara, T.; Harrison, C.J.; Hocking, R.K.; Shafiei, M. Pd- and PdO-Decorated TiO2 Nanospheres: Hydrogen Sensing Properties under Visible Light Conditions at Room Temperature. Chemosensors 2023, 11, 409. [Google Scholar] [CrossRef]
  43. Yang, J.E.; Kim, S.; Yoon, J.; Lee, J.; Park, I.K.; Kim, K.K. A UV-Photon-Energy-Integrated Gas Sensor Based on Pt-Nanoparticle-Decorated TiO2 Nanorods for Room-Temperature Hydrogen Detection. Chemosensors 2025, 13, 177. [Google Scholar] [CrossRef]
  44. Wu, X.; Zhang, Y.; Zhang, M.; Liang, J.; Bao, Y.; Xia, X.; Homewood, K.; Lourenco, M.; Gao, Y. An Ultrasensitive Room-Temperature H2 Sensor Based on a TiO2 Rutile–Anatase Homojunction. Sensors 2024, 24, 978. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, Y.; Chen, C.; Xiao, G.; Chen, C.; He, X.; Zhou, J.; Huang, Z.; Le, J.; Zhou, X. Novel High Performance Room Temperature Hydrogen Sensor—3D In2O3@rGO@PPy Aerogel with Dual Enhancement of Mechanical and Gas Sensitive Properties. Int. J. Hydrogen Energy 2025, 154, 150170. [Google Scholar] [CrossRef]
  46. He, X.; Chen, C.; Liu, Y.; Le, J.; Xiao, G.; Chen, C.; Wang, Y.; Zhou, J. Highly Sensitive Room Temperature Hydrogen Sensor Based on Cactus-like ZnO@3D RGO Aerogel. Sens. Actuators A Phys. 2025, 382, 116130. [Google Scholar] [CrossRef]
  47. Li, M.; Sun, X.; Wang, Y.; Qin, C.; Cao, J.; Wang, Y. Preparation and Room-Temperature Hydrogen Sensing Property of Flower-like In2O3/SnS2 Nanocomposite. Phys. E Low-Dimens. Syst. Nanostructures 2024, 160, 115938. [Google Scholar] [CrossRef]
  48. Yang, X.; He, X.; Tang, B.; Zhong, L.; Zhang, W.; Chen, C.; He, T. Room Temperature Hydrogen Sensor of Mace-Like In2O3@ZnO Microtubules. ACS Appl. Electron. Mater. 2024, 6, 5885–5893. [Google Scholar] [CrossRef]
  49. Duan, P.; Wang, H.; Peng, Q.; Chen, S.; Zhou, H.; Duan, Q.; Jin, K.; Sun, J. Ultra-Effective Room Temperature Gas Discrimination Based on Monolithic Pd@MOF-Derived Porous Nanocomposites: An Exclusive Scheme with Photoexcitation. J. Mater. Chem. A 2024, 12, 3896–3909. [Google Scholar] [CrossRef]
  50. Gautam, D.; Gautam, Y.K.; Kumar, A.; Vikal, S.; Sanger, A.; Malik, A.K.; Pal Singh, B. Highly Selective and Extensive Range Room Temperature Hydrogen Gas Sensor Based on Pd-Mg Alloy Thin Films. IEEE Sens. J. 2024, 24, 40423–40430. [Google Scholar] [CrossRef]
  51. Kalita, P.; Mondal, B. Humidity Tolerant Enhanced Hydrogen Gas Sensing Using MoSe2-WSe2 Heterostructures: An Experimental and Computational Insights. Sens. Actuators B Chem. 2025, 424, 136787. [Google Scholar] [CrossRef]
  52. Li, X.; He, H.; Tan, T.; Zou, Z.; Tian, Z.; Zhou, W.; Bao, Y.; Xia, X.; Gao, Y. Annealing Effect on the Methane Sensing Performance of Pt–SnO2/ZnO Double Layer Sensor. Appl. Surf. Sci. 2023, 640, 158428. [Google Scholar] [CrossRef]
  53. Yang, L.; Fu, W.; Mao, L.; Xu, L.; Yao, C.; Zhang, H.; Cheng, H. In2O3-ZnO/Laser-Induced Graphene Nanocomposites for a Highly Sensitive, Room-Temperature, Flexible Methane Gas Sensor. ACS Appl. Nano Mater. 2025, 8, 7510–7519. [Google Scholar] [CrossRef]
  54. Li, X.; Hu, H.; Tan, T.; Sun, M.; Bao, Y.; Huang, Z.; Muhammad, S.; Xia, X.; Gao, Y. Enhancing Methane Gas Sensing through Defect Engineering in Ag-Ru Co-Doped ZnO Nanorods. ACS Appl. Mater. Interfaces 2024, 16, 26395–26405. [Google Scholar] [CrossRef]
  55. Sun, X.; Zhu, L.; Qin, C.; Cao, J.; Wang, Y. Room-Temperature Methane Sensors Based on ZnO with Different Exposed Facets: A Combined Experimental and First-Principle Study. Surf. Interfaces 2023, 38, 102823. [Google Scholar] [CrossRef]
  56. Sun, X.; Zhang, Y.; Wang, Y.; Li, M.; Qin, C.; Cao, J.; Wang, Y. UV-Activated AuAg/ZnO Microspheres for High-Performance Methane Sensor at Room Temperature. Ceram. Int. 2024, 50, 30552–30559. [Google Scholar] [CrossRef]
  57. Li, X.; Tan, T.; Ji, W.; Zhou, W.; Bao, Y.; Xia, X.; Zeng, Z.; Gao, Y. Remarkably Enhanced Methane Sensing Performance at Room Temperature via Constructing a Self-Assembled Mulberry-Like ZnO/SnO2 Hierarchical Structure. Energy Environ. Mater. 2024, 7, e12624. [Google Scholar] [CrossRef]
  58. Sun, X.; Zhu, L.; Zhang, Y.; Li, M.; Wang, Y.; Qin, C.; Wang, Y. Simulated Sunlight Enhanced Methane-Sensing Performance Based on Au-Modified ZnO Microsphere under Room Temperature. J. Mater. Sci. Mater. Electron. 2023, 34, 1950. [Google Scholar] [CrossRef]
  59. Sun, X.; Li, M.; Wang, Y.; Qin, C.; Cao, J.; Wang, Y. NiO/ZnO Heterojunction Microspheres for Methane Detection at Room Temperature. Opt. Mater. 2024, 148, 114893. [Google Scholar] [CrossRef]
  60. Zabelin, D.; Zabelina, A.; Guselnikova, O.; Miliutina, E.; Kolska, Z.; Stulik, J.; Polansky, R.; Elashnikov, R.; Kalachyova, Y.; Svorcik, V.; et al. Selective Methane Chemiresistive Detection Using MWCNTs Array Decorated by Metal Organic Framework Layer. Surf. Interfaces 2023, 40, 103105. [Google Scholar] [CrossRef]
  61. Shukla, P.; Saxena, P.; Madhwal, D.; Bhardwaj, N.; Jain, V.K. Electrostatically Functionalized CVD Grown Multiwalled Carbon Nanotube/Palladium Polymer Nanocomposite (MWCNT/Pd) for Methane Detection at Room Temperature. Chem. Eng. Sci. 2022, 264, 118191. [Google Scholar] [CrossRef]
  62. Hadano, H.; Miyagi, A.; Okuno, T.; Nagawa, Y.; Ishiguro, Y. Development of a Catalytic Combustion Type Gas Sensor with Low Power Consumption. ECS Trans. 2016, 75, 195–198. [Google Scholar] [CrossRef]
  63. Jang, W.; Kim, I.H.; Jeong, Y.S.; Park, K.B.; Park, S.S.; Yoon, M.Y.; Roh, Y.; Park, J.S. Characteristics of Fabricated Catalytic Combustible Micro Gas Sensor with Low Power Consumption for Detecting Methane Leakage of Compressed Natural Gas Bus. J. Electroceramics 2013, 31, 280–285. [Google Scholar] [CrossRef]
  64. Yan, W.; Liu, Y.; Bai, Y.; Chen, Y.; Zhou, H.; Ahmad, W. Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H2 and CO Recognition. ACS Appl. Mater. Interfaces 2024, 16, 49474–49483. [Google Scholar] [CrossRef]
  65. Yang, Y.; Xie, D.; Chen, D.; Geng, L.; Zhang, Y.; Xu, L.; Wu, F. A Quadrilateral Gas Sensor Cell by Complete MEMS Process for Gas Identification. IEEE Sens. J. 2024, 25, 2267–2275. [Google Scholar] [CrossRef]
  66. Li, M.; Luo, W.; Yan, W. Low Concentration CO Gas Sensor Based on Pulsed-Heating and Wafer-Level Fabricated MEMS Hotplate. IEEE Electron Device Lett. 2024, 45, 689–692. [Google Scholar] [CrossRef]
  67. Luo, W.; Li, J.; Li, M. A Single MEMS Sensor Based on SnO Nanosheets for Selective Gas Identification. IEEE Trans. Electron Devices 2024, 71, 7016–7021. [Google Scholar] [CrossRef]
  68. Wu, H.; Yu, J.; Cao, R.; Yang, Y.; Tang, Z. Electrohydrodynamic Inkjet Printing of Pd Loaded SnO2 Nanofibers on a CMOS Micro Hotplate for Low Power H2 Detection. AIP Adv. 2018, 8, 055307. [Google Scholar] [CrossRef]
  69. Li, Z.; Li, M.; Jia, H.; Zhang, H.; Xu, P.; Li, X. High-Sensitive Hydrogen Detection in Oxygen-Free Environment with MEMS Differential Thermopiles. In Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS), Austin, TX, USA, 21–25 January 2024; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2024; pp. 859–862. [Google Scholar]
  70. Yang, Y.; Wei, G.; He, A.; Jiao, S.; Zhang, W.; Gao, C. Design and Optimization of a Novel Multiheater Microhotplate with Thermal Crosstalk. IEEE Sens. J. 2025, 25, 15893–15905. [Google Scholar] [CrossRef]
  71. Zhang, Z.; Zhao, X.; Wang, D.; Duan, G.; Xing, C. A Micro-Hotplate for MEMS Gas Sensor Wafer Level Packaging. In Proceedings of the 2023 24th International Conference on Electronic Packaging Technology, ICEPT 2023, Shihezi City, China, 8–11 August 2023; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2023. [Google Scholar]
  72. Wei, G.; Wang, P.; Li, M.; Lin, Z.; Nai, C. Simulation and Optimization of a Planar-Type Micro-Hotplate with Si3N4-SiO2 Transverse Composite Dielectric Layer and Annular Heater. Micromachines 2022, 13, 601. [Google Scholar] [CrossRef]
  73. Zhang, Z.; Luo, L.; Luo, Y.; Zhang, Z.; Xing, C.; Duan, G. High-Density Integration of Multiple Independent Temperature-Controlled Micro Hotplates for MEMS Gas Sensors. In Proceedings of the Proceedings of IEEE Sensors, Vienna, Austria, 29 October–1 November 2023; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2023. [Google Scholar]
  74. Li, J.M.; Jiao, M.Z.; Qian, C. Simulation and Performance Study of Low-Power Magnetron Sputtered ZnO Methane Sensor. Gongcheng Kexue Xuebao/Chin. J. Eng. 2023, 45, 987–994. [Google Scholar] [CrossRef]
  75. Kim, B.J.; Kim, J.S. Gas Sensing Characteristics of MEMS Gas Sensor Arrays in Binary Mixed-Gas System. Mater. Chem. Phys. 2013, 138, 366–374. [Google Scholar] [CrossRef]
  76. Moon, S.E.; Lee, J.W.; Choi, N.J.; Lee, H.K.; Yang, W.S.; Kim, J.; Park, Y. High-Response and Low-Power-Consumption CO Micro Gas Sensor Based on Nano-Powders and a Micro-Heater. J. Korean Phys. Soc. 2012, 60, 235–239. [Google Scholar] [CrossRef]
  77. Egger, L.; Sosada-Ludwikowska, F.; Steinhauer, S.; Singh, V.; Grammatikopoulos, P.; Köck, A. SnO2-Based CMOS-Integrated Gas Sensor Optimized by Mono-, Bi-, and Trimetallic Nanoparticles. Chemosensors 2025, 13, 59. [Google Scholar] [CrossRef]
  78. Vanmathi, M.; Senthil Kumar, M.; Ismail, M.M.; Senguttuvan, G. Optimization of Process Parameters for Al-Doping Back Ground on CO Gas Sensing Characteristics of Magnetron-Sputtered TiO2 Sensors. Mater. Res. Express 2019, 6, 106423. [Google Scholar] [CrossRef]
  79. Hsueh, T.J.; Wu, S.S. Highly Sensitive Co3O4 Nanoparticles/MEMS NO2 Gas Sensor with the Adsorption of the Au Nanoparticles. Sens. Actuators B Chem. 2021, 329, 129201. [Google Scholar] [CrossRef]
  80. Hsueh, T.J.; Li, P.S.; Fang, S.Y.; Hsu, C.L. A Vertical CuO-NWS/MEMS NO2 Gas Sensor That Is Produced by Sputtering. Sens. Actuators B Chem. 2022, 355, 131260. [Google Scholar] [CrossRef]
  81. Kul, O.; Vasiliev, A.; Shaposhnik, A.; Nikitin, A.; Dmitrieva, A.; Bolshakov, A.; Liu, Z.; Ma, M.; Mokrushin, A.; Simonenko, N.; et al. Novel Screen-Printed Ceramic MEMS Microhotplate for MOS Sensors. Sens. Actuators A Phys. 2024, 379, 115907. [Google Scholar] [CrossRef]
  82. Zhang, D.; Luo, N.; Xue, Z.; Bai, Y.; Xu, J. Hierarchically Porous ZnO Derived from Zeolitic Imidazolate Frameworks for High-Sensitive MEMS NO2 Sensor. Talanta 2024, 274, 125995. [Google Scholar] [CrossRef]
  83. Yan, Y.; Lan, X.; Li, Y.; Liang, M.; Liu, X.; Yang, J.; Jia, R.; Ge, Y.; Li, Z.; Huang, L. Oxygen-Vacancy-Rich SnO2 Nanoparticles Based Ultralow-Power MEMS Sensor for Nitrogen Dioxide Detection. ACS Appl. Nano Mater. 2025, 8, 6920–6929. [Google Scholar] [CrossRef]
  84. Zhang, M.; He, Z.; Cheng, W.; Li, X.; Zan, X.; Bao, Y.; Gu, H.; Homewood, K.; Gao, Y.; Zhang, S.; et al. A Room-Temperature MEMS Hydrogen Sensor for Lithium Ion Battery Gas Detecting Based on Pt-Modified Nb Doped TiO2 Nanosheets. Int. J. Hydrogen Energy 2024, 74, 307–315. [Google Scholar] [CrossRef]
  85. He, Z.; Zhang, M.; Cheng, W.; Li, X.; Zan, X.; Bao, Y.; Gu, H.; Homewood, K.; Gao, Y.; Chang, G.; et al. Reduced Graphene Oxide Modified Nb-Doped TiO2 Nanosheet for Hydrogen Micro-Electro-Mechanical System Gas Sensor. Int. J. Hydrogen Energy 2025, 100, 678–687. [Google Scholar] [CrossRef]
  86. Luo, N.; Wang, C.; Zhang, D.; Guo, M.; Wang, X.; Cheng, Z.; Xu, J. Ultralow Detection Limit MEMS Hydrogen Sensor Based on SnO2 with Oxygen Vacancies. Sens. Actuators B Chem. 2022, 354, 130982. [Google Scholar] [CrossRef]
  87. Li, Z.; Yu, J.; Dong, D.; Yao, G.; Wei, G.; He, A.; Wu, H.; Zhu, H.; Huang, Z.; Tang, Z. E-Nose Based on a High-Integrated and Low-Power Metal Oxide Gas Sensor Array. Sens. Actuators B Chem. 2023, 380, 133289. [Google Scholar] [CrossRef]
  88. Sagitova, A.; Markelova, M.; Nikolaeva, A.; Polomoshnov, S.; Generalov, S.; Khmelevskiy, N.; Grigoriev, Y.; Konstantinova, E.; Krivetskiy, V. Restraining SnO2 Gas Sensor Response Degradation through Heterovalent Doping. Sens. Actuators B Chem. 2025, 429, 137345. [Google Scholar] [CrossRef]
  89. Niu, F.; Zhou, F.; Wang, Z.; Wei, L.; Hu, J.; Dong, L.; Ma, Y.; Wang, M.; Jia, S.; Chen, X.; et al. Synthesizing Metal Oxide Semiconductors on Doped Si/SiO2 Flexible Fiber Substrates for Wearable Gas Sensing. Research 2023, 6, 100. [Google Scholar] [CrossRef]
  90. Li, G.; Wang, X.; Yan, L.; Wang, Y.; Zhang, Z.; Xu, J. PdPt Bimetal-Functionalized SnO2 Nanosheets: Controllable Synthesis and Its Dual Selectivity for Detection of Carbon Monoxide and Methane. ACS Appl. Mater. Interfaces 2019, 11, 26116–26126. [Google Scholar] [CrossRef]
  91. Murata, N.; Suzuki, T.; Lin, Y.; Nitani, H.; Niwa, Y.; Wada, T.; Uo, M.; Asakura, K. Structure of Atomically Dispersed Pt in a SnO2 Thin Film under Reaction Conditions: Origin of Its High Performance in Micro Electromechanical System Gas Sensor Catalysis. ACS Appl. Mater. Interfaces 2022, 14, 39507–39514. [Google Scholar] [CrossRef]
  92. Gu, F.; Wang, D.; Wang, J.; Wang, P.; Han, D.; Wang, Z.; Qiao, Z.; Hu, Y. Humidity-Independent, Highly Sensitive and Selective NO2Sensor Based on In2O3Nanoflowers Decorated with Graphite Nanoflakes. IEEE Sens. J. 2022, 22, 14753–14761. [Google Scholar] [CrossRef]
  93. Peveler, W.J.; Binions, R.; Hailes, S.M.V.; Parkin, I.P. Detection of Explosive Markers Using Zeolite Modified Gas Sensors. J. Mater. Chem. A 2013, 1, 2613–2620. [Google Scholar] [CrossRef]
  94. Yuan, Z.; Sun, H.; Ji, H.; Meng, F. Single Feature to Achieve Gas Recognition: Humidity Interference Suppression Strategy Based on Temperature Modulation and Principal Component Linear Discriminant Analysis. Sens. Actuators B Chem. 2025, 423, 136842. [Google Scholar] [CrossRef]
  95. Aurora, A. Algorithmic Correction of MOS Gas Sensor for Ambient Temperature and Relative Humidity Fluctuations. IEEE Sens. J. 2022, 22, 15054–15061. [Google Scholar] [CrossRef]
  96. Wang, Y.; Wang, X.; Zhang, H.; Gao, S.; Xu, W.; Zhao, Y.; Jiao, M.; Huang, S.; Gu, X. Machine-Learning-Aided Identification of Ethanol in Humid Air Using Zinc Complex Capped CsPbBr3 Resistive Sensors. New J. Chem. 2023, 47, 19077–19086. [Google Scholar] [CrossRef]
  97. Chen, Y.; Hu, J.; Hu, N.; Wu, S.; Lu, Y. Machine Learning-Assisted Pd-Au/MXene Sensor Array for Smart Gas Identification. Small Struct. 2025, 6, 2400619. [Google Scholar] [CrossRef]
  98. Wang, X.; Zhou, Y.; Zhao, Z.; Feng, X.; Wang, Z.; Jiao, M. Advanced Algorithms for Low Dimensional Metal Oxides-Based Electronic Nose Application: A Review. Crystals 2023, 13, 615. [Google Scholar] [CrossRef]
  99. Fang, C.; Li, H.-Y.; Li, L.; Su, H.-Y.; Tang, J.; Bai, X.; Liu, H. Smart Electronic Nose Enabled by an All–Feature Olfactory Algorithm. Adv. Intell. Syst. 2022, 4, 2200074. [Google Scholar] [CrossRef]
  100. Wei, G.; Liu, X.; He, A.; Zhang, W.; Jiao, S.; Wang, B. Design and Implementation of a ResNet-LSTM-Ghost Architecture for Gas Concentration Estimation of Electronic Noses. IEEE Sens. J. 2024, 24, 26416–26428. [Google Scholar] [CrossRef]
  101. Addae, S.; Kim, J.; Kang, M. Effective Gas Level Prediction Based on Sensor Array Using Deep Learning in Mixed Gases: A Comparative Analysis of CNN-1D, LSTM, and GRU Models. IEEE Trans. Instrum. Meas. 2025, 74, 1–9. [Google Scholar] [CrossRef]
  102. Wang, T.; Zhang, H.; Wu, Y.; Jiang, W.; Chen, X.; Zeng, M.; Yang, J.; Su, Y.; Hu, N.; Yang, Z. Target Discrimination, Concentration Prediction, and Status Judgment of Electronic Nose System Based on Large-Scale Measurement and Multi-Task Deep Learning. Sens. Actuators B Chem. 2022, 351, 130915. [Google Scholar] [CrossRef]
  103. Ni, W.; Wang, T.; Wu, Y.; Liu, X.; Li, Z.; Yang, R.; Zhang, K.; Yang, J.; Zeng, M.; Hu, N.; et al. Multi-Task Deep Learning Model for Quantitative Volatile Organic Compounds Analysis by Feature Fusion of Electronic Nose Sensing. Sens. Actuators B Chem. 2024, 417, 136206. [Google Scholar] [CrossRef]
  104. Mei, H.; Peng, J.; Wang, T.; Zhang, B.; Xuan, F.; Wang, L.; Zeng, M.; Yang, Z. Smart VOCs Recognition System Based on Single Gas Sensor and Multi-Task Deep Learning Model. Sens. Actuators B Chem. 2025, 439, 137853. [Google Scholar] [CrossRef]
  105. Fu, C.; Zhang, K.; Guan, H.; Deng, S.; Sun, Y.; Ding, Y.; Wang, J.; Liu, J. Progressive Prediction Algorithm by Multi-Interval Data Sampling in Multi-Task Learning for Real-Time Gas Identification. Sens. Actuators B Chem. 2024, 418, 136271. [Google Scholar] [CrossRef]
  106. Mei, H.; Peng, J.; Wang, T.; Wang, L.; Zhang, B.; Xuan, F. Overcoming Selectivity Challenges in Single Gas Sensor Leveraging Temperature Modulation and Multi-Feature Fusion. Chem. Eng. J. 2025, 522, 167722. [Google Scholar] [CrossRef]
  107. Zhang, S.; Yuan, Z.; Kong, L.; Wu, Z.; Meng, F. Drift-Like Effect Compensation for the Cyclic Temperature Operation of Semiconductor Gas Sensor. IEEE Trans. Ind. Electron. 2025, 72, 2140–2149. [Google Scholar] [CrossRef]
  108. Zhang, J.; Jiao, M.; Duan, L.; Zheng, L.; Nguyen, V.D.; Hung, C.M.; Nguyen, D.H. Gas Classification System Based on Hybrid Waveform Modulation Technology on FPGA. Sens. Actuators B Chem. 2025, 435, 137637. [Google Scholar] [CrossRef]
  109. Zhang, C.; Wang, T.; Zhang, G.; Gao, R.; Gao, C.; Wang, Z.; Xuan, F. Rational Design and Fabrication of MEMS Gas Sensors with Long-Term Stability: A Comprehensive Review. Adv. Sci. 2025, 12, e11555. [Google Scholar] [CrossRef] [PubMed]
  110. Wang, J.; Yang, P.; Wei, X. High-Performance, Room-Temperature, and No-Humidity-Impact Ammonia Sensor Based on Heterogeneous Nickel Oxide and Zinc Oxide Nanocrystals. ACS Appl. Mater. Interfaces 2015, 7, 3816–3824. [Google Scholar] [CrossRef]
  111. Zhu, X.; Chang, X.; Tang, S.; Chen, X.; Gao, W.; Niu, S.; Li, J.; Jiang, Y.; Sun, S. Humidity-Tolerant Chemiresistive Gas Sensors Based on Hydrophobic CeO2/SnO2 Heterostructure Films. ACS Appl. Mater. Interfaces 2022, 14, 25680–25692. [Google Scholar] [CrossRef]
  112. Azimi, G.; Dhiman, R.; Kwon, H.M.; Paxson, A.T.; Varanasi, K.K. Hydrophobicity of Rare-Earth Oxide Ceramics. Nat. Mater. 2013, 12, 315–320. [Google Scholar] [CrossRef]
Materials 18 04864 g001
Figure 1. (a) The energy band structure of ZnO and Au-ZnO [8]; (b,c) The mechanism of Au nanoparticles on Au/ZnO and the reaction of CO on the surface of samples [8]; (d) diagram of CO-sensing mechanism from p-NiO/n-SnO2 composites [10].
Figure 1. (a) The energy band structure of ZnO and Au-ZnO [8]; (b,c) The mechanism of Au nanoparticles on Au/ZnO and the reaction of CO on the surface of samples [8]; (d) diagram of CO-sensing mechanism from p-NiO/n-SnO2 composites [10].
Materials 18 04864 g001
Materials 18 04864 g002
Figure 2. Diagram of gas-sensing mechanism and band structure of the In2O3/Ti3C2: (a) The surface nad band structure of In2O3/Ti3C2 in air [22]; (b) The surface nad band structure of In2O3/Ti3C2 in NO2 [22].
Figure 2. Diagram of gas-sensing mechanism and band structure of the In2O3/Ti3C2: (a) The surface nad band structure of In2O3/Ti3C2 in air [22]; (b) The surface nad band structure of In2O3/Ti3C2 in NO2 [22].
Materials 18 04864 g002
Materials 18 04864 g003
Figure 3. TEM images (top) and the corresponding heterojunction band structure (bottom) of Te@Se with different thicknesses of Se [37]: (a) Te@Se-1, (b) Te@Se-2, and (c) Te@Se-3; Schematic diagrams illustrating the gas-sensing mechanism of Te NT/SnO2 [38]: (d) band structures of Te and SnO2, (e) sensing process of the TeNT/SnO2-based sensor.
Figure 3. TEM images (top) and the corresponding heterojunction band structure (bottom) of Te@Se with different thicknesses of Se [37]: (a) Te@Se-1, (b) Te@Se-2, and (c) Te@Se-3; Schematic diagrams illustrating the gas-sensing mechanism of Te NT/SnO2 [38]: (d) band structures of Te and SnO2, (e) sensing process of the TeNT/SnO2-based sensor.
Materials 18 04864 g003
Materials 18 04864 g004
Figure 4. Gas-sensitive response mechanism of different 3D materials: (a) In2O3-rGO-Ppy [45]; (b) Cactus-like ZnO@3D rGA [46].
Figure 4. Gas-sensitive response mechanism of different 3D materials: (a) In2O3-rGO-Ppy [45]; (b) Cactus-like ZnO@3D rGA [46].
Materials 18 04864 g004
Materials 18 04864 g005
Figure 5. (a) Band structure and methane sensing mechanism of: (a) Pt-SnO2/ZnO [52]; (b) AgRu/ZnO [54].
Figure 5. (a) Band structure and methane sensing mechanism of: (a) Pt-SnO2/ZnO [52]; (b) AgRu/ZnO [54].
Materials 18 04864 g005
Materials 18 04864 g007
Figure 7. (a) Schematic diagram of the planar configuration of the thermal quenching heater MHP [70]; (b) Temperature distribution of MHP with and without etched windows [70]; (c) Structural schematic of Si3N4-SiO2 composite dielectric layer MHP [72]; (d) Three-micro-thermal-plate integrated array structure MHP [73]; The temperature distributions under different heating voltages for structures 1 and 2 designed by our research group are shown as follows: (e) Structure 1 [74]; (f) Structure 2 [74].
Figure 7. (a) Schematic diagram of the planar configuration of the thermal quenching heater MHP [70]; (b) Temperature distribution of MHP with and without etched windows [70]; (c) Structural schematic of Si3N4-SiO2 composite dielectric layer MHP [72]; (d) Three-micro-thermal-plate integrated array structure MHP [73]; The temperature distributions under different heating voltages for structures 1 and 2 designed by our research group are shown as follows: (e) Structure 1 [74]; (f) Structure 2 [74].
Materials 18 04864 g007
Materials 18 04864 g008
Figure 8. (a) SEM image of Pd-SnO2 nanomaterials [75]; (b) 3D bar chart of gas sensitivity characteristics of Pd-SnO2 sensors in CO-NH3 gas system [75]; (c) TEM image of the three-metal PdAgRu structures [77]; (d) Normalized response of different SnO2 composites toward 50 ppm CO at various humidity levels [77].
Figure 8. (a) SEM image of Pd-SnO2 nanomaterials [75]; (b) 3D bar chart of gas sensitivity characteristics of Pd-SnO2 sensors in CO-NH3 gas system [75]; (c) TEM image of the three-metal PdAgRu structures [77]; (d) Normalized response of different SnO2 composites toward 50 ppm CO at various humidity levels [77].
Materials 18 04864 g008
Materials 18 04864 g009
Figure 9. Gas sensing mechanism of oxygen-enriched SnO2 NPs before and after exposure to NO2 [83]: (a) Diagram of the gas-sensing mechanism; (b) Energy band structure and electron transfer.
Figure 9. Gas sensing mechanism of oxygen-enriched SnO2 NPs before and after exposure to NO2 [83]: (a) Diagram of the gas-sensing mechanism; (b) Energy band structure and electron transfer.
Materials 18 04864 g009
Materials 18 04864 g010
Figure 10. (a) Schematic diagrams of electronic structures and gas-sensitive response mechanisms for SnO2 and SnO2-D4 under H2 environment [86]; (b) The dynamic responses of different SnO2 samples, SnO2-D3/SnO2-D4/SnO2-D5 refers to SnO2 calcinate at 300/400/500 °C in H2 atmosphere [86]; (c) SEM image of Pd-SnO2 [87]; (d) SEM image of Pd-WO3 [87]; (e) The dynamic responses of Pd-SnO2 [87].
Figure 10. (a) Schematic diagrams of electronic structures and gas-sensitive response mechanisms for SnO2 and SnO2-D4 under H2 environment [86]; (b) The dynamic responses of different SnO2 samples, SnO2-D3/SnO2-D4/SnO2-D5 refers to SnO2 calcinate at 300/400/500 °C in H2 atmosphere [86]; (c) SEM image of Pd-SnO2 [87]; (d) SEM image of Pd-WO3 [87]; (e) The dynamic responses of Pd-SnO2 [87].
Materials 18 04864 g010
Materials 18 04864 g011
Figure 11. (a) TEM image of SnO2-Cr1Nb1 material [88]; (b) Time-dependent response of gas sensors [88]; (c) TEM image of 1P-PdPt/SnO2-A, red dots refer to PtPt bimetal [90]; (d) Response spectra of 1P-PdPt/SnO2-A to different gases at 100/320 °C conditions [90].
Figure 11. (a) TEM image of SnO2-Cr1Nb1 material [88]; (b) Time-dependent response of gas sensors [88]; (c) TEM image of 1P-PdPt/SnO2-A, red dots refer to PtPt bimetal [90]; (d) Response spectra of 1P-PdPt/SnO2-A to different gases at 100/320 °C conditions [90].
Materials 18 04864 g011
Table 1. Comparison of the performance of various room-temperature CO-sensing materials.
Table 1. Comparison of the performance of various room-temperature CO-sensing materials.
Sensing MaterialConc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
Pd/CuO Nanorods/SnSe2 Nanoflower2001.5313/58[6]
1 wt% Pt/SnO2 nanoceramics4002427-/-[7]
0.10Au-decorated ZnO nanosheets100139.7561/61[8]
Au-Loaded WS2/SnO2112.6-/-[18]
MOF-derived SnO2/NiO505.4856/4[9]
CuO/SnO2 Hollow-Sphere3001.40-/-[5]
NiO/Ti3C2Tx4001.438/16[11]
PANI/Ti3AlC2/CeO25001.17347/-[19]
MWCNTs/SnO23001.805/7[16]
rGO wrapped SnS2 nanosphere101011/10[17]
Table 2. Comparison of the room-temperature NO2 sensing performance of various 2D materials.
Table 2. Comparison of the room-temperature NO2 sensing performance of various 2D materials.
Sensing MaterialConc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
rGO/Bi2S319.822/106[23]
rGO/CuO511.0410/110[24]
rGO/CuO nanoflakes51.266.8/55.1[20]
vacancies-rich SnO2-RGO15.895/385[25]
rGO/In2O311177675/559[21]
8 wt% GO-mediated In2O311021413/182[26]
In2O3/Ti3C2 nanosheets100371.1918/95[22]
Ti3C2Tx 101.14-/-[27]
Ti3C2Tx-sphere-like CuO501.5713.5/20.9[28]
Ti3C2-I1201.1590/105[29]
Ti3C2Tx/WS211.15-/-[30]
V2CTx/SnS252.494.8/4.7[31]
MoSe2-WS20.051.6068.9/65.7[32]
GaSe0.58O0.4267.7548/378[33]
MoSe2/0.5–Co3O40.051.26139/20[34]
Table 3. Comparison of the room-temperature NO2 sensing performance of various non-2D materials.
Table 3. Comparison of the room-temperature NO2 sensing performance of various non-2D materials.
Sensing MaterialConc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/
Recovery Time (s)		Reference
MoO3@CoMoO4@CoMoS3.135035.421.1/-[35]
Te@Se Core−Shell17.2210/30[37]
ZnInS4103.282/3.7[36]
Hollow Co3O4 nanocages in NiO cilia10047.41.3/9.6[39]
UV-activated p-type CuCrO22503.60107/300[40]
SnO2 decorated Te nanotubes0.61.3332/109[38]
Au@In2S3/In2O310020.712/27[41]
Table 4. Comparison of the room-temperature H2-sensing performance of various materials.
Table 4. Comparison of the room-temperature H2-sensing performance of various materials.
Sensing MaterialConc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
flower-like In2O3/SnS210003.43-/-[47]
Mace-like In2O3@ZnO microtubules10,0006.67178/338[48]
5.0Pd@ZnO2001.53-/-[49]
Pd-Mg alloy thin films5001.1385/360[50]
3D In2O3@rGO@PPy aerogel100011.613/29[45]
Cactus-like ZnO@3D rGA10,00081.0715/27[46]
MoSe2 -WSe2251.6016/30[51]
Table 5. Comparison of the room-temperature CH4 sensing performance of various materials.
Table 5. Comparison of the room-temperature CH4 sensing performance of various materials.
Sensing MaterialConc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
Pt-SnO2/ZnO20003.73150/147[52]
In2O3−ZnO/Laser-Induced Graphene5001.3848/169[53]
Ag−Ru Co-doped ZnO Nanorods8001.8-/-[54]
Mulberry-Like ZnO/SnO2 Hierarchical Structure1001.27163/89[57]
Photo-activated Au-modified ZnO microsphere50004.65-/-[58]
UV-activated ZnO spheres100010.186/134[55]
UV-activated AuAg/ZnO500062.615/-[56]
NiO/ZnO50008.6132/182[59]
MWCNT with MOF (PCN-14)50,000-120/-[60]
MWCNT/Pd1001.3620/25[61]
Table 7. Comparison of the sensing performance of various MEMS MOS NO2 sensors.
Table 7. Comparison of the sensing performance of various MEMS MOS NO2 sensors.
Sensing MaterialTemperature
(°C)		Conc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
Au-Co3O4 NPs1360.11.3484/68[79]
CuO NWs1100.52-/-[80]
ZnO nanopowder2001041.641/-[81]
Hierarchically porous ZnO190103.429/26[82]
Oxygen-enriched SnO2 NPs1020.514.745.9/178.2[83]
Table 8. Comparison of the sensing performance of various MEMS MOS H2 sensors.
Table 8. Comparison of the sensing performance of various MEMS MOS H2 sensors.
Sensing MaterialWorking
Temperature (°C)		Conc.
(ppm)		Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
Pt/Nb-doped TiO2 nanoplate40100012.330/270[84]
rGO/Nb-doped TiO2-nanoplate10010002.532.5/58[85]
PdNPs@Al2O3200400-14/10[69]
oxygen vacancy-enhanced SnO2 25062.37/12[86]
Pd-SnO2 nanoflower clusters with F-WO3 microparticles3001518.710/22.5[87]
Table 9. Comparison of the sensing performance of various MEMS MOS CH4 sensors.
Table 9. Comparison of the sensing performance of various MEMS MOS CH4 sensors.
Sensing MaterialWorking
Temperature (°C)		Conc. (ppm)Response Value
(Rg/Ra or Ra/Rg)		Response/Recovery
Time (s)		Reference
Nb and Cr-doped SnO2400503.35-/-[88]
Co-doped ZnO nanorods5010001.19350/106[89]
PdPt/SnO232010005.2-/-[90]
7.5 at% Pt-SnO2 thin-film43012,500--/-[91]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, Y.; Li, R.; Guo, R.; Jiao, M.; Wang, G.; Zhao, Z. Recent Progress in Low-Power-Consumption Metal Oxide Semiconductor Gas Sensors. Materials 2025, 18, 4864. https://doi.org/10.3390/ma18214864

AMA Style

Zhang Y, Li R, Guo R, Jiao M, Wang G, Zhao Z. Recent Progress in Low-Power-Consumption Metal Oxide Semiconductor Gas Sensors. Materials. 2025; 18(21):4864. https://doi.org/10.3390/ma18214864

Chicago/Turabian Style

Zhang, Yu, Renbo Li, Ruqi Guo, Mingzhi Jiao, Gang Wang, and Zhikai Zhao. 2025. "Recent Progress in Low-Power-Consumption Metal Oxide Semiconductor Gas Sensors" Materials 18, no. 21: 4864. https://doi.org/10.3390/ma18214864

APA Style

Zhang, Y., Li, R., Guo, R., Jiao, M., Wang, G., & Zhao, Z. (2025). Recent Progress in Low-Power-Consumption Metal Oxide Semiconductor Gas Sensors. Materials, 18(21), 4864. https://doi.org/10.3390/ma18214864

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop