Smart Metal Oxide Gas Sensors with Catalytic and Artificial Intelligence–Driven Selectivity

1. INTRODUCTION

Gas sensors are critical components in modern sensing technologies that enable the real-time detection and monitoring of various gaseous species with high sensitivity, selectivity, and reliability, thereby playing a vital role in modern life and contributing significantly to the pursuit of safety, well-being, and happiness [1-3]. As our society advances toward smarter, safer, and more sustainable systems, the demand for precise gas detection continues to increase across both industrial and consumer domains [4,5]. Representative gas sensing technologies include metal oxide semiconductor (MOS) sensors, electrochemical sensors, infrared (IR) sensors, catalytic bead sensors, and photoionization detectors (PIDs) [6-8]. These sensors serve as fundamental building blocks in a wide array of applications including environmental monitoring, industrial process control, automotive systems, healthcare diagnostics, and intelligent home appliances. In particular, rapid advancements in nanotechnology have led to significant progress in miniaturization and low power consumption, thereby enabling the development of compact and energy-efficient gas sensors [9,10].

Reliable gas sensing technologies are essential for addressing real-world challenges that directly impact human safety, public health, and environmental quality [11-13]. In industrial environments, undetected gas leaks can result in catastrophic explosions or occupational hazards, often causing significant loss of life and property damage [14]. Similarly, drunk driving poses a severe risk, and its early detection can prevent accidents, thereby enhancing public safety, reducing medical and legal costs, and ultimately saving lives [15]. Air pollution, although stemming from various sources, is primarily driven by vehicular emissions and fine particulate matter (PM) in urban areas, leading to serious respiratory health issues and contributing to global climate change [16]. Consequently, atmospheric monitoring of greenhouse gases and PM has become a central strategy in both national and international efforts to combat environmental degradation [18-20]. Moreover, the increasing frequency of large-scale wildfires in regions such as South Korea and the United States has resulted in devastating human and economic losses while releasing vast amounts of CO₂, thereby accelerating climate change. In addition to these environmental and safety concerns, gas sensing plays a vital role in the food industry [21]. Monitoring the emission of gases such as NH₃ and C₂H₄ from food products is crucial for assessing freshness and preventing spoilage of the food products that in turn helps extend their shelf life, reduce their wastage, and ensure consumer safety [22]. These diverse applications underscore the critical role of gas sensors in enabling real-time, on-site detection and deliv-ering actionable data to prevent or mitigate a wide range of harmful outcomes.

Various types of gas sensors have been successfully used in both commercial and industrial settings. For example, MOS-based sensors such as Figaro TGS2611 and TGS2610 are widely used in residential gas leak detectors to sense CH₄ and liquefied petroleum gas (LPG). In automotive applications, MOS-based alcohol sensors, such as ALCOSCAN AL1100 by SENTECH KOREA, are commonly used to detect alcohol concentration in drivers with high sensitivity and reliability, whereas O₂ sensors, such as Bosch 15703, utilize a zirconia-based electrochemical sensing mechanism to help optimize combustion efficiency and reduce harmful emissions. Wildfire detection systems commonly employ gas sensors (for example, CO and CO₂), temperature sensors, and smoke sensors such as MOS and IR types to enable real-time monitoring and early warning in remote forest environments. In the food packaging industry, gas sensors are used to monitor CO₂ and O₂ con-centrations to ensure the freshness of perishable products during transport and storage; commercial examples include Dansensor CheckPoint 3 by AMETEK MOCON. Addition-ally, recent developments in wearable sensors have enabled the noninvasive monitoring of exhaled biomarkers such as acetone or nitric oxide, offering the potential for personalized health-care diagnostics. These examples illustrate the widespread applicability and evolving sophistication of gas sensing tech-nologies in addressing both traditional and emerging chal-lenges.

However, despite the wide applicability and rapid progress in sensor technologies, several key challenges such as high response, high selectivity, and long-term stability still hinder their widespread deployment, particularly in real-world and harsh environmental conditions [23-25]. Many sensors are cross sensitive to multiple gases, often leading to false pos-itives and inaccurate interpretations. In addition, environmental factors such as temperature, humidity, and background gas interference can further degrade the sensor performance [26-28]. For example, MOS-based sensors, although widely used in household gas leak detectors and automotive systems owing to their high sensitivity and low cost, generally require high operating temperatures and demonstrate poor reliability under humid conditions [29-32]. The importance of overcoming these limitations is clearly highlighted by the strict air quality guidelines established by international and national agencies, as shown in Fig. 1 (a) [33]. For instance, the World Health Organization (WHO) recommends maximum concentrations of 50 µg/m³ for PM 10 and 25 µg/m³ for PM 2.5, whereas Korea sets even stricter standards for critical facilities—75 µg/m³ and 35 µg/m³, respectively. CO₂ concentrations are typically limited to 1,000 ppm in many countries, and formaldehyde (HCHO) must remain below 100 µg/m³ or even 80 µg/m³ in sensitive environments. Owing to its acute toxicity, CO con-centration is limited to as low as 9-10 ppm, depending on the exposure time. These quantitative thresholds, applied in set-tings such as hospitals, schools, residential buildings, and industrial workplaces, emphasize the urgent need for gas sen-sors that can operate reliably under diverse conditions while maintaining accuracy, stability, and rapid response. Meeting these standards is essential not only for regulatory compliance but also for protecting public health, ensuring safety, and enabling advanced applications such as environmental mon-itoring, fire detection, air purification, and smart healthcare systems.

Fig. 1.

Material and synthesis engineering for metal oxide gas sensors; (a) National and international indoor air quality standards. Reprinted with permission from Ref. [33], Copyright (2019) KEI, licensed under the Korea Open Government License Type 1 (KOGL Type 1). (b) Most extensively studied and commercially applied metal oxide (left-top), PEDOT:PSS structure (right-top), graphene structure (right-bottom), and TMDCs structure. Reprinted with permission from Ref. [111], [50], [112], and [59], Copyright (2010) MDPI, (2020) American Chemical Society, (2019) Springer Nature, and (2024) American Chemical Society, respectively, licensed under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). (c) Gas sensing mechanism of n-type semiconducting metal oxides (left) and the CO sensing mechanism of SnO2 (right). Reprinted with permission from Ref. [113] and [65], Copyright (2020) Wiley and (2016) MDPI, respectively, licensed under CC-BY-4.0. (d) Gas sensing mechanism of p-type semiconducting metal oxides (left) and the H2S sensing mechanism of CuO (right). Reprinted with permission from Ref. [113] and [114], Copyright (2020) Wiley and (2021) Elsevier, respectively, licensed under CC-BY-4.0. (e) Hydrothermal method (left) and SEM image (right) of ZnO nanorods. Reprinted with permission from Ref. [74], Copyright (2021) RSC, licensed under CC-BY-4.0. (f) Heat-up method (left) and SEM image (right) of SnO2 nanoparticles. Reprinted with permission from Ref. [75], Copyright (2020) RSC. (g) Microwave-assisted synthesis (left) and SEM image (right) of porous CuO. Reprinted with permission from Ref. [115] and [76], Copyright (2022) Nature and (2022) American Chemical Society, respectively, licensed under CC-BY-4.0. (h) Oblique electron beam deposition (left) and SEM image (right) of TiO2 nanorods. Reprinted with permission from Ref. [77], Copyright (2015) Springer Nature, licensed under CC-BY-4.0.

To address these limitations, recent research efforts have focused on the design of advanced sensing materials, nano-structures, and device architectures that enhance selectivity, sensitivity, and environmental robustness [34-36]. Strategies such as surface functionalization, heterojunction engineering, and the incorporation of catalytic layers have shown promise in improving selectivity toward target gases while minimizing cross-interference. The development of low-temperature sen-sors using novel materials—such as 2D transition metal dichal-cogenides (TMDCs), metal–organic frameworks (MOFs), and conductive polymers—also offers pathways to overcome ther-mal constraints [37-39]. Furthermore, the integration of sensor arrays with data processing algorithms based on machine learning is gaining traction, enabling pattern recognition and a more accurate discrimination between complex gas mixtures [40,41]. From a systems perspective, scalable fabrication methods, flexible substrates, and wireless communication modules are being actively explored to facilitate the devel-opment of compact, low-power, and user-friendly sensor plat-forms. These ongoing innovations are crucial for translating laboratory-scale prototypes into reliable, market-ready solu-tions capable of satisfying the diverse demands of real-world gas sensing applications.

In this review, we focus on recent advancements in gas sen-sor technologies based on MOS, with particular emphasis on material design and catalytic engineering to enhance sensing performance. Specifically, we examine strategies for tailoring the composition, morphology, and surface chemistry of metal oxide materials to improve their gas reactivity, selectivity, and long-term stability. Various catalytic approaches—such as noble metal decoration, heterojunction formation, and surface functionalization—are discussed in the context of promoting target gas adsorption and reaction kinetics while suppressing interference from non-target species. Additionally, we explore emerging methodologies aimed at improving selectivity, including operating temperature modulation, the use of catalysts, and sensor arrays. Finally, we highlight the growing role of signal processing techniques, including pattern recognition algorithms and machine learning, in enabling a more accurate and robust gas detection. By integrating advances in materials science, catalytic design, and data analysis, this review aims to provide a comprehensive overview of the current challenges and future directions in the development of high-performance metal oxide gas sensors.

2. MATERIAL AND SYNTHESIS ENGINEERING FOR METAL OXIDE GAS SENSORS

3. CATALYTIC EFFECTS IN HIGH PERFORMANCE GAS SENSORS

The gas sensing mechanism of metal oxide-based sensors involves complex and diverse processes that govern the interactions of the material with target gas molecules and transduces chemical signals into measurable electrical changes [78,79]. As illustrated in Fig. 2, these mechanisms can be broadly categorized into the following four major types: (1) Schottky barrier modulation, (2) spillover effect, (3) interface charge transfer, and (4) gas-induced chemical phase transformation [75,80-82]. Each process plays a critical role in modulating charge carrier dynamics, surface reaction rates, band alignment, and long-term stability that collectively determine the sensor performance [83].Notably, rather than acting independently, these mechanisms are closely interrelated, with each process potentially modulating and reinforcing the effects of the others [84-86]. In general, these approaches employ noble metal decoration, heterojunction formation, or core–shell architectures. Therefore, a fundamental understanding of the underlying mechanisms is crucial for the rational design and optimization of gas sensors with high performance and long-term stability, particularly for detecting low-concentration analytes in complex environments.

Fig. 2.

Catalytic effects in high performance gas sensors; sensing mechanism (left), selectivity (right) of (a) Schottky barrier modulation, (b) spill-over effect, (c) interface charge transfer, and (d) gas-induced chemical phase transformation. Reprinted with permission from Ref. [80], [81], [75], and [82], Copyright (2024) American Chemical Society, (2022) RSC, (2020) RSC, and (2021) RSC, respectively, licensed under the Creative Commons Attribution 3.0 International License (CC-BY-3.0).

The sensing behavior of metal oxide gas sensors is fundamentally driven by electronic effects that arise from gasinduced modifications in the band structure, charge redistribution, and interfacial potential barriers. A representative example is the recent study on Ag nanoparticle-decorated SnS₂/SnO₂ nano-heterojunctions for low-temperature NO₂ sensing, as illustrated in Fig. 2 (a) [80]. In this system, a heterojunction is first formed between SnS₂ and SnO₂, both exhibiting an n-type behavior that creates a built-in potential at the interface owing to their differing electron affinities. When Ag nanoparticles (Ag NPs), possessing a higher work function than both the semiconductors, are introduced onto the surface, they withdraw electrons from the underlying oxides, forming Schottky contacts and locally widening the depletion region. This effect increases the baseline resistance of the sensor. Upon NO₂ exposure, the strongly oxidizing gas interacts with the pre-adsorbed oxygen ions and further extracts electrons from the conduction band of the oxides. The result is a pronounced modulation of the interfacial barrier height at the Ag/SnO₂ and Ag/SnS₂ junctions, leading to a substantial increase in the resistance and enhanced sensor response. The device exhibits a clear and sharp response to 7 ppm of NO₂ at 100°C, with a response ratio of 56.4 that is nearly four times higher than that of pristine SnS₂. In addition to the enhanced sensitivity, the sensor demonstrates a rapid response/recovery behavior and reliable cycling stability under repeated gas exposure. This quantitative analysis demonstrates the manner in which electron transfer and interfacial band engineering at metal/oxide junctions can be harnessed to design highly sensitive, selective, and thermally stable gas sensors.

The spillover effect is a catalytic mechanism involving the migration of reactive species—typically atoms generated via dissociation on the surface of catalytic materials—from the catalyst to the support, thereby enhancing the overall reactions, particularly in metal oxides decorated with noble metal nanoparticles. As illustrated in Fig. 2 (b), when Pd nanoparticles are decorated onto Co₃O₄, they serve as highly active catalytic sites for the dissociation and activation of target gas molecules [81]. In this system, Pd facilitates the adsorption and activation of hydrogen molecules that are then efficiently transferred to the Co₃O₄ surface. This process accelerates the charge transfer between the gas molecules and the sensing material, resulting in a pronounced modulation of the sensor conductivity. The electron depletion layer formed at the Pd–Co₃O₄ interface further enhances band bending, amplifying the sensor response. Notably, the Pd–Co₃O₄ sensor exhibits a significantly higher response to hydrogen than pristine Co₃O₄, even at low concentrations. Additionally, the optimal operating temperature for the Pd–Co₃O₄ sensor is reduced, indicating that the Pd nanoparticles promote gas-phase reactions with lower thermal energy requirements. These results confirm that the spillover effect and catalytic activation by Pd not only amplify the gas response but also improve the sensitivity and selectivity at lower temperatures that is critical for practical, low-power gas sensing applications.

The interface charge transfer mechanism is also a key sensing principle, in which electronic interactions at the junction between two materials govern charge carrier dynamics and enhance sensing performance. As shown in Fig. 2 (c), the SnO₂-based NO₂ sensor decorated with nitrogen-doped carbon dots (N-CDs) exhibit significantly improved NO₂ sensing capabilities [75]. Upon exposure to NO₂, the molecules are adsorbed onto the carbon dots as NO₂^- species, extracting electrons from the carbon dots—a phenomenon confirmed by X-ray photoemission spectroscopy. The formation of a p-n heterojunction between the p-type N-CDs and n-type SnO₂ further promotes an efficient charge transport. In addition, the carbon dots provided an increased number of NO_x adsorption sites, collectively contributing to an enhanced sensing response. In the case of pristine SnO₂, the sensor response increases up to 6.3 at 200°C but declines at 250°C. In contrast, the N-CDs functionalized SnO₂ sensor achieves a maximum response of 4336 at only 50°C and demonstrates superior responsiveness across the entire temperature range. Furthermore, when exposed to various gases at 1 ppm concentration (H₂S, NO₂, SO₂, NH₃, and CO) at 150°C, the sensor exhibits high selectivity toward NO₂.

The gas-induced chemical phase transformation represents a powerful strategy for achieving high selectivity and long-term reliability in metal oxide-based gas sensors. This mechanism involves a phase or compositional change in the sensing material upon exposure to specific gas molecules, resulting in substantial variations in electrical conductivity. A representative example is the redox interaction between CuO and H₂S, that leads to the formation of Cu₂S [82]. Typically, CuO undergoes a phase transformation to CuS at relatively low temperatures (< 160°C) in the presence of H₂S, whereas the reverse reaction—regeneration of CuO via desulfurization—occurs at elevated temperatures (> 300°C). Because CuS exhibits metallic conductivity, this transition causes a dramatic change in the electrical response of the sensor. However, the irreversibility and harsh conditions required for regeneration pose challenges for practical applications, limiting the industrial feasibility of CuO-based catalysts for gas sensing.

To address this issue, Baik et al. introduced Nb₂O₅ as a catalyst to enable both the sulfidation and oxidation reactions to occur at the same temperature, aligning with the thermodynamic driving forces of product formation and reversibility—conditions necessary to ensure high sensing stability, as shown in Fig. 2 (d). Among various candidates, Nb₂O₅ is selected owing to its well-established role as an oxide promoter in hydrodesulfurization (HDS) catalysts, its high oxygen mobility, excellent redox properties, relatively low cost, and high positive standard Gibbs free energy (ΔG°) for sulfidation. Based on thermodynamic calculations and its demonstrated HDS activity, Nb₂O₅ facilitates CuO sulfidation and oxidation within a practically relevant temperature window. Specifically, CuO-Nb₂O₅ composites enable efficient chemical transformation-based H₂S sensing within the range of 160–220°C. At 220°C, dynamic sensing curves show a clear and stable response to H₂S concentrations ranging from 500 to 1500 ppb, with a detection limit as low as 70 ppb. Furthermore, the CuO–Nb₂O₅ sensors exhibit excellent selectivity toward H₂S over SO₂ and show a rapid, reproducible response–recovery behavior even at 500 ppb. Furthermore, long-term stability tests reveal that, whereas pristine CuO sensors suffer from significant degradation over 30 days, the CuO–Nb₂O₅ composites maintain consistent performance with minimal signal drift. These results suggest that the incorporation of Nb₂O₅ not only stabilizes the Cu-based sensing phase but also facilitates reversible phase transitions, thereby enhancing both the reliability and longevity of the sensor.

4. STRATEGIES FOR ENHANCING SELECTIVITY IN METAL OXIDE GAS SENSORS

The selectivity of metal oxide-based gas sensors plays a critical role in determining their applicability for real-world gas monitoring, particularly in mixed-gas environments [87,88]. The following two main strategies exist for gas detection: one strategy involves sensors comprising sensing and catalytic materials that respond specifically to a single target gas, and the other strategy uses an array of multiple sensors to selectively identify various gases through pattern recognition. The first strategy focuses on enhancing selectivity by tailoring the chemical affinity of the sensing material to a specific gas, often by incorporating catalysts that modulate surface reactivity and adsorption characteristics [89-91]. In contrast, the sensor array approach mimics the biological olfactory system, relying on cross-reactive responses and advanced signal processing algorithms [92-94]. This enables the simultaneous detection of multiple gases even in complex environments with potential interference.

H₂ gas is a colorless, odorless, and highly flammable molecule, widely used in energy, chemical, and semiconductor industries [95]. Because of its small molecular size and high diffusivity, H₂ easily leaks and accumulates, posing serious safety risks [96]. However, the selective detection of H₂ is challenging because its small size and high diffusivity often lead to nonspecific interactions with a wide range of sensing materials [97,98]. Moreover, H₂ often exhibits cross-sensitivity to other reducing gases such as CO, CH₄, and NH₃; hence, isolating its signal in complex environments is difficult [99]. SnO₂ thin films offer a versatile platform for hydrogen sensing, with performance tunable through post-deposition annealing. For example, Jung et al. systematically investigated the effects of annealing temperature on RF-sputtered SnO₂ films, as shown in Fig. 3 (a) [89]. Annealing at 400°C resulted in improved grain growth and oxygen vacancy concentration, enhancing H₂ adsorption and electron transport. The optimized sensor exhibited a sharp, selective response to H₂ at 200°C, with minimal cross-sensitivity to CO, CH₄, and NO₂. These results highlight the manner in which controlled annealing enhances the structural and electronic properties critical for selective hydrogen detection.

Fig. 3.

Strategies for enhancing selectivity in metal oxide gas sensors; (a) Mechanism (left) and H2 selectivity (right) via the SnO2 thin film sensor. Reprinted with permission from Ref. [89], Copyright (2022) MDPI, licensed under CC-BY-4.0. (b) Mechanism (left) and NO2 selectivity (right) of the La2O3-loaded WO3 sensor. Reprinted with permission from Ref. [90], Copyright (2023) American Chemical Society. (c) Mechanism (left) and Acetone selectivity (right) of the SnO2–Co3O4 composite sensor. Reprinted with permission from Ref. [91], Copyright (2023) American Chemical Society. (d, e) Sensor array structure (left) and LDA (right) of (d) the Pd, Ag-functionalized SnO2 nanowire array. Reprinted with permission from Ref. [92], Copyright (2010) American Chemical Society. (e) Junction-engineered CuO and ZnO nanowire arrays. Reprinted with permission from Ref. [93], Copyright (2013) American Chemical Society. (f) Sensor array structure (left) and PCA (right) of Pt promoting SnO2, CuO, In2O3, and ZnO arrays. Reprinted with permission from Ref. [94], Copyright (2023) Frontiers, licensed under CC-BY-4.0.

NO_x gases, including NO and NO₂, are the major air pollutants emitted by combustion engines and industrial processes [100]. They are toxic even at low concentrations and play a critical role in the formation of smog and acid rain, making their detection essential for environmental monitoring [101,102]. However, the selective detection of NO_x gases is challenging because of their similar oxidative behavior to other gases such as O₃ and Cl₂ [103]. This often results in cross-sensitivity, where the sensor responses overlap with those of other oxidizing species in complex environments. To overcome this problem, advanced sensing materials or catalytic filters are required to differentiate NO_x gases from chemically similar interferents. Liewhiran et al. demonstrated that La₂O₃-loaded WO₃ heterostructures significantly enhanced the NO₂ sensing performance by modulating the surface electronic structure and promoting oxygen adsorption, as shown in Fig. 3 (b) [90]. The 2.0 wt.% La₂O₃–WO₃ composite exhibited the highest gas response of 47.3 (R_a/R_g) to 5 ppm NO₂ at 160°C, with rapid response/recovery times of 20/25 s and excellent selectivity over interfering gases such as NH₃, CO, H₂, and ethanol. Density functional theory (DFT) calculations indicated that La doping increased the oxygen vacancy density and enhanced the electron transfer efficiency on the WO₃ surface. La₂O₃ acted as a Lewis base, facilitating NO₂ molecule adsorption and spillover, whereas WO₃ served as the electron conduction path. The sensor maintained more than 90% of its original response after 30 d, highlighting its outstanding stability, ppb-level detection potential, and strong application promise for real-time NO₂ monitoring.

Similarly, Ghosh et al. developed a CeO₂–CNT composite sensor with improved selectivity toward acetone, as shown in Fig. 3 (c) [91]. Acetone is a volatile organic compound (VOC) commonly used as a solvent in industrial processes, pharmaceuticals, and cosmetics [104]. Because of its high vapor pressure and low molecular weight, it readily evaporates and can be detected in both indoor and exhaled breath environments [105]. However, the selective detection of acetone is challenging because it shares similar chemical and physical properties with other VOCs such as ethanol, methanol, and isopropanol [106,107]. The enhanced acetone sensing performance arises from the formation of a CeO₂ quantum dot-decorated CNT heterostructure that facilitates the formation of multiple nanojunctions, high oxygen vacancy levels, and mesoporosity. These features significantly improve gas diffusion, surface adsorption, and electron transport. The CeO₂/CNT sensor exhibited an ultrahigh response of 10,890 toward 68 ppm acetone at room temperature (27°C) with ultrafast response/recovery times of 56 ms/22 ms. Additionally, the sensor showed excellent selectivity—its response to 41 ppm acetone (6319) was more than 2000 times higher than that to ethanol. These results demonstrated the synergistic effects of enhanced surface area, oxygen vacancies (Ce³⁺/Ce⁴⁺ ≈ 0.51), and conductive CNT networks. Based on these mechanisms, the sensor exhibited a strong response to acetone, while effectively suppressing responses to other VOCs such as methanol, toluene, and methane.

As aforementioned, sensor arrays are powerful platforms for the selective and reliable detection of multiple gases in complex environments. By integrating multiple sensing elements with distinct material properties or surface functionalities, sensor arrays—also known as electronic noses (E-noses)—enable pattern-based gas recognition through differential response profiles. One representative approach is catalyst-specific tuning, as demonstrated by Moskovits et al. who developed an SnO₂ nanowire sensor array modified with Pd and Ag nanoparticles, as shown in Fig. 3 (d) [92]. The sensing enhancement of Pd–SnO₂ is primarily attributed to the spillover effect, where Pd catalyzes the dissociation of gas molecules and facilitates their migration onto the SnO₂ surface, thereby increasing the reaction activity. In contrast, Ag–SnO₂ improves the sensing performance through an electronic effect by modulating the Schottky barrier at the Ag/SnO₂ interface that alters the charge carrier transport. Thus, whereas Pd induces a chemical sensitization mechanism, Ag enhances the sensing via electronic sensitization. Notably, Pd-decorated SnO₂ selectively enhances responses to H?and CO, whereas Ag-decorated SnO₂ exhibits higher selectivity toward C₂H₄, with suppressed responses to CO and H₂. The sensors exhibit unambiguous discrimination between three reducing gases (H₂, CO, and C₂H₄), highlighting their high selectivities.

Temperature gradients enable excellent selective gas detection. However, maintaining a stable temperature difference is challenging and often requires high power consumption. As alternative approaches to constructing sensor arrays, material variation and junction engineering between different materials have emerged as effective strategies. Park et al. reported an alternatively driven dual-nanowire system comprising an n-type ZnO and a p-type CuO, each with an average diameter of approximately 30 nm, grown on a single substrate, as shown in Fig. 3 (e) [93]. These nanowires are arranged in an array to form n-n, p-p, and p-n junctions. The transport characteristics of the n-n, p-p, and p-n junctions in the nanowire array differ because of the charge carrier dynamics and band alignment. The n-n junction exhibits symmetric nonlinear Schottky-like behavior, driven by electron transport through depletion zones influenced by surface and bulk carrier differences, with the barrier height modulated by adsorbed oxygen. The p-n junction shows rectifying behavior owing to the work function and band gap mismatch between CuO and ZnO, where the current mainly flows through the electrons. In contrast, the p-p junction demonstrates near-linear I–V characteristics, with hole transport occurring through a surface accumulation layer, thereby minimizing the role of potential barriers. Based on these transport properties, in the n-n junction, the current—known as thermionic emission current—must pass over the barrier height at the overlapping nanowires because of the presence of a depletion zone. In contrast, in the p-p junction, holes flow through a hole accumulation layer formed in air without encountering a significant barrier. For the p-n junction, electrons cross a potential barrier; however, the sensor resistance is additionally influenced by surface chemical reactions with analytes on CuO that can reduce gas sensitivity. These distinct transport mechanisms enable the sensor array to effectively differentiate between the junction types. The sensors showed successful discrimination with three gases (H₂, CO, and NO₂) using linear discriminant analysis (LDA).

To further enhance the selectivity and tunability of sensor arrays, recent studies have focused on the simultaneous modification of the sensing materials and catalytic interfaces. Chen et al. developed a 3D nanotube array of SnO₂ integrated onto porous alumina, featuring nanotubes with diameters of approximately 70 nm and lengths of 40 μm and introduced approximately 5 nm Pt nanoparticles as catalytic promoters, as shown in Fig. 3 (f) [94]. The incorporation of Pt facilitated H₂ dissociation via catalytic spillover and significantly reduced the activation energy for surface reactions, resulting in strong responses of 11% at 100 ppm and 67% at 4000 ppm at room temperature. The Pt–SnO₂ interface promoted electron donation to the SnO₂ conduction band, enhancing the baseline conductivity and improving the sensitivity. Combined with the hollow tubular geometry that enabled efficient radial gas diffusion, the sensor achieved ultralow power consumption (12.5 μW) and successfully discriminated gases such as NO₂ and benzene at 5 ppm using principal component analysis (PCA). These findings demonstrate that noble metal catalytic layers can fundamentally modify surface reaction kinetics and significantly improve both the sensitivity and selectivity of metal oxide-based sensor arrays.

5. EDGE AI SYSTEM FOR REAL-TIME GAS DETECTION AND CALIBRATION

To implement a real-time detection system, research has been conducted to integrate Edge AI into gas sensor systems [108]. Fig. 4 (a) presents the overall architecture of the Edge AI system, where the analog front-end (AFE) processes raw sensor signals. The processed data are analyzed using an AI model to classify the gas types and estimate their concentrations. The integration of Edge AI significantly reduces the data transmission volume compared to conventional systems, thereby lowering communication power consumption and improving efficiency for low-power applications. Fig. 4 (b) demonstrates the calibration mechanism within the AFE that employs a readout integrated circuit (ROIC) and microcontroller unit (MCU) to maintain sensor accuracy [109]. This calibration process effectively mitigates sensor drift and environmental variations, resulting in enhanced long-term data reliability. The ROIC utilizes an eight-bit resistive/current digital-to-analog converter (RDAC/IDAC) to adjust the sensor resistance, with the IDAC providing finer resolution. The differential signals are converted via an ADC and supplied to the AI model to ensure a stable and reliable input.

Fig. 4.

Edge AI System for real-time gas detection and calibration; (a) Gas signal acquisition and preprocessing mechanism for real-time detection. The system integrates a gas sensor, an analog front-end (AFE), and AI processing to enable low-power, real-time gas classification. (b) Block diagram of the AFE that includes the calibration circuit in the preprocessing stage. The circuit corrects sensor offset and sensitivity variations. (c) Schematic representation of the calibration circuit operation. The process consists of base calibration for offset correction and slope calibration for sensitivity adjustment, ensuring accurate measurements. Reprinted with permission from Ref. [108], Copyright (2022) IEEE. (d) AI learning strategy and gas classification results. A time window-based method converts sensor signals into 2D data for AI training, improving classification accuracy for various gases.

The calibration algorithm is depicted in Fig. 4 (c), where the RDAC and IDAC adjustments compensate for environmental changes, thereby improving the sensitivity. This enhancement contributes to a significantly higher inference accuracy and reliability in AI predictions. Convolutional neural networks (CNN) have been validated for their efficacy in real-time data analysis because of their capability to extract meaningful features from complex signals [110]. Fig. 4 (d) illustrates the CNN-based real-time prediction model that employs a sliding time window approach to transform sensor signals into feature maps. The CNN effectively analyzes signal patterns through convolutional layers, leading to enhanced classification accuracy and faster detection of gas types. This approach also improves adaptability to dynamic environmental conditions, making it suitable for real-time applications.

6. SUMMARY AND OUTLOOK

This review highlights the recent advancements in metal oxide-based gas sensors, focusing on material innovations, catalytic engineering, selectivity enhancement strategies, and the integration of Edge AI for real-time detection. Through the development of novel nanostructures such as 1D nanorods, 2D heterostructures, and core–shell systems, along with synthesis techniques such as hydrothermal, heat-up, and microwave-assisted methods, researchers have achieved significant improvements in sensor sensitivity, stability, and power efficiency. Catalytic strategies—including Schottky barrier modulation, spill-over effects, interfacial charge transfer, and chemical phase transformation—have further enabled selective gas detection in complex environments. The integration of noble metal nanoparticles and junction engineering between p- and n-type semiconductors has been particularly effective in tailoring gas selectivity and lowering operational temperatures. Concurrently, the use of sensor arrays and data processing algorithms such as PCA and LDA has enhanced the recognition of multiple gases, even at low concentrations.

The convergence of advanced materials and AI-driven electronics represents a promising path toward next-generation gas sensing systems. The incorporation of Edge AI enables low-power, real-time analysis, reduces reliance on cloud-based computation, and enhances system autonomy—features that are critical for wearable, portable, and distributed sensor networks. Calibration techniques integrated within AFE circuitry improve long-term reliability, compensating for environmental drifts and sensor degradation. Moreover, the application of deep learning models such as CNNs enables complex signal interpretation, facilitating highly accurate gas classification under dynamic conditions. Future research will likely focus on multifunctional sensing platforms capable of simultaneous detection of diverse gas species, further miniaturization for IoT applications, and improved robustness under harsh operating conditions. By bridging advances in material science, device architecture, and intelligent signal processing, metal oxide gas sensors are well-positioned to satisfy the growing demands of environmental monitoring, public safety, and smart healthcare.

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government Ministry of Science and ICT (MSIT) (No. RS-2025-00520713).

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