TY - JOUR
T1 - Smart Metal Oxide Gas Sensors with Catalytic and Artificial Intelligence–Driven Selectivity
AU - Heon Kim, Sang
AU - Kim, Yonggi
AU - Sol Choi, Han
AU - Joon Kim, Jae
AU - Min Baik, Jeong
N1 - Publisher Copyright:
© The Korean Sensors Society.
PY - 2025/5
Y1 - 2025/5
N2 - This review summarizes recent progress in metal oxide-based gas sensors, focusing on material design, catalytic engineering, and real-time sensing strategies. Advances in nanostructured materials, hetero junctions, and noble metal catalysts have significantly improved sensor sensitivity, selectivity, and stability. Techniques such as Schottky barrier modulation, spill-over effects, and interfacial charge transfer are key to enhancing gas response. Additionally, integrating sensor arrays with artificial intelligence (AI)-based analysis, including Edge AI and convolutional neural networks, enables accurate, low-power, and real-time gas detection. These combined strategies pave the way for next-generation gas sensors suitable for diverse applications in environmental monitoring, safety, and healthcare.
AB - This review summarizes recent progress in metal oxide-based gas sensors, focusing on material design, catalytic engineering, and real-time sensing strategies. Advances in nanostructured materials, hetero junctions, and noble metal catalysts have significantly improved sensor sensitivity, selectivity, and stability. Techniques such as Schottky barrier modulation, spill-over effects, and interfacial charge transfer are key to enhancing gas response. Additionally, integrating sensor arrays with artificial intelligence (AI)-based analysis, including Edge AI and convolutional neural networks, enables accurate, low-power, and real-time gas detection. These combined strategies pave the way for next-generation gas sensors suitable for diverse applications in environmental monitoring, safety, and healthcare.
KW - Catalytic engineering
KW - Edge artificial intelligence
KW - Metal oxide gas sensors
KW - Realt imeg as detection
KW - Selectivity enhancement
UR - https://www.scopus.com/pages/publications/105008237317
U2 - 10.46670/JSST.2025.34.3.208
DO - 10.46670/JSST.2025.34.3.208
M3 - Review article
AN - SCOPUS:105008237317
SN - 1225-5475
VL - 34
SP - 208
EP - 223
JO - Journal of Sensor Science and Technology
JF - Journal of Sensor Science and Technology
IS - 3
ER -