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Single-atom engineered materials for smart gas sensing: Recent progress and emerging strategies.
Shaowei Li1, Chuanxuan Zhou1, Fuchao Yang2
1Ministry of Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, Hubei University, Wuhan 430062, PR China.
Advances in Colloid and Interface Science
|February 6, 2026
Summary
Single-atom catalysts (SACs) offer advanced gas sensing with high accuracy and selectivity. Integrating SACs into sensor arrays with machine learning enables reliable detection of complex gas mixtures.
Area of Science:
- Materials Science
- Chemical Sensing
- Nanotechnology
Background:
- Next-generation gas sensors require enhanced accuracy, stability, and selectivity.
- Single-atom catalysts (SACs) show promise due to high catalytic activity and atomic utilization.
- Interface interactions in SACs enable efficient gas response and low power consumption.
Purpose of the Study:
- To summarize common monoatomic metals and their synthesis for SACs.
- To emphasize SAC integration into sensor arrays for expanded gas recognition.
- To explore the combination of SACs, sensor arrays, and machine learning for intelligent gas sensing.
Main Methods:
- Comprehensive review of monoatomic metals (Pt, Pd, Fe, Co, Ni, Cu) and their synthesis.
- Integration strategies of SACs into sensor arrays to create multidimensional response patterns.
- Application of machine learning algorithms for classification and prediction of gas mixtures.
Main Results:
- SACs exhibit excellent gas response characteristics with low power consumption.
- Sensor arrays with SACs expand gas recognition capabilities.
- Machine learning enables reliable real-time classification and concentration prediction of complex gas mixtures.
Conclusions:
- The fusion of SACs, sensor array engineering, and artificial intelligence (AI) represents the future of intelligent gas sensing.
- This approach has significant potential in environmental monitoring, industrial safety, and medical diagnostics.
- Future directions include high-throughput SAC synthesis, heterogeneous SAC array design, and on-device AI frameworks.
Keywords:
Interface interactionMachine learningSensor arraySingle-atom catalystsSurface engineeringSynthetic methodMore Related Videos
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