Intelligent chemical sensors: learning-enabled platforms for adaptive chemical detection

Abstract

Functional materials-based chemical sensors play a crucial role in industrial process control, environmental monitoring, medical diagnostics, and safety assurance. Nearly all conventional chemical sensors rely on static material properties and specific operating parameters despite substantial advances in sensing materials and device fabrication, prompting minimal adaptability under inconsistent and complex environments. On the grounds of these constraints, there has been growing interest in developing intelligent chemical sensing, where adaptive behaviour is employed to improve selectivity, robustness, and prolonged stability. This review envisages intelligent chemical sensors as learning-enabled platforms for adaptive chemical detection while propounding a materials-centric yet system-aware vantage on intelligent chemical sensors. The integration of intelligence through the sensing pipeline, adaptive transduction strategies, encompassing hybrid materials and responsive ceramics, learning paradigms, and incorporated sensing architectures is discussed here. Functional materials are intended to enable selectivity, plasticity, drift mitigation, and dynamic sensitivity, while endorsed by system-level incorporation and learning-assisted signal interpretation. Reliable chemical detection in elaborate environments executed by intelligent material device system coupling is highlighted in representative examples. Key challenges associated with material stability, interpretability, data scarcity, and energy efficiency are critically examined besides emerging research directions such as memory-enabled sensing interfaces, chemically adaptive materials, and autonomous sensing ecosystems. This review intends to bridge materials innovation and intelligent system design, proposing perceptiveness for the evolution of next-generation adaptive chemical sensors.