Solid-substrate colorimetric sensors for gaseous formaldehyde detection: a review

This review systematically examines and critically evaluates the development of solid-substrate colorimetric sensors for gaseous formaldehyde detection over the past two decades, with a focus on the effects of sensing-molecule reaction mechanisms, substrate properties, and testing conditions on sensing performance. Based on formaldehyde recognition mechanisms, the sensing systems are classified into five groups: 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (AHMT), hydroxylamine sulfate, primary amines, electron-rich carbonyl compounds, and metal-based systems. Comparative analysis shows that AHMT, hydroxylamine sulfate, and electron-rich carbonyl compound systems can achieve low-concentration formaldehyde detection. The chemical properties of sensing molecules primarily determine formaldehyde recognition mechanisms and color response, whereas substrate pore structure, specific surface area, surface properties, and mass-transfer characteristics influence sensing-molecule immobilization, formaldehyde diffusion, and interfacial reactions. Further analysis indicates that substrate properties do not exhibit a simple monotonic relationship with sensing performance. Similar substrate characteristics may produce different effects across sensing systems, and these differences are jointly influenced by sensing-molecule properties, sampling methods, and temperature and humidity conditions. In addition, sampling mode, temperature, humidity, and signal readout methods can affect sensor performance and complicate comparisons among reported systems. Overall, solid-substrate colorimetric sensors offer simplicity, cost-effectiveness, and visual detection, but challenges remain in long-term stability, environmental adaptability, and standardized performance evaluation. Future research should focus on the synergistic design of sensing molecules and substrates, optimization of sensing layers and interfaces, and standardized methods for sampling, color-signal readout, and performance evaluation to improve data comparability, detection reliability, and practical applicability.