Following the construction of a dataset of cross-category corrosion inhibitors at different concentrations based on 1241 data from 184 research papers, a performance prediction model incorporating 2D–3D molecular graph representation and corrosion inhibitor concentration information was established. This model was shown to effectively predict the inhibition efficiency (IE) of different categories of corrosion inhibitors for carbon steel in 1 mol/L HCl solution. The model was also able to predict IEs of corrosion inhibitors at different concentrations. The results demonstrated that 3D features of corrosion inhibitors, especially those of large molecules, had a significant impact on the prediction precision of IEs.

Constructing near-infrared light (NIR) light-enhanced room temperature gas sensors is becoming more promising for practical application. In this study, learning from the structure and photosynthetic process of chlorophyll thylakoid membranes in plants, the first “Thylakoid membrane” structural formaldehyde (HCHO) sensor is constructed by matching the upconversion emission of the lanthanide-doped upconversion nanoparticles (UCNPs) and the UV–vis adsorption of the as-prepared nanocomposites. The NIR-mediated sensor exhibits excellent performances, including ultra-high response (R_a / R_g = 2.22, 1 ppm), low practical limit of detection (50 ppb), reliable repeatability, high selectivity, and broadband spectral response. The practicality of the NIR-mediated gas sensor is confirmed through the remote and external stimulation test. A study of sensing mechanism demonstrates that it is the UCNPs-based light transducer produces more light-induced oxygen species for gas response in the process of non-radiative/radiative energy transfer, playing a key role in significantly improving the sensing properties of the sensor. The universality of NIR-mediated gas sensors based on UCNPs is verified using ZnO, In₂O_3, and SnO₂ systems. This work paves a way for fabricating high-performance NIR-mediated gas sensors and will expand the application fields of NIR light.

Current experimental verification, computational modeling, and machine learning methods for predicting corrosion inhibition efficiency (IE) are limited to specific inhibitor categories with high cost and poor generalization. In this study, a cross-category corrosion inhibitor dataset is constructed and a three-level direct message passing neural network (3 L–DMPNN) model using molecular structure information that integrates atomic-level, chemical bond-level, and molecular-level features to predict the IEs of compounds in a specific environment is established. This work demonstrates that the 3 L–DMPNN model can predict IEs of cross-category corrosion inhibitors from other independent literature and experimental dataset effectively and quickly.