Generalizable Descriptors of Highly Sensitive Detection of As(III) over Transition-Metal Single Atoms: A Combined Density Function Theory and Gradient Boosting Regression Approach.

Traditional nanomodified electrodes have made great achievements in electrochemical stripping voltammetry of sensing materials for As(III) detection. Moreover, the intermediate states are complicated to probe because of the ultrashort lifetime and complex reaction conditions of the electron transfer process in electroanalysis, which seriously hinder the identification of the actual active site. Herein, the intrinsic interaction of highly sensitive analytical behavior of nanomaterials is elucidated from the perspective of electronic structure through density functional theory (DFT) and gradient boosting regression (GBR). It is revealed that the atomic radius, d-band center (ε), and the largest coordinative TM-N bond length play a crucial role in regulating the arsenic reduction reaction (ARR) performance by the established ARR process for 27 sets of transition-metal single atoms supported on N-doped graphene. Furthermore, the database composed of filtered intrinsic electronic structural properties and the calculated descriptors of the central metal atom in TM-N-Gra were also successfully extended to oxygen evolution reaction (OER) systems, which effectively verified the reliability of the whole approach. Generally, a multistep workflow is developed through GBR models combined with DFT for valid screening of sensing materials, which will effectively upgrade the traditional trial-and-error mode for electrochemical interface designing.