Density Functional Theory-Based Method to Predict the Activities of Nanomaterials as Peroxidase Mimics

作者：Xiaomei Shen, Zhenzhen Wang, Xingfa Gao, Yuliang Zhao · 发表于：ACS Catalysis · 年份：2020 · DOI：10.1021/acscatal.0c03426 · 被引用次数：206 · 研究领域：Advanced Nanomaterials in Catalysis、Electrochemical sensors and biosensors、Advanced biosensing and bioanalysis techniques

A wide variety of nanomaterials possess peroxidase-like catalytic activities and show promise as cost-effective and versatile replacements for natural peroxidases. However, a universal tool for predicting the activities of these materials is still lacking, thus hindering the efficient discovery of nanomaterials as peroxidase mimics. Here, we use density functional theory calculations to reveal the peroxidase-mimetic mechanisms for a series of iron-oxide nanosurfaces, and we derive a volcano-shaped plot of catalytic activity as a function of simple energy-based descriptors. The activity curves and the descriptors can be used to predict peroxidase-like activities for not only iron oxides but also other nanomaterials that share similar catalytic mechanisms. The results demonstrate that the method developed herein can systematically predict the peroxidase-like activities of nanomaterials and thus is expected to be of use for computer-aided design of nanomaterial-based peroxidase mimics.