今日获知，李吉群同学的论文Design of Dual-Atom Catalysts for CO2 Reduction to C2 Products: From Synergistic Mechanisms to Machine Learning Guidance被Chemical Engineering Journal接收。论文得到国家自然科学基金、陕西省杰出青年科学基金、西安交通大学基本科研业务费资助。论文摘要如下：

Single-atom catalysts (SACs) have garnered significant attention due to their maximized atomic utilization and intrinsic catalytic activity. However, their application—particularly in multi-electron pathways—is often constrained by low metal loading and the geometric isolation of active sites. In contrast, dual-atom catalysts (DACs) offer a compelling solution by enhancing site density and fostering neighboring synergistic effects. These proximal sites provide a versatile platform for breaking scaling relations, rendering DACs an ideal strategy for facilitating C-C coupling. Here, we comprehensively review the design and application of DACs for electrochemical CO2 reduction (CO2RR) to C2 products. We first elucidate the reaction mechanisms, contrasting the “single-site dilemma” of SACs with the synergistic advantages of DACs. Furthermore, we examine the critical role of machine learning (ML) in accelerating catalyst discovery, highlighting its capability to efficiently navigate the vast compositional space for rational design. Finally, we summarize existing challenges and outline future directions for bridging the gap between laboratory-scale research and industrial applications.