Angew. Chem. ：基于DigCat平台的大数据分析与精准理论建模，深度解析Sn基催化剂CO₂还原的pH效应

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锡（Sn）基催化剂已被广泛研究用于电化学CO2还原反应生成甲酸，但单原子Sn和多原子Sn的结构敏感性对其pH依赖性性能的复杂影响仍然是个谜团。


近日，日本东北大学李昊团队携手香港城市大学范俊团队、山东大学程合锋团队，结合李昊团队开发的DigCat平台（https://www.digcat.org）进行大规模的数据挖掘，通过pH依赖的动力学预测与实验验证，揭示了单原子Sn和多原子Sn催化剂之间存在相反的pH依赖行为。


此外，团队不仅将所有的计算结构和实验数据上传至DigCat平台，更重要的是，也将pH依赖的火山图模型上传至DigCat平台，方便后续用户使用和分析。



1

图1

通过对2,348种已报道催化剂的CO2还原性能数据进行大规模挖掘 (所有实验数据均已储存于作者团队开发的DigCat数据库)，团队系统分析了不同催化剂类型的主要产物法拉第效率(FE)。其中，Sn基催化剂在形成甲酸方面表现出显著的活性和pH依赖性。

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图2

在电化学条件下，准确识别Sn基催化剂的活性表面是揭示结构-性能关系的关键。团队通过表面Pourbaix相图和机器学习力场加速的分子动力学模拟，分析了多原子Sn在负电位下的表面状态及重构行为。结果表明，SnO2表面在负电位下逐渐形成富含氧空位的还原表面，最终转变为金属Sn纳米棒覆盖的表面。

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图3

研究表明，单原子Sn上正偶极矩使*OCHO在负电场中更稳定，而多原子Sn（重构的SnO2）体系表现出相反趋势。进一步通过显式溶剂模型精准评估了Sn基催化剂的零电荷电势的值，为了后续pH依赖的动力学模拟做铺垫。

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图4

团队通过pH依赖模型，结合动力学和热力学分析，揭示单原子和多原子Sn催化剂在CO2RR中生成甲酸的结构敏感性。结果表明，pH变化导致单原子Sn催化剂和多原子Sn催化剂的活性火山图分别向相反方向移动。本研究提出的pH依赖模型深入揭示了Sn基催化剂pH依赖的行为。通过合成Sn-N4-C SAC并结合详细表征，验证结果与预测高度一致。此外，r-SnO2-x在中性和碱性条件下的实验与理论数据表现出良好一致性。


最终，团队提出Sn基催化剂相关的设计策略：单原子Sn催化剂应适当增强OCHO的结合强度，多原子Sn催化剂则需适当减弱其结合能。

论文信息

Divergent Activity Shifts of Tin-Based Catalysts for Electrochemical CO2 Reduction: pH-Dependent Behavior of Single-Atom Versus Polyatomic Structures

Yuhang Wang, Di Zhang, Bin Sun, Xue Jia, Linda Zhang, Hefeng Cheng, Jun Fan, Hao Li


Angewandte Chemie International Edition

DOI: 10.1002/anie.202418228




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