Generalized Evaluation of the Effect of SO<sub>2</sub> Adsorption-Modulated <i>d</i>-π* Back-Donation Activation on Transition Metals
SO2 adsorption significantly affects the d-electron structure of transition metal catalysts, thereby altering their d-π* activation capability for molecules with abundant antibonding orbitals (e.g., NO, CO, etc.). However, a universal theoretical framework for systematically evaluating the effect of SO2 adsorption on the activation ability of transition metal surfaces is still lacking. This study proposes a method to comprehensively evaluate the response characteristics of different transition metals to SO2 adsorption, based on the modulation effects of SO2 adsorption on the work function (WF) and d-band center (ED–EF). The ΔWF and Δ(ED–EF) values, normalized by surface volume, can be used to establish a systematic criterion for assessing the impact of SO2 adsorption on the d-π* activation capability of molecules on transition metal surfaces. Preliminary validation of this method was conducted through crystal orbital Hamiltonian population (COHP) calculations for C≡O bonds and adsorption property analysis of CO molecules. This research provides a clear theoretical basis for designing novel sulfur-tolerant catalysts and offers guidance for optimizing practical catalytic systems, such as selective catalytic reduction (SCR) for NOx removal, three-way catalytic (TWC) reactions, CO oxidation, and many other processes.
Authors
- Jingyu Xue
- Jinfei Chen
- Xuesen Du (ORCID: https://orcid.org/0000-0002-3647-1002)
- Penghui Ren (ORCID: https://orcid.org/0000-0001-5817-3199)
- Wenyun Qiao (ORCID: https://orcid.org/0009-0002-3969-9907)
- Linhan Yu
- Heyuan Zhang
- Xueyan Zheng
- Shuai Xiao
- Xinbao Li (ORCID: https://orcid.org/0000-0003-0323-477X)
- Liya Li (ORCID: https://orcid.org/0000-0003-3168-2836)
- Tian Tang (ORCID: https://orcid.org/0009-0007-7483-5376)
Institutions
- Ministry of Education (NZ)
- Chongqing University (CN)
- Beijing Forestry University (CN)
- Dongfeng Motor Group (China) (CN)
- China Jiliang University (CN)
Publication Details
- Journal
- The Journal of Physical Chemistry C
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1021/acs.jpcc.4c08531
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China