Research Centre for
Carbon-Strategic Catalysis
碳戰略催化研究中心
Research Themes
The main research focus of RCCSC is the in-depth explorations of catalysts and their applications in different chemical reactions related to multi-scale energy conversion and supply systems under the carbon neutrality goals.
Theme 1. Catalyst Analysis:
applying theoretical calculations strategy to optimize the synthesis approaches of catalysts, and further improve the catalyst activity.
Theme 2. Catalyst Applications:
CO2 reduction (CO2RR), developing novel catalysts for water-splitting and other small molecule conversions, developing carbo-based catalysts for long-term fuel cells applicable under both acidic and alkaline condition, and developing advanced catalysts for biofuel/biodiesel generation.
Highlights
1. Sun, M., Wong, H. H., Wu, T., Dougherty, A. W., Huang, B., Entanglement of Spatial and Energy Segmentation for C1 Pathways in CO2 Reduction on Carbon Skeleton Supported Atomic Catalysts. Adv. Energy Mater. 2022, 12, 2103781.
Electroreduction of CO2 has become the most attractive approach to generate value-added chemicals and fuels. Products of single atomic catalysts (SAC) in CO2 reduction reaction reactions (CO2RR) are mostly limited to CO since the contributions of spatial and thermodynamic factors are not distinguished. To break through the challenges, comprehensive explorations in graphdiyne(GDY)-based SAC are made, to reveal detailed influences of active sites, elements, and adsorptions on the selectivity and reaction energy of the C1 pathway. Unique d electrons dominated adsorption behaviors are identified, where the d6 boundary is able to help screen out promising candidates for achieving complicated C2+ products. Based on spatial and thermodynamic factors, metal sites are still the most promising active sites. The transition metal based GDY-SACs show element-dependent electroactivity towards different products in CO2RR. Meanwhile, the GDY-Pr and GDY-Pm SACs are promising candidates for the CO2RR and even C2 products in the future. This work supplies in-depth insights into the CO2RR to facilitate the design of efficient atomic catalysts in future work.
2. Sun, M., Dougherty, A. W., Huang, B., Li, Y., Yan, C.-H., Accelerating Atomic Catalyst Discovery by Theoretical Calculations-Machine Learning Strategy. Adv. Energy Mater. 2020, 10, 1903949.
In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag-tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy-related areas.
3. M. Sun, T. Wu, Y. Xue, A. W. Dougherty, B. Huang, Y. Li, C. Yan, Mapping of atomic catalyst on graphdiyne. Nano Energy, Volume 62, 2019, Pages 754-763.
In this work, GDY-based ACs with potentially superior performance are identified by the proposed mapping strategy. By considering the electron transfer ability as the redox process, the possibility of anchoring on GDY as exceptional ACs of all the TMs (IIIB to IIB) as well as the lanthanide elements have been systematically studied. The feasible charge transfer redox models evaluated the GDY-based electrocatalysts from an innovative perspective. Beyond the present reported Fe, Ni and Pd, this strategy proposed Co and Pt as available choices for achieving high-performance and stable ACs. The introductions of advanced deep-learning algorithm and big-data technologies demonstrate a new direction of rational designs and modifications for complicated ACs with expected performance. This start-up work supplies a general strategy in dealing with complicated electrocatalysis process that not only benefits the GDY-based ACs but also other electrocatalysts.
