Keynote Speaker
Biography
Dr. Bolong Huang has received his PhD in 2012 from the University of Cambridge, and his BSc in condensed matter physics from the Department of Physics, Peking University in 2007. Following systematic training periods of Post-doc at Peking University, and in Hong Kong, he started his independent research at the Hong Kong Polytechnic University in 2015. He is now the Associate Professor at the Department of Applied Biology and Chemical Technology and Director of the Research Centre for Carbon-Strategic Catalysis. His main research fields are electronic structures of nanomaterials, energy materials, solid functional materials, and rare earth materials, as well as their applications in multi-scale energy conversion and supply systems. Dr. Huang has published 309 research papers in peer-reviewed international journals with 260 papers as the corresponding author/first author/co-first author including Nature, Science, Chem. Soc. Rev., Energy Environ. Sci., Sci. Adv. J. Am. Chem. Soc., Angew. Chem. Int. Ed., Nat. Commun., Adv. Mater., Adv. Energy Mater., Chem (Cell), etc., and has received citations over 22000 times with an h-index of 80. He has been recognized as 2022-2023 Highly Cited Researcher by Clarivate Analytics and 2022-2023 World’s Top 2% Most-Cited Scientists by Stanford University.
Advanced Atomic Catalysts Design for Energy Systems
Bolong HUANG
Abstract
Currently, atomic catalysts (ACs) as the frontier research topics have attracted tremendous attention due to their ultra-high electroactivity and broad applications in different energy systems. However, a large number of possible combinations between metals and support materials, the complexity of catalytic materials, as well as the complicated reaction mechanisms are still the main difficulties for designing novel ACs. To supply theoretical guidance for designing novel electrocatalysts, we have carried out comprehensive theoretical studies of ACs supported on graphdiyne (GDY) through density functional theory (DFT) calculations and machine learning (ML) techniques. First, we have proposed the “Redox Barrier Model” to quantify the capability of electron exchange and transfer. For the hydrogen evolution (HER) process, we have extended the conventional indicator of proton binding energy to more diverse criteria, where the screened electrocatalysts for HER are also verified ML. To design dual atomic catalysts (DACs), the formation stability and electronic modulations for all the combinations between transition metals (TMs) and lanthanide (Ln) metals are compared. Due to the electronic self-balance effects by f-d orbital coupling, the combinations of the Ln metals and TMs achieve optimized stability and electroactivity of GDY-DACs. For the applications of GDY-ACs in the CO2 reduction reaction (CO2RR), a comprehensive reaction pathway mapping of C1 and C2 products is achieved for the first time, where the integrated large-small cycle mechanism and double-dependence correlations are identified. Moreover, the first principles machine learning (FPML) approach is proposed to predict the reaction trends for different products and C-C couplings for novel C3 products. Therefore, these theoretical explorations have supplied important insights and effective approaches for the design of novel ACs, opening a new avenue to enable broad applications of ACs towards different energy systems.
References
[1] M. Sun, B. Huang*, Adv. Energy Mater. 2024, 14: 2400152.
[2] M. Sun, B. Huang*, Adv. Energy Mater. 2023, 13: 2301948.
[3] M. Sun, H. H. Wong, T. Wu, Q. Lu, L. Lu, C. H. Chan, B. Chen, A. W. Dougherty, B. Huang*, Adv. Energy Mater. 2023, 13: 2203858.
[4] M. Sun, H. H. Wong, T. Wu, A. W. Dougherty, B. Huang*, Adv. Energy Mater. 2022, 12: 2103781.
[5] M. Sun, H. H. Wong, T. Wu, A. W. Dougherty, B. Huang*, Adv. Energy Mater. 2021, 11: 2101404.
[6] M. Sun, T. Wu, A. W. Dougherty, M. Lam, B. Huang*, Y. Li, C. -H. Yan, Adv. Energy Mater. 2021, 11: 2003796.
[7] M. Sun, A. W. Dougherty, B. Huang*, Y. Li, C. -H. Yan, Adv. Energy Mater. 2020, 10: 1903949.