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Unlocking CO2 Reduction Potentials: The Synergy of Double-Atom Catalysts and Machine Learning

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CO2 is the primary greenhouse gas responsible for global warming and climate change. By reducing CO2 emissions, we can help stabilize global temperatures and mitigate the adverse effects of climate change, such as extreme weather events, rising sea levels, and loss of biodiversity. The electrocatalytic reduction of CO2 involves converting the carbon dioxide into useful chemicals and fuels using electrochemical processes. This technology is considered a promising strategy for carbon recycling and sustainable energy production. The process utilizes catalysts to facilitate the reaction at an electrode surface, where CO2 is reduced to products like carbon monoxide (CO), methane (CH4), ethylene (C2H4), and even alcohols, using green electrical energy. The efficiency of the CO2 reduction process heavily relies on the catalysts used. Various materials, including metals, metal oxides, and organic compounds, have been explored as potential catalysts. There are several technical challenges to overcome, including the development of more active, selective, and stable catalysts, the need for high energy efficiency, and the integration of such technology into existing industrial processes.

In a new study published in ACS Catalysis by Professor Zhongfang Chen from University of Puerto Rico together with Linke Yu and Professor Fengyu Li from Inner Mongolia University and Dr. Jingsong Huang and Dr. Bobby Sumpter from Oak Ridge National Laboratory and Dr. William Mustain from University of South Carolina, the researchers conducted comprehensive studies to understand the efficacy of double-atom catalysts (DACs) featuring an inverse sandwich structure, particularly for the electrocatalytic reduction of CO2 into valuable C1 products. Their approach combined the precision of first-principles density functional theory (DFT) calculations with the predictive power of machine learning (ML) to investigate into the catalytic potential of these novel structures.

The team utilized spin-polarized DFT to investigate the structural, electronic, and catalytic properties of DACs anchored on defective graphene. They focused on both homonuclear (same metal atoms) and heteronuclear (different metal atoms) DACs, exploring a wide range of metal combinations to determine their stability and catalytic activity. They integrated ML models with DFT findings and identified key atomic and structural features that contribute to catalytic activity. This novel approach allowed them to predict the performance of a broader set of DACs beyond those directly studied through DFT. Moreover, they conducted stability assessments, including thermodynamic and electrochemical stability evaluations using parameters such as binding energy, formation energy, and dissolution potential. Furthermore, the team analyzed the reaction pathways and intermediates involved in CO2 reduction over these DACs, focusing on the energetics of various steps to identify potential-determining steps and the ultimate products of the reaction.

The researchers found that among the homonuclear DACs, Rh2⊥gra (Rhodium dimers perpendicular to graphene) demonstrated exceptional catalytic activity, outperforming its homonuclear counterparts and Rh-based DACs with non-inverse sandwich structures. Additionally, 14 heteronuclear DACs showed significant stability and catalytic prowess. Notably, RhIr⊥gra and RhPt⊥gra emerged as standout candidates, featuring remarkably low limiting potentials indicative of high catalytic efficiency. The inverse sandwich configuration, where metal dimers are perpendicularly bonded to the graphene plane, was found to be crucial. This structure facilitated moderate CO2 adsorption strength, aligning with the optimal catalyst behavior as per Sabatier’s principle.

The authors’ detailed analysis of reaction mechanisms revealed that the DACs predominantly favored the reduction of CO2 to CH3OH and CH4, with the competition between CO2RR and HER being effectively managed through selective CO2 adsorption. The study’s findings shed light on the electron transfer mechanisms, highlighting the role of graphene as an electron reservoir and the importance of the metal dimers in mediating electron transfer to the adsorbed CO2. According to the authors, the integration of ML not only provided insights into the relationship between the DACs’ atomic properties and their catalytic activity but also enabled the prediction of the performance of an extensive array of DACs, identifying 154 potential candidates with promising catalytic activities.

In conclusion, the new study by Professor Zhongfang Chen and colleagues showcased a new class of DACs with high catalytic activity for CO2 reduction, supported by a novel design strategy that leverages the unique inverse sandwich structure. The combination of DFT and ML provided a comprehensive understanding and predictive capability, paving the way for the development of efficient catalysts for CO2 utilization and sustainable energy applications.


Unlocking CO2 Reduction Potentials: The Synergy of Double-Atom Catalysts and Machine Learning - Advances in Engineering


Professor-Fengyu-Li.jpg

About the author​


Professor Fengyu Li

School of Physical Science and Technology
Inner Mongolia University

Dr. Fengyu Li received her PhD from Dalian University of Technology and University of Puerto Rico in 2012 and 2014, respectively. She then pursued a two-year postdoctoral fellowship under the guidance of Professor Zhongfang Chen at the University of Puerto Rico. In 2017, Dr. Li was selected as one of the inaugural talents of the ‘Junma Plan’ and joined the School of Physical Science and Technology at Inner Mongolia University, and in 2022, she was promoted to a full professor. She serves as a Distinguished Editor for Advanced Powder Materials and a Young Editor for Journal of Materials Informatics. Her research is dedicated to the theoretical design and property prediction of low-dimensional nanomaterials, with an emphasis on their potential applications in electronics, as well as energy storage and conversion technologies.”​


Dr.-Jingsong-Huang.jpg

About the author​


Dr. Jingsong Huang

Senior R&D staff scientist
The Center for Nanophase Materials Sciences
Oak Ridge National Laboratory

Dr. Jingsong Huang initially specialized in experimental organic and physical chemistry, complemented by experience in the chemical industry. In 1999, he pivoted to theoretical and computational studies, addressing physical and chemical issues related to functional materials. His research expertise primarily encompasses modeling, simulation, and theoretical analysis of structure-property relationships, weak intermolecular covalent bonding interactions, and the conversion and storage of electrical energy. This involves developing theoretical models and applying various levels of theoretical frameworks. With a background of an experimental chemist with a deep understanding of theoretical and computational chemistry, Dr. Huang aims to integrate experimental observations with theoretical insights, thereby guiding materials discovery and optimization. Dr. Huang has published around 100 papers in peer-reviewed journals, including Science, Science Advances, Nature Materials, Nature Catalysis, Nature Communications, and the Journal of the American Chemical Society, as well as Angewandte Chemie International Edition.​


Professor-Zhongfang-Chen.jpg

About the author​


Professor Zhongfang Chen

Department of Chemistry
University of Puerto Rico, Rio Piedras Campus
San Juan, PR 00931, USA.

Professor Zhongfang Chen earned his PhD from Nankai University (China) in 2000. Before joining University of Puerto Rico as an associate professor in 2008, he worked in University of Erlangen-Nuremberg and Max-Plank Institute for Coal Research in Germany, University of Georgia, and had a short stint in Rensselaer Polytechnic Institute.

His laboratory focuses on computational studies of nanomaterials, aiming to design new materials for energy, environmental, and health applications. Dr. Chen’s group collaborates closely with experimental researchers, successfully turning theoretical predictions into real-world materials. Recent work concentrates on two-dimensional materials for molecular electronics, nano-devices, energy production and storage, nanocatalysts for fuel cells, metal-air batteries, and water pollutant removal. His team emphasizes high-throughput computations, machine learning, and big data to innovate in these fields. Dr. Chen has published over 330 papers. He is a Fellow of Royal Society of Chemistry, and the associate editor of Green Energy & Environment, Materials Informatics, and Journal of Hazardous Matters Letters.​

Reference


Linke Yu, Professor Fengyu Li*, Dr. Jingsong Huang*, Dr. Bobby Sumpter, William Mustain, and Professor Zhongfang Chen*. Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO2 Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation. ACS Catal. 2023, 13, 14, 9616–9628

Go to ACS Catal.
The post Unlocking CO2 Reduction Potentials: The Synergy of Double-Atom Catalysts and Machine Learning appeared first on Advances in Engineering.
 
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