1
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Kazemi A, Manteghi F, Tehrani Z. Metal Electrocatalysts for Hydrogen Production in Water Splitting. ACS OMEGA 2024; 9:7310-7335. [PMID: 38405471 PMCID: PMC10882616 DOI: 10.1021/acsomega.3c07911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 02/27/2024]
Abstract
The rising demand for fossil fuels and the resulting pollution have raised environmental concerns about energy production. Undoubtedly, hydrogen is the best candidate for producing clean and sustainable energy now and in the future. Water splitting is a promising and efficient process for hydrogen production, where catalysts play a key role in the hydrogen evolution reaction (HER). HER electrocatalysis can be well performed by Pt with a low overpotential close to zero and a Tafel slope of about 30 mV dec-1. However, the main challenge in expanding the hydrogen production process is using efficient and inexpensive catalysts. Due to electrocatalytic activity and electrochemical stability, transition metal compounds are the best options for HER electrocatalysts. This study will focus on analyzing the current situation and recent advances in the design and development of nanostructured electrocatalysts for noble and non-noble metals in HER electrocatalysis. In general, strategies including doping, crystallization control, structural engineering, carbon nanomaterials, and increasing active sites by changing morphology are helpful to improve HER performance. Finally, the challenges and future perspectives in designing functional and stable electrocatalysts for HER in efficient hydrogen production from water-splitting electrolysis will be described.
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Affiliation(s)
- Amir Kazemi
- Research
Laboratory of Inorganic Chemistry and Environment, Department of Chemistry, Iran University of Science and Technology, 16846-13114 Tehran, Iran
| | - Faranak Manteghi
- Research
Laboratory of Inorganic Chemistry and Environment, Department of Chemistry, Iran University of Science and Technology, 16846-13114 Tehran, Iran
| | - Zari Tehrani
- The
Future Manufacturing Research Institute, Faculty of Science and Engineering, Swansea University, SA1 8EN Swansea, United Kingdom
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2
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Wang Z, Chen A, Tao K, Han Y, Li J. MatGPT: A Vane of Materials Informatics from Past, Present, to Future. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306733. [PMID: 37813548 DOI: 10.1002/adma.202306733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/05/2023] [Indexed: 10/17/2023]
Abstract
Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.
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Affiliation(s)
- Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - An Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kehao Tao
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China
- Key Laboratory of Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
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3
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Kawashima K, Márquez RA, Smith LA, Vaidyula RR, Carrasco-Jaim OA, Wang Z, Son YJ, Cao CL, Mullins CB. A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts. Chem Rev 2023. [PMID: 37967475 DOI: 10.1021/acs.chemrev.3c00005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
Transition metal borides, carbides, pnictides, and chalcogenides (X-ides) have emerged as a class of materials for the oxygen evolution reaction (OER). Because of their high earth abundance, electrical conductivity, and OER performance, these electrocatalysts have the potential to enable the practical application of green energy conversion and storage. Under OER potentials, X-ide electrocatalysts demonstrate various degrees of oxidation resistance due to their differences in chemical composition, crystal structure, and morphology. Depending on their resistance to oxidation, these catalysts will fall into one of three post-OER electrocatalyst categories: fully oxidized oxide/(oxy)hydroxide material, partially oxidized core@shell structure, and unoxidized material. In the past ten years (from 2013 to 2022), over 890 peer-reviewed research papers have focused on X-ide OER electrocatalysts. Previous review papers have provided limited conclusions and have omitted the significance of "catalytically active sites/species/phases" in X-ide OER electrocatalysts. In this review, a comprehensive summary of (i) experimental parameters (e.g., substrates, electrocatalyst loading amounts, geometric overpotentials, Tafel slopes, etc.) and (ii) electrochemical stability tests and post-analyses in X-ide OER electrocatalyst publications from 2013 to 2022 is provided. Both mono and polyanion X-ides are discussed and classified with respect to their material transformation during the OER. Special analytical techniques employed to study X-ide reconstruction are also evaluated. Additionally, future challenges and questions yet to be answered are provided in each section. This review aims to provide researchers with a toolkit to approach X-ide OER electrocatalyst research and to showcase necessary avenues for future investigation.
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Affiliation(s)
- Kenta Kawashima
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Raúl A Márquez
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Lettie A Smith
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Rinish Reddy Vaidyula
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Omar A Carrasco-Jaim
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Ziqing Wang
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Yoon Jun Son
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Chi L Cao
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - C Buddie Mullins
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Center for Electrochemistry, The University of Texas at Austin, Austin, Texas 78712, United States
- H2@UT, The University of Texas at Austin, Austin, Texas 78712, United States
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4
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Mou LH, Han T, Smith PES, Sharman E, Jiang J. Machine Learning Descriptors for Data-Driven Catalysis Study. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301020. [PMID: 37191279 PMCID: PMC10401178 DOI: 10.1002/advs.202301020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/07/2023] [Indexed: 05/17/2023]
Abstract
Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.
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Affiliation(s)
- Li-Hui Mou
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - TianTian Han
- Hefei JiShu Quantum Technology Co. Ltd., Hefei, 230026, China
| | - Pieter E S Smith
- YDS Pharmatech, ETEC, 1220 Washington Ave., Albany, NY, 12203, USA
| | - Edward Sharman
- Department of Neurology, University of California, Irvine, CA, 92697, USA
| | - Jun Jiang
- Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, 230026, China
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5
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Chen S, Gao Y, Wang W, Prezhdo OV, Xu L. Prediction of Three-Metal Cluster Catalysts on Two-Dimensional W 2N 3 Support with Integrated Descriptors for Electrocatalytic Nitrogen Reduction. ACS NANO 2023; 17:1522-1532. [PMID: 36606598 DOI: 10.1021/acsnano.2c10607] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In the electrocatalytic nitrogen reduction reaction (NRR), nitrogen (N2) is chemically inert, it is difficult to break the triple bond, and the subsequent protonation step is very challenging. Suitable catalysts with high selectivity and high activity are needed to promote the electrocatalytic NRR. We screen a large number of clusters composed of three metal atoms embedded into a two-dimensional metal nitride, W2N3, with a N vacancy, and calculate the reaction energetics. The VNiCu cluster has the best catalytic activity among all the catalysts proposed so far. The Fe3 and Fe2Co clusters are excellent catalysts as well. In all cases, spin polarization is needed to observe the catalytic effect. We establish the optimal NRR path and confirm that it remains unchanged in the presence of a solvent. We find three groups of descriptors that can well predict the materials' properties and exhibit linear relationships with the NRR limiting potential.
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Affiliation(s)
- Siyu Chen
- Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
- Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
| | - Yongqi Gao
- Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
- Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
| | - Wugang Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
- Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
| | - Oleg V Prezhdo
- Department of Chemistry, University of Southern California, Los Angeles, California90089, United States
| | - Lai Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
- Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123Jiangsu, P.R. China
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6
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Zhang W, Huang W, Tan J, Guo Q, Wu B. Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects. CHEMOSPHERE 2022; 308:136447. [PMID: 36116627 DOI: 10.1016/j.chemosphere.2022.136447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/08/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Qingwei Guo
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PRC, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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7
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Gradient boosting algorithm for current-voltage prediction of fuel cells. Electrochim Acta 2022. [DOI: 10.1016/j.electacta.2022.141148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Chen YY, Ross Kunz M, He X, Fushimi R. Recent progress toward catalyst properties, performance, and prediction with data-driven methods. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Zhang X, Tian Y, Chen L, Hu X, Zhou Z. Machine Learning: A New Paradigm in Computational Electrocatalysis. J Phys Chem Lett 2022; 13:7920-7930. [PMID: 35980765 DOI: 10.1021/acs.jpclett.2c01710] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis.
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Affiliation(s)
- Xu Zhang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Yun Tian
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Letian Chen
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
| | - Xu Hu
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
| | - Zhen Zhou
- School of Chemical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
- School of Materials Science and Engineering, Institute of New Energy Material Chemistry, Renewable Energy Conversion and Storage Center (ReCast), Key Laboratory of Advanced Energy Chemistry (Ministry of Education), Nankai University, Tianjin 300350, P. R. China
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10
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Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, Pang CH, Wu T, Li S, Yin Z, Yu XF. Data-Driven Materials Innovation and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104113. [PMID: 35451528 DOI: 10.1002/adma.202104113] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 03/19/2022] [Indexed: 05/07/2023]
Abstract
Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.
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Affiliation(s)
- Zhuo Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Zhehao Sun
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Hang Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xinghui Liu
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seoburo, Jangan-Gu, Suwon, 16419, Republic of Korea
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Cheng Heng Pang
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- Municipal Key Laboratory of Clean Energy Conversion Technologies, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Tao Wu
- Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- New Materials Institute, University of Nottingham, Ningbo, China, Ningbo, 315100, P. R. China
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zongyou Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
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11
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Karthikeyan M, Mahapatra DM, Razak ASA, Abahussain AA, Ethiraj B, Singh L. Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects. CATALYSIS REVIEWS 2022:1-31. [DOI: 10.1080/01614940.2022.2103980] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/13/2022] [Indexed: 09/02/2023]
Affiliation(s)
- M Karthikeyan
- Department of Environmental Science, SRM University-AP, Amaravati, India
| | - Durga Madhab Mahapatra
- Department of Chemical Engineering, Energy Cluster, School of Engineering, University of Petroleum and Energy Studies, Energy Acres, Dehradun, India
| | - Abdul Syukor Abd Razak
- Faculty of Civil Engineering Technology, Universiti Malaysia Pahang (UMP), Kuantan, Malaysia
| | | | - Baranitharan Ethiraj
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
| | - Lakhveer Singh
- Department of Chemistry, Sardar Patel University, Mandi,India
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12
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022; 122:13478-13515. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Dehong Chen
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,Biochemistry and Chemistry, La Trobe University, Kingsbury Drive, Bundoora, Victoria 3042, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Rachel A Caruso
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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13
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Kolluru A, Shuaibi M, Palizhati A, Shoghi N, Das A, Wood B, Zitnick CL, Kitchin JR, Ulissi ZW. Open Challenges in Developing Generalizable Large-Scale Machine-Learning Models for Catalyst Discovery. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Adeesh Kolluru
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Muhammed Shuaibi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Aini Palizhati
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Nima Shoghi
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - Abhishek Das
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - Brandon Wood
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - C. Lawrence Zitnick
- Fundamental AI Research at Meta AI, Menlo Park, California 94025, United States
| | - John R. Kitchin
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Zachary W. Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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14
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Ryu B, Wang L, Pu H, Chan MKY, Chen J. Understanding, discovery, and synthesis of 2D materials enabled by machine learning. Chem Soc Rev 2022; 51:1899-1925. [PMID: 35246673 DOI: 10.1039/d1cs00503k] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.
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Affiliation(s)
- Byunghoon Ryu
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Luqing Wang
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA.,The Materials Research Center, Northwestern University, Evanston, Illinois 60208, USA
| | - Haihui Pu
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, USA.,Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA.
| | - Maria K Y Chan
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA.,Northwestern Argonne Institute of Science and Engineering, Evanston, Illinois 60208, USA
| | - Junhong Chen
- Chemical Sciences and Engineering Division, Physical Sciences and Engineering Directorate, Argonne National Laboratory, Lemont, Illinois 60439, USA.,Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA.
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15
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Guan Y, Chaffart D, Liu G, Tan Z, Zhang D, Wang Y, Li J, Ricardez-Sandoval L. Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117224] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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16
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Wang B, Zhang F. Main Descriptors To Correlate Structures with the Performances of Electrocatalysts. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202111026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Bin Wang
- State Key Laboratory of Catalysis Dalian National Laboratory for Clean Energy The Collaborative Innovation Center of Chemistry for Energy Materials (iChEM) Dalian Institute of Chemical Physics Chinese Academy of Sciences 457# Zhongshan Road Dalian 116023 Liaoning China
- Center for Advanced Materials Research School of Materials and Chemical Engineering Zhongyuan University of Technology 41# Zhongyuan Road Zhengzhou 450007 Henan China
| | - Fuxiang Zhang
- State Key Laboratory of Catalysis Dalian National Laboratory for Clean Energy The Collaborative Innovation Center of Chemistry for Energy Materials (iChEM) Dalian Institute of Chemical Physics Chinese Academy of Sciences 457# Zhongshan Road Dalian 116023 Liaoning China
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17
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Chen L, Zhang X, Chen A, Yao S, Hu X, Zhou Z. Targeted design of advanced electrocatalysts by machine learning. CHINESE JOURNAL OF CATALYSIS 2022. [DOI: 10.1016/s1872-2067(21)63852-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Cao X, Chen C, Min Y, Yuan H, Chen S, Xu L. Prediction of bimetal embedded in two-dimensional materials for CO 2 reduction electrocatalysis with a new integrated descriptor. Phys Chem Chem Phys 2021; 23:26241-26249. [PMID: 34787123 DOI: 10.1039/d1cp03805b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
CO2 reduction catalysis plays an important role in the process of converting harmful exhaust gas into useful fuels. However, the product complexity and the difficult hydrogenation in critical steps make it difficult to find a suitable catalyst for CO2 reduction. In this work, we report homo/hetero bimetal embedded in two-dimensional materials for electrocatalysis and discovered a new descriptor. We chose β12-borophene accommodating two transition metal atoms for efficient CO2RR as a model system. We found that MnCo and VV systems are promising for CO2 reduction with good stability and high selectivity over HER. Through least absolute shrinkage and selection operator (LASSO) regression, we discovered a new integrated descriptor containing the spin moment of the metals and the descriptor is linked with the performance of the first step of CO2 hydrogenation. The MnCo system could catalyze a C1 process with low free energy change of the rate determining step. The VV system could also conduct the C2 process with low free energy change of the rate determining step. Bader charge analysis shows the ability of the borophene substrate to provide or hold electrons. This work demonstrates homonuclear and heteronuclear biatomic catalysts with high activity for CO2RR.
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Affiliation(s)
- Xin Cao
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Chongyang Chen
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Yuxiang Min
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Hao Yuan
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Shiqian Chen
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
| | - Lai Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
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19
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Majumder M, Saini H, Dědek I, Schneemann A, Chodankar NR, Ramarao V, Santosh MS, Nanjundan AK, Kment Š, Dubal D, Otyepka M, Zbořil R, Jayaramulu K. Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia. ACS NANO 2021; 15:17275-17298. [PMID: 34751563 DOI: 10.1021/acsnano.1c08455] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The conversion of nitrogen to ammonia offers a sustainable and environmentally friendly approach for producing precursors for fertilizers and efficient energy carriers. Owing to the large energy density and significant gravimetric hydrogen content, NH3 is considered an apt next-generation energy carrier and liquid fuel. However, the low conversion efficiency and slow production of ammonia through the nitrogen reduction reaction (NRR) are currently bottlenecks, making it an unviable alternative to the traditional Haber-Bosch process for ammonia production. The rational design and engineering of catalysts (both photo- and electro-) represent a crucial challenge for improving the efficiency and exploiting the full capability of the NRR. In the present review, we highlight recent progress in the development of graphene-based systems and graphene derivatives as catalysts for the NRR. Initially, the history, fundamental mechanism, and importance of the NRR to produce ammonia are briefly discussed. We also outline how surface functionalization, defects, and hybrid structures (single-atom/multiatom as well as composites) affect the N2 conversion efficiency. The potential of graphene and graphene derivatives as NRR catalysts is highlighted using pertinent examples from theoretical simulations as well as machine learning based performance predictive methods. The review is concluded by identifying the crucial advantages, drawbacks, and challenges associated with principal scientific and technological breakthroughs in ambient catalytic NRR.
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Affiliation(s)
- Mandira Majumder
- Department of Chemistry, Indian Institute of Technology Jammu, Jammu, Jammu & Kashmir 181221, India
| | - Haneesh Saini
- Department of Chemistry, Indian Institute of Technology Jammu, Jammu, Jammu & Kashmir 181221, India
| | - Ivan Dědek
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Andreas Schneemann
- Lehrstuhl für Anorganische Chemie I, Technische Universität Dresden, Bergstr. 66, 01069 Dresden, Germany
| | - Nilesh R Chodankar
- Department of Energy & Materials Engineering, Dongguk University, Seoul 100-715, South Korea
| | - Viswanatha Ramarao
- Centre for Incubation, Innovation, Research and Consultancy (CIIRC) and Department of Chemistry, Jyothy Institute of Technology, Thataguni, Off Kanakpura Road, Bangalore, Karnataka 560082, India
| | - Mysore Sridhar Santosh
- Centre for Incubation, Innovation, Research and Consultancy (CIIRC) and Department of Chemistry, Jyothy Institute of Technology, Thataguni, Off Kanakpura Road, Bangalore, Karnataka 560082, India
- CSIR-Central Institute of Mining & Fuel Research, Digwadih Campus, PO FRI, Dhanbad, Jharkhand 828 108, India
| | - Ashok Kumar Nanjundan
- Centre for Materials Science, School of Chemistry and Physics, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD 4001, Australia
| | - Štěpán Kment
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
- Nanotechnology Centre, Centre of Energy and Environmental Technologies, VŠB - Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Deepak Dubal
- Centre for Materials Science, School of Chemistry and Physics, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD 4001, Australia
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
- IT4Innovations, VŠB - Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Radek Zbořil
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
- Nanotechnology Centre, Centre of Energy and Environmental Technologies, VŠB - Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Kolleboyina Jayaramulu
- Department of Chemistry, Indian Institute of Technology Jammu, Jammu, Jammu & Kashmir 181221, India
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
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20
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Rangel-Martinez D, Nigam K, Ricardez-Sandoval LA. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.08.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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21
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Wang B, Zhang F. Main Descriptors To Correlate Structures with the Performances of Electrocatalysts. Angew Chem Int Ed Engl 2021; 61:e202111026. [PMID: 34587345 DOI: 10.1002/anie.202111026] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/27/2021] [Indexed: 01/05/2023]
Abstract
Traditional trial and error approaches to search for hydrogen/oxygen redox catalysts with high activity and stability are typically tedious and inefficient. There is an urgent need to identify the most important parameters that determine the catalytic performance and so enable the development of design strategies for catalysts. In the past decades, several descriptors have been developed to unravel structure-performance relationships. This Minireview summarizes reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discusses their merits/limitations. Understanding the trends in electrocatalytic performance and predicting promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning have also been adopted to discover new and advanced descriptors. Finally, linear scaling relationships are analyzed and several strategies proposed to circumvent the established scaling relationships and overcome the constraints imposed on the catalytic performance.
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Affiliation(s)
- Bin Wang
- State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, The Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457# Zhongshan Road, Dalian 116023, Liaoning, China.,Center for Advanced Materials Research, School of Materials and Chemical Engineering, Zhongyuan University of Technology, 41# Zhongyuan Road, Zhengzhou, 450007, Henan, China
| | - Fuxiang Zhang
- State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, The Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457# Zhongshan Road, Dalian 116023, Liaoning, China
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22
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Wang M, Zhu H. Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts. ACS Catal 2021. [DOI: 10.1021/acscatal.1c00178] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Min Wang
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Hongwei Zhu
- State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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23
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Routh PK, Liu Y, Marcella N, Kozinsky B, Frenkel AI. Latent Representation Learning for Structural Characterization of Catalysts. J Phys Chem Lett 2021; 12:2086-2094. [PMID: 33620230 DOI: 10.1021/acs.jpclett.0c03792] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum-structure relationship that can be eventually mapped to physicochemical properties. The latent space of the autoencoder also provides a pathway to interpret the information content "hidden" in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes.
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Affiliation(s)
- Prahlad K Routh
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Yang Liu
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Nicholas Marcella
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
| | - Boris Kozinsky
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Bosch Research, Cambridge, Massachusetts 02139, United States
| | - Anatoly I Frenkel
- Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States
- Division of Chemistry, Brookhaven National Laboratory, Upton, New York 11973, United States
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24
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Yuan H, Min Y, Xu L. Prediction of Dual-Doped Integrated CsPbBr 3-CsPbCl 3 Perovskite Heterostructure for Practical Photocatalytic Water Splitting with a New Descriptor. J Phys Chem Lett 2021; 12:822-828. [PMID: 33417457 DOI: 10.1021/acs.jpclett.0c03745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Photocatalytic water splitting has always been a field where breakthroughs are expected to solve energy and environmental problems. However, current catalysts suffer from low activity in mismatched catalytic environments and high cost. Herein, we designed a series of integrated CsPbBr3-CsPbCl3 heterostructures to explore their catalytic capability. Based on extensive calculations, we discovered the inner connection between dopant atoms and the catalytic performance and proposed a new descriptor by applying the Least Absolute Shrinkage and Selection Operator (LASSO) analysis. After systematic screening, the CsPbBr3:Ni-CsPbCl3:Co system is found to be promising for single-catalyst overall water splitting under the same environment. Furthermore, a smaller bandgap that covers the redox potential of water splitting suggests the capability for photocatalysis. Besides, the CsPbBr3:Ni-CsPbCl3:Co system bulk-doped by Co could conduct the photocatalysis with better performance.
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Affiliation(s)
- Hao Yuan
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Yuxiang Min
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Lai Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
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25
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26
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Qian J, Baskin A, Liu Z, Prendergast D, Crumlin EJ. Addressing the sensitivity of signals from solid/liquid ambient pressure XPS (APXPS) measurement. J Chem Phys 2020; 153:044709. [DOI: 10.1063/5.0006242] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Jin Qian
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Artem Baskin
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Zhi Liu
- State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - David Prendergast
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Ethan J. Crumlin
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
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27
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Zhang X, Teng SY, Loy ACM, How BS, Leong WD, Tao X. Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E1012. [PMID: 32466377 PMCID: PMC7353444 DOI: 10.3390/nano10061012] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 05/18/2020] [Accepted: 05/19/2020] [Indexed: 01/29/2023]
Abstract
The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry.
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Affiliation(s)
- Xixia Zhang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China;
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Sin Yong Teng
- Institute of Process Engineering & NETME Centre, Brno University of Technology, Technicka 2896/2, 616 69 Brno, Czech Republic;
| | - Adrian Chun Minh Loy
- Department of Chemical Engineering, Monash University, Clayton, Melbourne 3800, Australia;
| | - Bing Shen How
- Research Centre for Sustainable Technologies, Faculty of Engineering, Computing and Science, Swinburne University of Technology, Jalan Simpang Tiga, Kuching 93350, Malaysia;
| | - Wei Dong Leong
- Department of Chemical and Environmental Engineering, University of Nottingham, Semenyih 43500, Malaysia;
| | - Xutang Tao
- State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China;
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28
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Taylor NT, Price CJ, Petkov A, Romanis Carr MI, Hale JC, Hepplestone SP. The Potential of Overlayers on Tin-based Perovskites for Water Splitting. J Phys Chem Lett 2020; 11:4124-4130. [PMID: 32354214 PMCID: PMC7304906 DOI: 10.1021/acs.jpclett.0c00964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Photoelectrochemical water splitting is a promising method of clean hydrogen production for green energy uses. Here, we report on a tin-based oxide perovskite combined with an overlayer that shows enhanced bifunctional hydrogen and oxygen evolution. In our first-principles study of tin-based perovskites, based upon density functional theory, we investigate how the formation of a surface affects the electronic properties of these materials. We show that the best candidate, SrSnO3, possesses hydrogen and oxygen overpotentials of 0.75 and 0.72 eV, respectively, which are reduced to 0.35 and 0.54 eV with the inclusion of a ZrO2 overlayer. Furthermore, this overlayer promotes charge extraction, stabilizes the reaction pathways, and improves the band gap such that it straddles the overpotentials between pH 0 and pH 12. This result indicates that SrSnO3 with a ZrO2 overlayer has significant potential as a highly efficient bifunctional water splitter for producing hydrogen and oxygen gas on the same surface.
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29
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Yang TT, Saidi WA. Graphene Activation Explains the Enhanced Hydrogen Evolution on Graphene-Coated Molybdenum Carbide Electrocatalysts. J Phys Chem Lett 2020; 11:2759-2764. [PMID: 32188252 DOI: 10.1021/acs.jpclett.0c00615] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Molybdenum carbides (MoxC) have shown high catalytic activities toward hydrogen evolution reaction (HER) when coupled with graphene. Herein, we use density functional theory (DFT) calculations in conjunction with ab initio thermodynamics and electrochemical modeling on γ-MoC supported graphene to determine the origin of the enhanced HER activities. In addition to previous claims that graphene's main role is to prevent agglomeration of MoxC nanoparticles, we show that the interplay between γ-MoC coupling and graphene defect chemistry activates graphene for the HER. For γ-MoC supported graphene systems, the HER mechanism follows the Volmer-Heyrovsky pathway with the Heyrovsky reaction as the rate-determining step. To simulate the electrochemical linear sweep voltammetry at the device level, we develop a computational current model purely from the thermodynamic and kinetics descriptors obtained using DFT. This model shows that γ-MoC supported graphene with divacancies is optimum for HER with an exchange current density of ∼1 × 10-4 A/cm2 and Tafel slope of ∼50 mV/dec-1, which are in good agreement with experimental results.
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Affiliation(s)
- Timothy T Yang
- Department of Materials Science and Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Wissam A Saidi
- Department of Materials Science and Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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