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Wang C, Wang B, Wang C, Chang Z, Yang M, Wang R. Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys. ACS APPLIED MATERIALS & INTERFACES 2024; 16:16050-16061. [PMID: 38512022 DOI: 10.1021/acsami.3c17377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.
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Affiliation(s)
- Chao Wang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Bing Wang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Changhao Wang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Zhipeng Chang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Mengqi Yang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Ruzhi Wang
- Key Laboratory of Advanced Functional Materials of Education Ministry of China, Institute of New Energy Materials and Devices, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, China
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Liu L, Corma A. Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles. Chem Rev 2023; 123:4855-4933. [PMID: 36971499 PMCID: PMC10141355 DOI: 10.1021/acs.chemrev.2c00733] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Heterogeneous bimetallic catalysts have broad applications in industrial processes, but achieving a fundamental understanding on the nature of the active sites in bimetallic catalysts at the atomic and molecular level is very challenging due to the structural complexity of the bimetallic catalysts. Comparing the structural features and the catalytic performances of different bimetallic entities will favor the formation of a unified understanding of the structure-reactivity relationships in heterogeneous bimetallic catalysts and thereby facilitate the upgrading of the current bimetallic catalysts. In this review, we will discuss the geometric and electronic structures of three representative types of bimetallic catalysts (bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles) and then summarize the synthesis methodologies and characterization techniques for different bimetallic entities, with emphasis on the recent progress made in the past decade. The catalytic applications of supported bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles for a series of important reactions are discussed. Finally, we will discuss the future research directions of catalysis based on supported bimetallic catalysts and, more generally, the prospective developments of heterogeneous catalysis in both fundamental research and practical applications.
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Wang L, Ore RM, Jayamaha PK, Wu ZP, Zhong CJ. Density functional theory based computational investigations on the stability of highly active trimetallic PtPdCu nanoalloys for electrochemical oxygen reduction. Faraday Discuss 2023; 242:429-442. [PMID: 36173024 DOI: 10.1039/d2fd00101b] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Activity, cost, and durability are the trinity of catalysis research for the electrochemical oxygen reduction reaction (ORR). While studies towards increasing activity and reducing cost of ORR catalysts have been carried out extensively, much effort is needed in durability investigation of highly active ORR catalysts. In this work, we examined the stability of a trimetallic PtPdCu catalyst that has demonstrated high activity and incredible durability during ORR using density functional theory (DFT) based computations. Specifically, we studied the processes of dissolution/deposition and diffusion between the surface and inner layer of Cu species of Pt20Pd20Cu60 catalysts at electrode potentials up to 1.2 V to understand their role towards stabilizing Pt20Pd20Cu60 catalysts. The results show there is a dynamic Cu surface composition range that is dictated by the interplay of the four processes, dissolution, deposition, diffusion from the surface to inner layer, and diffusion from the inner layer to the surface of Cu species, in the stability and observed oscillation of lattice constants of Cu-rich PtPdCu nanoalloys.
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Affiliation(s)
- Lichang Wang
- School of Chemical and Biomolecular Sciences and the Materials Technology Center, Southern Illinois University, Carbondale, IL 62901, USA.
| | - Rotimi M Ore
- School of Chemical and Biomolecular Sciences and the Materials Technology Center, Southern Illinois University, Carbondale, IL 62901, USA.
| | - Peshala K Jayamaha
- School of Chemical and Biomolecular Sciences and the Materials Technology Center, Southern Illinois University, Carbondale, IL 62901, USA.
| | - Zhi-Peng Wu
- Department of Chemistry, State University of New York at Binghamton, Binghamton, NY 13902, USA
| | - Chuan-Jian Zhong
- Department of Chemistry, State University of New York at Binghamton, Binghamton, NY 13902, USA
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Loevlie D, Ferreira B, Mpourmpakis G. Demystifying the Chemical Ordering of Multimetallic Nanoparticles. Acc Chem Res 2023; 56:248-257. [PMID: 36680516 PMCID: PMC9910050 DOI: 10.1021/acs.accounts.2c00646] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
ConspectusMultimetallic nanoparticles (NPs) have highly tunable properties due to the synergy between the different metals and the wide variety of NP structural parameters such as size, shape, composition, and chemical ordering. The major problem with studying multimetallic NPs is that as the number of different metals increases, the number of possible chemical orderings (placements of different metals) for a NP of fixed size explodes. Thus, it becomes infeasible to explore NP energetic differences with highly accurate computational methods, such as density functional theory (DFT), which has a high computational cost and is typically applied to up to a couple of hundred metal atoms. Here, we demonstrate a methodology advancing NP simulations by effectively exploring the vast materials space of multimetallic NPs and accurately identifying the ones with the most thermodynamically preferred chemical orderings. With accuracies reaching that of DFT, our methodology is applicable to practically any NP size, shape, and metal composition. We achieve this by significantly advancing the bond-centric (BC) model, a physics-based model that has been previously shown to rapidly predict bimetallic NP cohesive energies (CEs). Specifically, the BC model is trained in a way to understand how the bimetallic bond strength changes under different coordination environments present on a NP and how the metal composition of every site affects the detailed coordination environment using fractional coordination numbers. This newly modified BC model leads to an improvement from 0.331 (original model) to 0.089 eV/atom in CE predictions when compared to DFT values on a robust data set of 90 different NPs consisting of PtPd, AuPt, and AuPd NPs with varying compositions and chemical orderings. By incorporating the modified BC model into an in-house-developed genetic algorithm (GA) we can effectively and accurately predict the most stable chemical orderings of large, realistic bimetallic NPs consisting of thousands of metal atoms. This is demonstrated on AuPd bimetallic NPs, a challenging system due to the similarity in the cohesion of the two metals. By training our BC model using a unique DFT calculation on a bimetallic NP (one calculation for two metals combining together), we expand to explore the chemical ordering of multimetallic NPs. We first demonstrate the application of our methodology on a AuPdPt NP and validate our stability predictions with literature data. Then, we effectively explore the vast materials space of multimetallic NPs consisting of combinations of Au, Pt, and Pd as a function of metal composition. Our thermodynamic stability trends are presented in a ternary diagram revealing detailed, and yet, unexpected chemical ordering trends. Our computational framework can aid both experimental and computational researchers toward effectively screening multimetallic NP stability. Moreover, we provide an outlook of how this framework can be applied to catalyst discovery, high-entropy alloys, and single-atom alloys.
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Niu G, Gao F, Wang Y, Zhang J, Zhao L, Jiang Y. Bimetallic Nanomaterials: A Promising Nanoplatform for Multimodal Cancer Therapy. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248712. [PMID: 36557846 PMCID: PMC9783205 DOI: 10.3390/molecules27248712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/25/2022] [Accepted: 12/03/2022] [Indexed: 12/13/2022]
Abstract
Bimetallic nanomaterials (BMNs) composed of two different metal elements have certain mixing patterns and geometric structures, and they often have superior properties than monometallic nanomaterials. Bimetallic-based nanomaterials have been widely investigated and extensively used in many biomedical fields especially cancer therapy because of their unique morphology and structure, special physicochemical properties, excellent biocompatibility, and synergistic effect. However, most reviews focused on the application of BMNs in cancer diagnoses (sensing, and imaging) and rarely mentioned the application of the treatment of cancer. The purpose of this review is to provide a comprehensive perspective on the recent progress of BNMs as therapeutic agents. We first introduce and discuss the synthesis methods, intrinsic properties (size, morphology, and structure), and optical and catalytic properties relevant to cancer therapy. Then, we highlight the application of BMNs in cancer therapy (e.g., drug/gene delivery, radiotherapy, photothermal therapy, photodynamic therapy, enzyme-mediated tumor therapy, and multifunctional synergistic therapy). Finally, we put forward insights for the forthcoming in order to make more comprehensive use of BMNs and improve the medical system of cancer treatment.
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Affiliation(s)
| | | | | | - Jie Zhang
- Correspondence: (J.Z.); (L.Z.); (Y.J.); Tel.: +86-17865551290 (Y.J.)
| | - Li Zhao
- Correspondence: (J.Z.); (L.Z.); (Y.J.); Tel.: +86-17865551290 (Y.J.)
| | - Yanyan Jiang
- Correspondence: (J.Z.); (L.Z.); (Y.J.); Tel.: +86-17865551290 (Y.J.)
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Prabhu AM, Choksi TS. Data-driven methods to predict the stability metrics of catalytic nanoparticles. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Millstone JE, Eikey EA, Fagan AM, Gan XY, Lord RW, Sen R, Steimle BC, Schaak RE. Virtual Issue on Nanosynthetic Chemistry. ACS NANO 2021; 15:13893-13896. [PMID: 34583467 DOI: 10.1021/acsnano.1c08046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
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Moriau LJ, Hrnjić A, Pavlišič A, Kamšek AR, Petek U, Ruiz-Zepeda F, Šala M, Pavko L, Šelih VS, Bele M, Jovanovič P, Gatalo M, Hodnik N. Resolving the nanoparticles' structure-property relationships at the atomic level: a study of Pt-based electrocatalysts. iScience 2021; 24:102102. [PMID: 33659872 PMCID: PMC7890412 DOI: 10.1016/j.isci.2021.102102] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
Achieving highly active and stable oxygen reduction reaction performance at low platinum-group-metal loadings remains one of the grand challenges in the proton-exchange membrane fuel cells community. Currently, state-of-the-art electrocatalysts are high-surface-area-carbon-supported nanoalloys of platinum with different transition metals (Cu, Ni, Fe, and Co). Despite years of focused research, the established structure-property relationships are not able to explain and predict the electrochemical performance and behavior of the real nanoparticulate systems. In the first part of this work, we reveal the complexity of commercially available platinum-based electrocatalysts and their electrochemical behavior. In the second part, we introduce a bottom-up approach where atomically resolved properties, structural changes, and strain analysis are recorded as well as analyzed on an individual nanoparticle before and after electrochemical conditions (e.g. high current density). Our methodology offers a new level of understanding of structure-stability relationships of practically viable nanoparticulate systems.
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Affiliation(s)
- Leonard Jean Moriau
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Armin Hrnjić
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Andraž Pavlišič
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Ana Rebeka Kamšek
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Urša Petek
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Francisco Ruiz-Zepeda
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Martin Šala
- Department of Analytical Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Luka Pavko
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Vid Simon Šelih
- Department of Analytical Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Marjan Bele
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Primož Jovanovič
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Matija Gatalo
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Nejc Hodnik
- Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
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Wang Y, Su YQ, Hensen EJM, Vlachos DG. Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. ACS NANO 2020; 14:13995-14007. [PMID: 33054171 DOI: 10.1021/acsnano.0c06472] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pdn (n = 1-21) clusters supported on CeO2(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.
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Affiliation(s)
- Yifan Wang
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
- Catalysis Center for Energy Innovation, RAPID Manufacturing Institute, and Delaware Energy Institute (DEI), University of Delaware, 221 Academy Street, Newark, Delaware 19716, United States
| | - Ya-Qiong Su
- Laboratory of Inorganic Materials and Catalysis, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
- School of Chemistry, Xi'an Key Laboratory of Sustainable Energy Materials Chemistry, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China
| | - Emiel J M Hensen
- Laboratory of Inorganic Materials and Catalysis, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Dionisios G Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
- Catalysis Center for Energy Innovation, RAPID Manufacturing Institute, and Delaware Energy Institute (DEI), University of Delaware, 221 Academy Street, Newark, Delaware 19716, United States
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