How oxidation state and lattice distortion influence the oxygen evolution activity in acid of iridium double perovskites

Retuerto, M.; Pascual, L.; Piqué, Oriol; Kayser, Paula; Salam, Mohamed Abdel; Mokhtar, Mohamed; Alonso, J. A.; Peña, M.A.; Calle‐Vallejo, Federico; Rojas, Sergio

doi:10.1039/d0ta10316k

Cited by 44 publications

(46 citation statements)

References 73 publications

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“…Analyzing these results (results can be found in Supporting Information, Table S8), we have observed tensile strain in the second region atoms and compressive strain in the third region atoms for most of the active catalysts compared to the pure Cu system. Rojas and co-workers 69 have shown that distortion on the catalytic surface can improve the activity of the catalyst. Therefore, the distortion in the microstructure could be the reason for the enhancement of catalytic activity of these catalysts.…”

Section: Feature Importance and The Correlation Matrixmentioning

confidence: 99%

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Roy

Mandal

Pathak

2021

ACS Appl. Mater. Interfaces

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The revolutionary development of machine learning and data science and exploration of its application in material science are huge achievements of the scientific community in the past decade. In this work, we have reported an efficient approach of machine learning-aided high-throughput screening for finding selective earth-abundant high-entropy alloy-based catalysts for CO 2 to methanol formation using a machine learning algorithm and microstructure model. For this, we have chosen earth-abundant Cu, Co, Ni, Zn, and Mg metals to form various alloy-based compositions (bimetallic, trimetallic, tetrametallic, and high-entropy alloys) for selective CO 2 reduction reaction toward CH 3 OH. Since there are several possible surface microstructures for different alloys, we have used machine learning along with DFT calculations for high-throughput screening of the catalysts. In this study, the stability of various 8-atom fcc periodic (111) surface unit cells has been calculated using the atomic-size difference factor (δ) as well as the ratio taken from Gibbs free energy of mixing (Ω). Thinking about the simplicity and accuracy, microstructure models by considering the neighboring atoms of the adsorption sites and others as Cu atoms have been considered for different adsorption sites (on-top, bridge, and hollow-hcp). Moreover, the adsorption energies of the *H, *O, *CO, *HCO, *H 2 CO, and *H 3 CO intermediates have been predicted using the best fitted algorithm of the training set. The predicted adsorption energies have been screened based on the pure Cu adsorption energy. Furthermore, the screened catalysts have been correlated among different adsorption site microstructures. At the end, we were able to find seven active catalysts, among which two catalysts are CuCoNiZn-based tetrametallic, three catalysts are CuNiZnbased trimetallic, and two catalysts are CuCoZn-based trimetallic alloys. Hence, this work demonstrates not an ultimate but an efficient approach for finding new product-selective catalysts, and we expect that it can be convenient for other similar types of reactions in forthcoming days.

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Section: Feature Importance and The Correlation Matrixmentioning

confidence: 99%

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Roy

Mandal

Pathak

2021

ACS Appl. Mater. Interfaces

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“…Hence, the formation of *HCO from *CO is preferred on Cu-dNC as the distorted surface can stabilize the intermediates as also reported by Rojas and coworkers, who have shown that the lattice distortion of the catalyst can influence the activity, and higher distortion results in higher catalytic activity. 68 Moreover, the average coordination number (CN) of Cu-dNC (CN = 7) is less than that of the periodic Cu(100) surface (CN = 8). This is due to the higher extent of deformation within Cu-dNC.…”

Section: Comparison Among C 1 -And C 2 -Based Productsmentioning

confidence: 99%

Density Functional Theory Calculations on Electrocatalytic CO₂ Hydrogenation to C₂-Based Products over Cu(100) Nanocubes

Mandal

Pathak

2021

ACS Appl. Nano Mater.

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In this study, we have considered a Cu nanocube (Cu-NC)-based catalyst exposed with (100) facets for CO 2 hydrogenation reactions. All the feasible mechanistic pathways for the formation of C 1 (HCOOH, CH 3 OH, and CH 4 ) and C 2 (C 2 H 4 and C 2 H 5 OH) based products have been explored using the density functional theoretical calculations, and the most plausible pathways have been identified. The calculated results are compared with the previous reports on the periodic Cu(100) and Cu(111) surfaces and also on the surface of the Cu 85 nanocluster and Cu(111) monolayer. The in-depth mechanistic investigation shows that Cu-NC can be very selective toward the C 2 -based products with a lower limiting potential (calculated) compared to the periodic surfaces. The underlying reasons for such findings have been explained and compared that with the periodic surfaces. We therefore propose that the Cu-NC-based catalysts can be more promising for C 2 -based products.

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“…The improvement in activity with testing is hypothesized to be caused by Sr leaching into the acidic electrolyte, leaving behind a highly active surface iridium oxide polymorph. , Many questions remain in the explanation of its heightened OER activity and stability in acid, including the identification of specific active sites, surface structural changes, and the role of electronic structure and oxidation state of surface Ir sites. While orthorhombic SrIrO 3 requires massive energy inputs or lattice stabilization methods to achieve syntheses, B-site alkaline earth and transition metal substitution enables syntheses at reasonable temperatures and pressures to obtain powder forms of SrIr x M 1– x O 3 , where M is the partially substituted metal. − …”

Section: Introductionmentioning

confidence: 99%

Constant Change: Exploring Dynamic Oxygen Evolution Reaction Catalysis and Material Transformations in Strontium Zinc Iridate Perovskite in Acid

2021

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While iridium-based perovskites have been identified as promising candidates for the oxygen evolution reaction (OER) in proton exchange membrane (PEM) electrolyzer applications, an improved fundamental understanding of these highly dynamic materials under reaction conditions is needed to inform more robust future catalyst design. Herein, we study the highly active SrIr0.8Zn0.2O3 perovskite for the OER in acid by employing electrochemical experiments with in situ and ex situ characterization techniques to understand the dynamic nature of this material at both short and long time scales. We observe initial intrinsic OER activity improvement with electrochemical cycling as well as an initial increase of Ir oxidation state under OER conditions via in situ X-ray absorption spectroscopy. We discover that the SrIr0.8Zn0.2O3 perovskite experiences an OER-induced metal to insulator transition (MIT) with extensive electrochemical cycling, caused by surface reorganization and changes to the material crystallinity that occur with exposure to an acidic and oxidizing environment. Our novel identification of an OER-induced MIT for iridate perovskites reveals an additional stability concern for iridate catalysts which are known to experience material dissolution challenges; this work ultimately aims to inform future catalyst material design for PEM water electrolysis applications.

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How oxidation state and lattice distortion influence the oxygen evolution activity in acid of iridium double perovskites

Abstract: In view of iridium's scarceness and high cost, Ir-containing catalysts for water electrolyzers should have low loadings and maximal utilization.

Cited by 44 publications

References 73 publications

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Density Functional Theory Calculations on Electrocatalytic CO₂ Hydrogenation to C₂-Based Products over Cu(100) Nanocubes

Constant Change: Exploring Dynamic Oxygen Evolution Reaction Catalysis and Material Transformations in Strontium Zinc Iridate Perovskite in Acid

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How oxidation state and lattice distortion influence the oxygen evolution activity in acid of iridium double perovskites

Abstract: In view of iridium's scarceness and high cost, Ir-containing catalysts for water electrolyzers should have low loadings and maximal utilization.

Cited by 44 publications

References 73 publications

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol

Density Functional Theory Calculations on Electrocatalytic CO2 Hydrogenation to C2-Based Products over Cu(100) Nanocubes

Constant Change: Exploring Dynamic Oxygen Evolution Reaction Catalysis and Material Transformations in Strontium Zinc Iridate Perovskite in Acid

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Product

Resources

About

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO₂ Hydrogenation to Methanol

Density Functional Theory Calculations on Electrocatalytic CO₂ Hydrogenation to C₂-Based Products over Cu(100) Nanocubes