The well-known anti-corrosive property of stainless steels is largely attributed to the addition of Cr,which can assist in forming an inert film on the corroding surface.To maximize the corrosion-resistant ability of ...The well-known anti-corrosive property of stainless steels is largely attributed to the addition of Cr,which can assist in forming an inert film on the corroding surface.To maximize the corrosion-resistant ability of Cr,a thorough study dealing with the passivation behaviors of this metal,including the structure and composition of the passive film as well as related reaction mechanisms,is required.Here,continuous electrochemical adsorptions of OH-groups of water molecules onto Cr terraces in acid solutions are investigated using DFT methods.Different models with various surface conditions are applied.Passivation is found to begin in the active region,and a fully coated surface mainly with oxide is likely to be the starting point of the passive region.The calculated limiting potentials are in reasonable agreement with passivation potentials observed via experiment.展开更多
In this work,we explore the suitability of several density functionals with the generalized gradient approximation(GGA)and beyond for describing the dissociative chemisorption of methane on the reconstructed Pt(110)-(...In this work,we explore the suitability of several density functionals with the generalized gradient approximation(GGA)and beyond for describing the dissociative chemisorption of methane on the reconstructed Pt(110)-(2×1)surface.The bulk and surface structures of the metal,methane adsorption energy,and dissociation barrier are used to assess the functionals.A van der Waals corrected GGA functional(optPBE-vdW)and a metaGGA functional with van der Waals correction(MS PBEl-rVV10)are selected for ab initio molecular dynamics calculations of the sticking probability.Our results suggest that the use of these two functionals may lead to a better agreement with existing experimental results,thus serving as a good starting point for future development of reliable machine-learned potential energy surfaces for the dissociation of methane on the Pt(110)-(2×1)surface.展开更多
The development of high-performance and low-cost oxygen reduction and evolution catalysts that can be easily integrated into existing devices is crucial for the wide deployment of energy storage systems that utilize O...The development of high-performance and low-cost oxygen reduction and evolution catalysts that can be easily integrated into existing devices is crucial for the wide deployment of energy storage systems that utilize O2-H2O chemistries, such as regenerative fuel cells and metal-air batteries. Herein, we report an NHB-activated N-doped hierarchical carbon （NHC） catalyst synthesized via a scalable route, and demonstrate its device integration. The NHC catalyst exhibited good performance for both the oxygen reduction reaction （ORR） and the oxygen evolution reaction （OER）, as demonstrated by means of electrochemical studies and evaluation when integrated into the oxygen electrode of a regenerative fuel cell. The activities observed for both the ORR and the OER were comparable to those achieved by state-of-the-art Pt and Ir catalysts in alkaline environments. We have further identified the critical role of carbon defects as active sites for electrochemical activity through density functional theory calculations and high-resolution TEM visualization. This work highlights the potential of NHC to replace commercial precious metals in regenerative fuel cells and possibly metal-air batteries for cost-effective storage of intermittent renewable energy.展开更多
Single atom catalysts have recently attracted interest due to their maximization of the utilization of expensive noble metals as well as their unique catalytic properties. Based on its surface atomic properties, CeO2 ...Single atom catalysts have recently attracted interest due to their maximization of the utilization of expensive noble metals as well as their unique catalytic properties. Based on its surface atomic properties, CeO2 is one of the most common supports for stabilizing single metal atoms. Many single atom catalysts are limited in their metal contents by the formation of metal nanoparticles once the catalyst support capacity for single atoms has been exceeded. Currently, there are no direct measurements to determine the capacity of a support to stabilize single atoms. In this work we develop a nanoparticle-based technique that allows for quantification of that capacity by redispersing Ru nanoparticles into single atoms and taking advantage of the different catalytic properties of Ru single atoms and nanoparticles in the CO2 hydrogenation reaction. This method avoids complications in metal loading caused by counterions in incipient wetness impregnation and can eventually be applied to a variety of different metals. Results using this technique follow trends in oxygen vacancy concentration and surface oxygen content and show promise as a new method for quantifying support single atom stabilization capacity.展开更多
Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional ...Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional route, in which alkane steam cracking (SC) at high temperature is applied to produce ethylene and propylene, the catalytic ethane/propane non-oxidative dehydrogenation (EDH/PDH) possess the advantages of high selectivity and low energy consumption. Industrially, Pt is the major component to catalyze this reaction, but it suffers from low selectivity and fast deactivation because of favorable coke formation [2].展开更多
Highly active and low-cost catalysts for electrochemical reactions such as the hydrogen evolution reaction （HER） are crucial for the development of efficient energy conversion and storage technologies. Theoretical s...Highly active and low-cost catalysts for electrochemical reactions such as the hydrogen evolution reaction （HER） are crucial for the development of efficient energy conversion and storage technologies. Theoretical simulations have been instrumental in revealing the correlations between the electronic structure of materials and their catalytic activity, and guide the prediction and development of improved catalysts. However, difficulties in accurately engineering the desired atomic sites lead to challenges in making direct comparisons between experi- mental and theoretical results. In MoS2, the Mo-edge has been demonstrated to be active for HER whereas the S-edge is inert. Using a computational descriptor- based approach, we predict that by incorporating transition metal atoms （Fe, Co, Ni, or Cu） the S-edge site should also become HER active. Vertically standing, edge-terminated MoS2 nanofilms provide a well-defined model system for verifying these predictions. The transition metal doped MoS2 nanofilms show an increase in exchange current densities by at least two-fold, in agreement with the theoretical calculations. This work opens up further opportunities for improving electrochemical catalysts by incorporating promoters into particular atomic sites, and for using well-defined systems in order to understand the origin of the promotion effects.展开更多
Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets.Where datasets are lacking,unbiased data generation can be achieved with genetic algorithms.Here a mach...Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets.Where datasets are lacking,unbiased data generation can be achieved with genetic algorithms.Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate.This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning.The approach is used to search for stable,compositionally variant,geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery,e.g.,nanoalloy catalysts.The machine learning accelerated approach,in this case,yields a 50-fold reduction in the number of required energy calculations compared to a traditional“brute force”genetic algorithm.This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible,using density functional theory calculations.展开更多
For high-throughput screening of materials for heterogeneous catalysis,scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species.However,conditioning on a single des...For high-throughput screening of materials for heterogeneous catalysis,scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species.However,conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy.In this article,we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates.With a large dataset,we use Bayesian Information Criteria(BIC)as the model evidence to select the best linear regression model.Furthermore,Gaussian Process Regression(GPR)based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model.This integrated Bayesian model selection and Gaussian process regression,dubbed as residual learning,can achieve performance comparable to standard DFT error(0.1 eV)for most adsorbate system.For sparse and small datasets,we propose an ad hoc Bayesian Model Averaging(BMA)approach to make a robust prediction.With this Bayesian framework,we significantly reduce the model uncertainty and improve the prediction accuracy.The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.展开更多
Small-scale and decentralized production of H_(2)O_(2)via electrochemical reduction of oxygen is of great benefit,especially for sanitization,air and water purification,as well as for a variety of chemical processes.T...Small-scale and decentralized production of H_(2)O_(2)via electrochemical reduction of oxygen is of great benefit,especially for sanitization,air and water purification,as well as for a variety of chemical processes.The development of low-cost and highperformance catalysts for this reaction remains a key challenge.Carbon-based materials have drawn substantial research efforts in recent years due to their advantageous properties,such as high chemical stability and high tunability in active sites and morphology.Deeper understanding of structure–activity relationships can guide the design of improved catalysts.We hypothesize that mass transport to active sites is of great importance,and herein we use carbon materials with unique flower-like superstructures to achieve high activity and selectivity for O2 reduction to H_(2)O_(2).The abundance of nitrogen active sites controlled by pyrolysis temperature resulted in high catalytic activity and selectivity for oxygen reduction reaction(ORR).The flower superstructure showed higher performance than the spherical nanoparticles due to greater accessibility to the active sites.Chemical activation improves the catalysts’performances further,driving the production of H_(2)O_(2)to a record-setting rate of 816 mmol·gcat^(−1)·h^(−1)using a bulk electrolysis setup.This work demonstrates the development of a highly active catalyst for the sustainable production of H_(2)O_(2)through rational design and synthetic control.The understanding from this work provides further insight into the design of future carbon-based electrocatalysts.展开更多
The chemisorption energy is an integral aspect of surface chemistry,central to numerous fields such as catalysis,corrosion,and nanotechnology.Electronic-structure-based methods such as the Newns-Anderson model are the...The chemisorption energy is an integral aspect of surface chemistry,central to numerous fields such as catalysis,corrosion,and nanotechnology.Electronic-structure-based methods such as the Newns-Anderson model are therefore of great importance in guiding the engineering of material surfaces with optimal properties.However,existing methods are inadequate for interpreting complex,multi-metallic systems.Herein,we introduce a physics-based chemisorption model for alloyed transition metal surfaces employing primarily metal d-band properties that accounts for perturbations in both the substrate and adsorbate electronic states upon interaction.Importantly,we show that adsorbate-induced changes in the adsorption site interact with its chemical environment leading to a second-order response in chemisorption energy with the d-filling of the neighboring atoms.We demonstrate the robustness of the model on a wide range of transition metal alloys with O,N,CH,and Li adsorbates yielding a mean absolute error of 0.13 eV versus density functional theory reference chemisorption energies.展开更多
Nickel-iron layered double hydroxide （NiFe-LDH） nanosheets have shown optimal oxygen evolution reaction （OER） performance; however, the role of the intercalated ions in the OER activity remains unclear. In this wo...Nickel-iron layered double hydroxide （NiFe-LDH） nanosheets have shown optimal oxygen evolution reaction （OER） performance; however, the role of the intercalated ions in the OER activity remains unclear. In this work, we show that the activity of the NiFe-LDHs can be tailored by the intercalated anions with different redox potentials. The intercalation of anions with low redox potential （high reducing ability）, such as hypophosphites, leads to NiFe-LDHs with low OER overpotential of 240 mV and a small Tafel slope of 36.9 mV/dec, whereas NiFe-LDHs intercalated with anions of high redox potential （low reducing ability）, such as fluorion, show a high overpotential of 370 mV and a Tafel slope of 80.8 mV/dec. The OER activity shows a surprising linear correlation with the standard redox potential. Density functional theory calculations and X-ray photoelectron spectroscopy analysis indicate that the intercalated anions alter the electronic structure of metal atoms which exposed at the surface. Anions with low standard redox potential and strong reducing ability transfer more electrons to the hydroxide layers. This increases the electron density of the surface metal sites and stabilizes their high-valence states, whose formation is known as the critical step prior to the OER process.展开更多
基金financially supported by the National Key Research and Development Program of China(No.2017YFB0702100)the National Natural Science Foundation of China(Nos.51571028,51431004,and U1706221)financial support from China Scholarship Council
文摘The well-known anti-corrosive property of stainless steels is largely attributed to the addition of Cr,which can assist in forming an inert film on the corroding surface.To maximize the corrosion-resistant ability of Cr,a thorough study dealing with the passivation behaviors of this metal,including the structure and composition of the passive film as well as related reaction mechanisms,is required.Here,continuous electrochemical adsorptions of OH-groups of water molecules onto Cr terraces in acid solutions are investigated using DFT methods.Different models with various surface conditions are applied.Passivation is found to begin in the active region,and a fully coated surface mainly with oxide is likely to be the starting point of the passive region.The calculated limiting potentials are in reasonable agreement with passivation potentials observed via experiment.
基金financial support from the National Natural Science Foundation of China(No.21973013 and No.21673040)the National Natural Science Foundation of Fujian Province,China(No.2020J02025)+3 种基金the“Chuying Program”for the Top Young Talents of Fujian Provincesupported financially through a NWO/CW TOP grant(No.715.017.001)by a grant of supercomputer time from NWO Exacte en Natuurwetenschappen(NWO-ENW,No.2019.015)the National Science Foundation(No.CHE1951328)。
文摘In this work,we explore the suitability of several density functionals with the generalized gradient approximation(GGA)and beyond for describing the dissociative chemisorption of methane on the reconstructed Pt(110)-(2×1)surface.The bulk and surface structures of the metal,methane adsorption energy,and dissociation barrier are used to assess the functionals.A van der Waals corrected GGA functional(optPBE-vdW)and a metaGGA functional with van der Waals correction(MS PBEl-rVV10)are selected for ab initio molecular dynamics calculations of the sticking probability.Our results suggest that the use of these two functionals may lead to a better agreement with existing experimental results,thus serving as a good starting point for future development of reliable machine-learned potential energy surfaces for the dissociation of methane on the Pt(110)-(2×1)surface.
文摘The development of high-performance and low-cost oxygen reduction and evolution catalysts that can be easily integrated into existing devices is crucial for the wide deployment of energy storage systems that utilize O2-H2O chemistries, such as regenerative fuel cells and metal-air batteries. Herein, we report an NHB-activated N-doped hierarchical carbon （NHC） catalyst synthesized via a scalable route, and demonstrate its device integration. The NHC catalyst exhibited good performance for both the oxygen reduction reaction （ORR） and the oxygen evolution reaction （OER）, as demonstrated by means of electrochemical studies and evaluation when integrated into the oxygen electrode of a regenerative fuel cell. The activities observed for both the ORR and the OER were comparable to those achieved by state-of-the-art Pt and Ir catalysts in alkaline environments. We have further identified the critical role of carbon defects as active sites for electrochemical activity through density functional theory calculations and high-resolution TEM visualization. This work highlights the potential of NHC to replace commercial precious metals in regenerative fuel cells and possibly metal-air batteries for cost-effective storage of intermittent renewable energy.
基金support from the Stanford Precourt Institute for Energysupport from the School of Engineering at Stanford University+3 种基金a Terman Faculty Fellowshipsupport from a Stanford Graduate Fellowship(SGF)an EDGE fellowshipsupported by the National Science Foundation under award ECCS-1542152。
文摘Single atom catalysts have recently attracted interest due to their maximization of the utilization of expensive noble metals as well as their unique catalytic properties. Based on its surface atomic properties, CeO2 is one of the most common supports for stabilizing single metal atoms. Many single atom catalysts are limited in their metal contents by the formation of metal nanoparticles once the catalyst support capacity for single atoms has been exceeded. Currently, there are no direct measurements to determine the capacity of a support to stabilize single atoms. In this work we develop a nanoparticle-based technique that allows for quantification of that capacity by redispersing Ru nanoparticles into single atoms and taking advantage of the different catalytic properties of Ru single atoms and nanoparticles in the CO2 hydrogenation reaction. This method avoids complications in metal loading caused by counterions in incipient wetness impregnation and can eventually be applied to a variety of different metals. Results using this technique follow trends in oxygen vacancy concentration and surface oxygen content and show promise as a new method for quantifying support single atom stabilization capacity.
基金supported by the U.S.Department of EnergyOffice of Science,Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division,Catalysis Science Program to the SUNCAT Center for Interface Science and Catalysis。
文摘Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional route, in which alkane steam cracking (SC) at high temperature is applied to produce ethylene and propylene, the catalytic ethane/propane non-oxidative dehydrogenation (EDH/PDH) possess the advantages of high selectivity and low energy consumption. Industrially, Pt is the major component to catalyze this reaction, but it suffers from low selectivity and fast deactivation because of favorable coke formation [2].
文摘Highly active and low-cost catalysts for electrochemical reactions such as the hydrogen evolution reaction （HER） are crucial for the development of efficient energy conversion and storage technologies. Theoretical simulations have been instrumental in revealing the correlations between the electronic structure of materials and their catalytic activity, and guide the prediction and development of improved catalysts. However, difficulties in accurately engineering the desired atomic sites lead to challenges in making direct comparisons between experi- mental and theoretical results. In MoS2, the Mo-edge has been demonstrated to be active for HER whereas the S-edge is inert. Using a computational descriptor- based approach, we predict that by incorporating transition metal atoms （Fe, Co, Ni, or Cu） the S-edge site should also become HER active. Vertically standing, edge-terminated MoS2 nanofilms provide a well-defined model system for verifying these predictions. The transition metal doped MoS2 nanofilms show an increase in exchange current densities by at least two-fold, in agreement with the theoretical calculations. This work opens up further opportunities for improving electrochemical catalysts by incorporating promoters into particular atomic sites, and for using well-defined systems in order to understand the origin of the promotion effects.
基金The authors acknowledge support of the European Commission under the FP7 Fuel Cells and Hydrogen Joint Technology Initiative grant agreement FP7-2012-JTI-FCH-325327(SMARTCat)V-Sustain:The VILLUM Centre for the Science of Sustainable Fuels and Chemicals(no.9455)from VILLUM FONDEN.
文摘Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets.Where datasets are lacking,unbiased data generation can be achieved with genetic algorithms.Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate.This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning.The approach is used to search for stable,compositionally variant,geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery,e.g.,nanoalloy catalysts.The machine learning accelerated approach,in this case,yields a 50-fold reduction in the number of required energy calculations compared to a traditional“brute force”genetic algorithm.This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible,using density functional theory calculations.
文摘For high-throughput screening of materials for heterogeneous catalysis,scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species.However,conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy.In this article,we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates.With a large dataset,we use Bayesian Information Criteria(BIC)as the model evidence to select the best linear regression model.Furthermore,Gaussian Process Regression(GPR)based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model.This integrated Bayesian model selection and Gaussian process regression,dubbed as residual learning,can achieve performance comparable to standard DFT error(0.1 eV)for most adsorbate system.For sparse and small datasets,we propose an ad hoc Bayesian Model Averaging(BMA)approach to make a robust prediction.With this Bayesian framework,we significantly reduce the model uncertainty and improve the prediction accuracy.The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.
基金This research was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division,Catalysis Science Program to the SUNCAT Center for Interface Science and Catalysis.Part of this work was performed at the Stanford Nano Shared Facilities,supported by the National Science Foundation under award ECCS-2026822.
文摘Small-scale and decentralized production of H_(2)O_(2)via electrochemical reduction of oxygen is of great benefit,especially for sanitization,air and water purification,as well as for a variety of chemical processes.The development of low-cost and highperformance catalysts for this reaction remains a key challenge.Carbon-based materials have drawn substantial research efforts in recent years due to their advantageous properties,such as high chemical stability and high tunability in active sites and morphology.Deeper understanding of structure–activity relationships can guide the design of improved catalysts.We hypothesize that mass transport to active sites is of great importance,and herein we use carbon materials with unique flower-like superstructures to achieve high activity and selectivity for O2 reduction to H_(2)O_(2).The abundance of nitrogen active sites controlled by pyrolysis temperature resulted in high catalytic activity and selectivity for oxygen reduction reaction(ORR).The flower superstructure showed higher performance than the spherical nanoparticles due to greater accessibility to the active sites.Chemical activation improves the catalysts’performances further,driving the production of H_(2)O_(2)to a record-setting rate of 816 mmol·gcat^(−1)·h^(−1)using a bulk electrolysis setup.This work demonstrates the development of a highly active catalyst for the sustainable production of H_(2)O_(2)through rational design and synthetic control.The understanding from this work provides further insight into the design of future carbon-based electrocatalysts.
基金J.H.S.gratefully acknowledge funding via the Knut and Alice Wallenberg foundation(grant no.2019.0586)We thank Dr.Johannes Voss,Dr.Karun Kumar Rao and Dr.Benjamin Comer for fruitful discussions and acknowledge computational support from the National Energy Research Scientific Computing Center(computer time allocation m2997),a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘The chemisorption energy is an integral aspect of surface chemistry,central to numerous fields such as catalysis,corrosion,and nanotechnology.Electronic-structure-based methods such as the Newns-Anderson model are therefore of great importance in guiding the engineering of material surfaces with optimal properties.However,existing methods are inadequate for interpreting complex,multi-metallic systems.Herein,we introduce a physics-based chemisorption model for alloyed transition metal surfaces employing primarily metal d-band properties that accounts for perturbations in both the substrate and adsorbate electronic states upon interaction.Importantly,we show that adsorbate-induced changes in the adsorption site interact with its chemical environment leading to a second-order response in chemisorption energy with the d-filling of the neighboring atoms.We demonstrate the robustness of the model on a wide range of transition metal alloys with O,N,CH,and Li adsorbates yielding a mean absolute error of 0.13 eV versus density functional theory reference chemisorption energies.
基金G. L. acknowledges financial supports by the fund of the National Natural Science Foundation of China （No. 21701168） and the Liaoning Natural Science Foundation （No. 20170540897）, and beamline BL14B （Shanghai Synchrotron Radiation Facility） for providing the beam time. A portion of this report was prepared as an account of work sponsored by an agency of the United States Government （D. R. K.）. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accurac~ completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
基金This work was supported by the National Natural Science Foundation of China （NSFC）, the National Key Research and Development Project （Nos. 2016YFF0204402 and 2016YFC0801302）, the Program for Changjiang Scholars, and innovative Research Team in the University, and the Fundamental Research Funds for the Central Universities, and the long term subsidy mechanism from the Ministry of Finance and the Ministry of Education of China. S. S. gratefully acknowledges Villum Foundation.
文摘Nickel-iron layered double hydroxide （NiFe-LDH） nanosheets have shown optimal oxygen evolution reaction （OER） performance; however, the role of the intercalated ions in the OER activity remains unclear. In this work, we show that the activity of the NiFe-LDHs can be tailored by the intercalated anions with different redox potentials. The intercalation of anions with low redox potential （high reducing ability）, such as hypophosphites, leads to NiFe-LDHs with low OER overpotential of 240 mV and a small Tafel slope of 36.9 mV/dec, whereas NiFe-LDHs intercalated with anions of high redox potential （low reducing ability）, such as fluorion, show a high overpotential of 370 mV and a Tafel slope of 80.8 mV/dec. The OER activity shows a surprising linear correlation with the standard redox potential. Density functional theory calculations and X-ray photoelectron spectroscopy analysis indicate that the intercalated anions alter the electronic structure of metal atoms which exposed at the surface. Anions with low standard redox potential and strong reducing ability transfer more electrons to the hydroxide layers. This increases the electron density of the surface metal sites and stabilizes their high-valence states, whose formation is known as the critical step prior to the OER process.
