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Browsing by Author "Zhu, Huiyuan"

Now showing 1 - 9 of 9
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    Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
    Pillai, Hemanth Somarajan (Virginia Tech, 2023-01-12)
    Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments
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    Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
    Gao, Qiang; Pillai, Hemanth Somarajan; Huang, Yang; Liu, Shikai; Mu, Qingmin; Han, Xue; Yan, Zihao; Zhou, Hua; He, Qian; Xin, Hongliang; Zhu, Huiyuan (Nature Portfolio, 2022-04-29)
    Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia. The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V-RHE and a yield rate of 6.25 mol h(-1) g(-1) at -0.6 V-RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
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    Electrochemical Carbon Dioxide Reduction for Renewable Carbonaceous Fuels and Chemicals
    Han, Xue (Virginia Tech, 2023-03-15)
    Electrochemical CO2 reduction reaction (ECO2RR) powered by renewable electricity possesses the potential to store intermittent energy in chemical bonds while producing sustainable chemicals and fuels. Unfortunately, it is hard to achieve low overpotential, high selectivity, and activity simultaneously of ECO2RR. Developing efficient electrocatalysts is the most promising strategy to enhance electrocatalytic activity in CO2 reduction. Herein, we designed novel Bi-Cu2S heterostructures by a one-pot wet-chemistry method. The epitaxial growth of Cu2S on Bi results in abundant interfacial sites and these heterostructured nanocrystals demonstrated high electrocatalytic performance of ECO2RR with high current density, largely reduced overpotential, near-unity FE for formate production (Chapter 2). Meanwhile, we see a lot of opportunities for catalysis in a confined space due to their tunable microenvironment and active sites on the surface, leading to a broad spectrum of electrochemical conversion schemes. Herein, we reveal fundamental concepts of confined catalysis by summarizing recent experimental investigations. We mainly focus on carbon nanotubes (CNTs) encapsulated metal-based materials and summarize their applications in emerging electrochemical reactions, including ECO2RR and more (Chapter 3). Although we were able to obtain high activity and selectivity toward C1 products, it is more attractive to go beyond C1 chemicals to produce C2 products due to their high industrial value. Herein, we designed Ag-modified Cu alloy catalysts that can create a CO-rich local environment for enhancing C-C coupling on Cu for C2 formation. Moreover, Ag corporate in Cu can chemically improve the structural stability of Cu lattice. (Chapter 4) Nevertheless, advanced electrocatalytic platforms cannot be developed without a fundamental understanding of binding configurations of the surface-adsorbed intermediates and adsorbate-adsorbate interaction on the local environment in electrochemical CO2 reduction. In this case, we make discussions of recent developments of machine learning based models of adsorbate-adsorbate interactions, including the oversimplified linear analytic relationships, the cluster expansion models parameterized by machine learning algorithms, and the highly nonlinear deep learning models. We also discuss the challenges of the field, particularly overcoming the limitations of pure data driven models with the integration of computational theory and machine learning of lateral interactions for catalyst materials design. (Chapter 5).
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    Harnessing strong metal-support interactions via a reverse route
    Wu, Peiwen; Tan, Shuai; Moon, Jisue; Yan, Zihao; Fung, Victor; Li, Na; Yang, Shi-Ze; Cheng, Yongqiang; Abney, Carter W.; Wu, Zili; Savara, Aditya; Momen, Ayyoub M.; Jiang, De-en; Su, Dong; Li, Huaming; Zhu, Wenshuai; Dai, Sheng; Zhu, Huiyuan (2020-06-16)
    Engineering strong metal-support interactions (SMSI) is an effective strategy for tuning structures and performances of supported metal catalysts but induces poor exposure of active sites. Here, we demonstrate a strong metal-support interaction via a reverse route (SMSIR) by starting from the final morphology of SMSI (fully-encapsulated core-shell structure) to obtain the intermediate state with desirable exposure of metal sites. Using core-shell nanoparticles (NPs) as a building block, the Pd-FeOx NPs are transformed into a porous yolk-shell structure along with the formation of SMSIR upon treatment under a reductive atmosphere. The final structure, denoted as Pd-Fe3O4-H, exhibits excellent catalytic performance in semi-hydrogenation of acetylene with 100% conversion and 85.1% selectivity to ethylene at 80 degrees C. Detailed electron microscopic and spectroscopic experiments coupled with computational modeling demonstrate that the compelling performance stems from the SMSIR, favoring the formation of surface hydrogen on Pd instead of hydride.
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    High-performance electrolytic oxygen evolution with a seamless armor core-shell FeCoNi oxynitride
    Di, Jun; Zhu, Huiyuan; Xia, Jiexiang; Bao, Jian; Zhang, Pengfei; Yang, Shi-Ze; Li, Huaming; Dai, Sheng (2019-04-21)
    Highly active, low-cost, and durable electrocatalysts for the water oxidation reaction are pivotal in energy conversion and storage schemes. Here we report a nitride-core, oxide-shell-armor structured FeCoNi oxynitride as an efficient oxygen evolution electrocatalyst with a homogeneous nitride (Fe0.70Co0.56Ni0.92N1.0O0.06) core and an oxide (Fe0.48Co0.1Ni0.21N0.05O1.0) shell. The catalyst demonstrated excellent activity for the oxygen evolution reaction with a current density of 10 mA cm(-2) at a low overpotential of 0.291 V in alkaline media (1 M KOH), which is superior to the activities of commercial IrO2, RuO2, and Pt/C catalysts and comparable to those of state-of-the-art catalysts (e.g., NiFe-LDH, NiCo2O4, O-NiCoFe-LDH). Density functional theory simulations suggested that the incorporation of multiple metal elements can indeed improve the reaction energetics with a synergistic effect from the core-shell structure. This unique structure of a nitride-core with a oxide-shell presents a new form of multimetallic oxynitride with compelling performance in electrolytic oxygen evolution.
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    Orbital Level Understanding of Adsorbate-Surface Interactions in Metal Nanocatalysis
    Wang, Siwen (Virginia Tech, 2020-06-15)
    We develop a theoretical framework for a priori estimation of catalytic activity of metal nanoparticles using geometry-based reactivity descriptors of surface atoms and kinetic analysis of reaction pathways at various types of active sites. We show that orbitalwise coordination numbers 𝐶𝑁α (α = 𝑠 or 𝑑) can be used to predict chemical reactivity of a metal site (e.g., adsorption energies of critical reaction intermediates) by being aware of the neighboring chemical environment, outperforming their regular (𝐶𝑁) and generalized (𝐶̅𝑁̅) counterparts with little added computational cost. Here we include two examples to illustrate this method: CO oxidation on Au (5𝑑¹⁰6𝑠¹) and O₂ reduction on Pt (5𝑑⁹6𝑠¹). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis.
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    Structure Sensitivity in the Subnanometer Regime on Pt and Pd Supported Catalysts
    Kuo, Chun-Te (Virginia Tech, 2020-10-29)
    Single-atom and cluster catalysts have been receiving significant interest due to not only their capability to approach the limit of atom efficiency but also to explore fundamentally unique properties. Supported Pt-group single atoms and clusters catalysts in the subnanometer size regime maximize the metal utilization and were reported to have extraordinary activities and/or selectivities compared with nanoparticles for various reactions including hydrogenation reactions. However, the relationship between metal nuclearity, electronic and their unique catalytic properties are still unclear. Thus, it is crucial to establish their relations for better future catalyst design. Ethylene hydrogenation and acetylene hydrogenation are two important probe reactions with the simplest alkene and alkyne, and they have been broadly studied as the benchmark reactions on the various catalyst systems. However, the catalytic properties and reaction mechanism of those hydrogenation reactions for metal nuclearitiy in the subnanometer regime is still not well understood. In this study, we applied different characterization techniques including x-ray absorption fine structure (XAFS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy(XPS), diffuse reflectance infrared spectroscopy (DRIFTS), calorimetry and high-resolution scanning transmission electron microscopy (STEM) to investigate the structure of Pt/TiO2 and Pd/COF single-atom catalysts and tested their catalytic properties for hydrogenation reactions. In order to develop such relations, we varied the nuclearity of Pt supported on TiO2 from single atoms to subnanometer clusters to larger nanoparticles. For acetylene hydrogenation, Pt in the subnanometer size regime exhibits remarkably high selectivity to ethylene compared to its nanoparticle counterparts. The high selectivity is resulted from the decreased electron density on Pt and destabilization of C2H4, which were rationalized by X-ray photoelectron spectroscopy and calorimetry results. On the other hand, the activity of H2 activation and acetylene hydrogenation decreased as Pt nuclearity decreased. Therefore, our results show there's a trade-off between activity and selectivity for acetylene hydrogenation. Additionally, the kinetics measurements of ethylene hydrogenation and acetylene hydrogenation were performed on Pt/TiO2 catalysts, and they found to be structure sensitive for both reactions, which the reaction orders and activation energy changes as particles size change. The activity of ethylene hydrogenation decreases, and activation energy increase from 43 to 86 kJ/mol, as Pt nuclearity decreased from an average size of 2.1 nm to 0.7 nm and single atoms. The reaction orders in hydrocarbons (ethylene and acetylene) were less negative on subnanometer clusters and single atoms in contract to nanoparticles. The results imply that hydrocarbons, ethylene and acetylene species, do not poison the catalyst on Pt in the subnanometer size regime, and hydrogen activation turn to competitive adsorption path with surface hydrocarbons species. Moreover, single atom Pd supported on imine-linked covalent organic framework was synthesized, characterized by a various of techniques including X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) of adsorbed CO, and evaluated its catalytic properties for ethylene hydrogenation. The XAS results show that Pd atoms are isolated and stabilized by two covalent Pd–N and Pd-Cl bonds. DRIFTS of CO adsorption shows a sharp symmetrical peak at 2130 cm−1. The Pd single atoms are active for hydrogenation of ethylene to ethane at room temperature. The reaction orders in C2H4 and H2 were 0.0 and 0.5 suggesting that ethylene adsorption is not limiting while hydrogen forms on Pd through dissociative adsorption.
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    Toward Designing Active ORR Catalysts via Interpretable and Explainable Machine Learning
    Omidvar, Noushin (Virginia Tech, 2022-09-22)
    The electrochemical oxygen reduction reaction (ORR) is a very important catalytic process that is directly used in carbon-free energy systems like fuel cells. However, the lack of active, stable, and cost-effective ORR cathode materials has been a major impediment to the broad adoption of these technologies. So, the challenge for researchers in catalysis is to find catalysts that are electrochemically efficient to drive the reaction, made of earth-abundant elements to lower material costs and allow scalability, and stable to make them last longer. The majority of commercial catalysts that are now being used have been found through trial and error techniques that rely on the chemical intuition of experts. This method of empirical discovery is, however, very challenging, slow, and complicated because the performance of the catalyst depends on a myriad of factors. Researchers have recently turned to machine learning (ML) to find and design heterogeneous catalysts faster with emerging catalysis databases. Black-box models make up a lot of the ML models that are used in the field to predict the properties of catalysts that are important to their performance, such as their adsorption energies to reaction intermediates. However, as these black-box models are based on very complicated mathematical formulas, it is very hard to figure out how they work and the underlying physics of the desired catalyst properties remains hidden. As a way to open up these black boxes and make them easier to understand, more attention is being paid to interpretable and explainable ML. This work aims to speed up the process of screening and optimizing Pt monolayer alloys for ORR while gaining physical insights. We use a theory-infused machine learning framework in combination with a high-throughput active screening approach to effectively find promising ORR Pt monolayer catalysts. Furthermore, an explainability game-theory approach is employed to find electronic factors that control surface reactivity. The novel insights in this study can provide new design strategies that could shape the paradigm of catalyst discovery.
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    Understanding Interfacial Kinetics of Catalytic Carbon Dioxide Transformations from Multiscale Simulations
    Mou, Tianyou (Virginia Tech, 2023-07-19)
    Carbon dioxide (CO2), as a greenhouse gas, has shown to achieve the highest level in history, causes the global warming issue, leading to a 1.2 ℃ increase of the global average temperature. The consumption of fossil fuels is one of the main reasons that cause CO2 emission. Current industrial production of chemicals accounts for 29% of total fossil fuels consumption, which can be the feedstock or raw materials for carbon source, or act as the fuel to generate heat and power. CO2 conversion technologies, e.g., thermo-catalytic reaction and electrochemical reduction, have drawn researchers' attention, since they have the potential to resolve the feedstock and fuel consumption sectors of chemical production at the same time. CO2 conversion technologies use CO2 as the direct carbon source of chemicals and store the intermittent renewable energies as the energy source, which can ultimately achieve a net-zero CO2 emission and produce value-added chemical products. However, there are challenges for a practical application of CO2 conversion technologies. For instance, electrochemical CO2 reduction reaction (ECO2RR) suffers from the low activity and selectivity, while thermocatalytic CO2 conversion, or the CO2 hydrogenation reaction, usually requires harsh reaction conditions and has a low selectivity. Nonetheless, the improvement of developing new promising catalysts remains limited, due to the lack of insights of the reactions. The complex reaction networks and kinetics lead to an elusive reaction mechanism, and various effects, e.g., solvation, potential, structure, and coverage, hinder our fundamental understanding of catalytic processes. Herein, we report the efforts that we have been put in to gain insights of reaction mechanism of CO2 reduction reactions. Bi has shown to reduce CO2 to formic acid (HCOOH), while we have found that, by constituting a Bi-Cu2S heterostructure catalyst, a better catalytic performance was achieved, due to the structural effect of the interface (Chapter 2). However, it is shown that the CO2 electrochemical reduction mechanism on Bi has changed when switching the electrolyte from water to aprotic media, e.g., ionic liquids, and CO was obtained as the main product instead of HCOOH, showing a shift of reaction pathway due to the electrolyte effect (Chapter 3). However, the fundamental understanding of reaction mechanism requires not only the reaction pathways, but the reaction kinetics under reaction conditions, where the lateral or adsorbate-adsorbate interactions play an important role. In this case, we summarized recent advances of applications of machine learning (ML) algorithms for adsorbate-adsorbate interaction model developments to deal with the realistic reaction kinetics (Chapter 4). The lattice based Kinetic Monte Carlo (KMC) has shown promising performances for considering the lateral interactions of surface reactions. We report the mechanistic and KMC kinetic study of CO2 hydrogenation on Cesium promoted Au(111) surface, to gain insights of alkali metal promoting effects under reaction conditions (Chapter 5). To expand the scope, the integration of CO2 reduction with the C-N bond formation provides a promising strategy to produce more value-added product such as urea. Recent studies show that urea can be produced by reducing CO2 and nitrate (NO3-) from wastewater, which mitigate both global warming and nitrate pollution issue. However, the reaction mechanism remains elusive due to the complicated reaction network. Therefore, we employed the first-principles molecular dynamics to reveal the reaction mechanism of C-N coupling and the effect of different reaction conditions including applied potential and electrolyte (Chapter 6). Although recent advances in the computational catalysis field have significantly push forward the understanding of the chemistry nature of heterogeneous catalysis, the gap between theory and experiment remains far beyond bridged due to the complexity nature of the problem in a wide range of time and length scales, hinders the development and discovery of active catalytic materials. Recent advances of narrowing and bridging the complexity gap between theory and experiment with machine learning have been summarized to emphasize the importance of utilizing machine learning for rational catalyst design (Chapter 7).