publications

2020

1. Catalysis at Metal/Oxide Interfaces: Density Functional Theory and Microkinetic Modeling of Water Gas Shift at Pt/MgO Boundaries

   Ghanekar, Pushkar, Kubal, Joseph, Cui, Yanran, Mitchell, Garrett, Delgass, W. Nicholas, Ribeiro, Fabio, and Greeley, Jeffrey

   Topics in Catalysis 2020

   Abs

   The impact of metal/oxide interfaces on the catalytic properties of oxide-supported metal nanoparticles is a topic of longstanding interest in the field of heterogeneous catalysis. The significance of the metal/oxide interaction has been shown to vary according to both the inherent reactivity of the metal nanoparticle and the properties of the oxide support, with effects such as the metal d-band center, the nanoparticle shape, and the reducibility of the oxide believed to contribute to the overall system reactivity. In recent years, the water gas shift (WGS) reaction, wherein carbon monoxide and water are converted to carbon dioxide and hydrogen, has emerged as a model chemistry to probe the molecular-level details of how catalysis can be promoted in such environments, and this reaction is the focus of the present contribution. Using a combination of periodic Density Functional Theory calculations and microkinetic modeling, we present a comprehensive analysis of the WGS mechanism at the interface between a quasi-one dimensional platinum nanowire and an irreducible MgO support. The nanowire is lattice matched to the MgO support to remove spurious strain at the metal/oxide interface, and reactions both on the nanowire and at the three-phase boundary itself are considered in the mechanistic analysis. Additionally, to elucidate the consequences of adsorbate–adsorbate interactions on the WGS chemistry, an ab-initio thermodynamic analysis of CO coverage is performed, and the impact of the higher coverage CO states on the reaction chemistry is explicitly evaluated. These results are combined with detailed calculations of adsorbate entropies and dual-site microkinetic modeling to determine the kinetically significant features of the WGS reaction network which are subsequently, validated through experimental measurements of apparent reaction orders and activation barrier. The analysis demonstrates the important role that the metal/oxide interface plays in the reaction, with the water dissociation step being facile at the interface compared to the pure metal or oxide surfaces. Further, explicit consideration of CO interactions with other adsorbates at the metal/oxide interface is found to be central to correctly determining reaction mechanisms, rate determining steps, reaction orders, and effective activation barriers. These results are captured in a closed-form Langmuir–Hinshelwood model, derived from a simplified version of the complete microkinetic analysis, which reveals, among other results, that the celebrated carboxyl mechanism of Mavrikakis and coworkers is the governing pathway when accounting for reaction-relevant CO coverages.

2. Origin of Electronic Modification of Platinum in a Pt3V Alloy and Its Consequences for Propane Dehydrogenation Catalysis

   Purdy, Stephen C., Ghanekar, Pushkar, Mitchell, Garrett, Kropf, A. Jeremy, Zemlyanov, Dmitry Y., Ren, Yang, Ribeiro, Fabio, Delgass, W. Nicholas, Greeley, Jeffrey, and Miller, Jeffrey T.

   ACS Applied Energy Materials 2020

   Abs

   We demonstrate the synthesis of a Pt3V alloy and Pt/Pt3V core/shell catalysts, which are highly selective for propane dehydrogenation. The selectivity is a result of the Pt3V intermetallic phase, which was characterized by in situ synchrotron XRD and XAS. Formation of a continuous alloy surface layer 2–3 atomic layers thick was sufficient to obtain identical catalytic properties between a core–shell and full alloy catalyst, which demonstrates the length scale over which electronic effects pertinent to dehydrogenation act. Electronic characterization of the alloy phase was investigated by using DFT, XPS, XANES, and RIXS, all of which show a change in the energy of the filled and unfilled Pt 5d states resulting from Pt–V bonding. The electronic modification leads to a change in the most stable binding site of hydrocarbon fragments, which bind to V containing ensembles despite the presence of 3-fold Pt ensembles in Pt3V. In addition, electronic modification destabilizes deeply dehydrogenated species thought to be responsible for hydrogenolysis and coke formation.

2021

1. Promoting a Safe Laboratory Environment Using the Reactive Hazard Evaluation and Analysis Compilation Tool

   Talpade, Abhijit D., Ghanekar, Pushkar, Ezenwa, Sopuruchukwu, Joshi, Ravi, Kravitz, Samuel, Tunga, Anirudh, Devaraj, Jayachandran, Ribeiro, Fabio H., and Mentzer, Ray

   ACS Chemical Health & Safety 2021

   Abs

   In the past several years, the U.S. Chemical Safety Board has found an increase in the frequency of laboratory accidents and injuries. An independent survey of industrial and academic laboratories by the authors indicated the shortage of documentation on best practices and lack of free and user-friendly risk assessment tools to be some of the key reasons for the occurrence of safety incidents. Thus, development of a framework to document, assess, and mitigate hazards is a critical starting point for ensuring safe laboratory practices. To address this requirement, Reactive Hazards Evaluation Analysis and Compilation Tool (RHEACT), an online platform to compile and scrutinize hazards-related information, was developed. When planning an experiment, the researchers provide RHEACT: (1) information about the chemicals involved in the reaction, in the form of Safety Data Sheets (SDS), and (2) operating parameters of the reaction. Through the user-supplied SDS, an operational hazard matrix and a chemical compatibility matrix are generated. In addition, adiabatic temperature rise of the reaction is estimated to ensure that the chemistry is within user-controlled bounds. The user is provided with a broad initial evaluation of potential hazards and is notified of safety concerns associated with the reaction before conducting the experiment. We believe that this user-friendly online tool will help engender a safer laboratory working environment.

2022

1. Toward improved safety culture in academic and industrial chemical laboratories: an assessment and recommendation of best practices

   Ezenwa, Sopuruchukwu, Talpade, Abhijit D, Ghanekar, Pushkar, Joshi, Ravi, Devaraj, Jayachandran, Ribeiro, Fabio H, and Mentzer, Ray

   ACS Chemical Health & Safety 2022
2. Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

   Ghanekar, Pushkar G., Deshpande, Siddharth, and Greeley, Jeffrey

   Nature Communications 2022

   Abs

   Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

2023

1. Active Learning of Ternary Alloy Structures and Energies

   Deshmukh, Gaurav, Wichrowski, Noah J., Evangelou, Nikolaos, Ghanekar, Pushkar G., Deshpande, Siddharth, Kevrekidis, Ioannis G., and Greeley, Jeffrey

   2023

   Abs

   High-throughput screening of catalysts using first-principles methods, such as density functional theory (DFT), has traditionally been limited by the large, complex, and multidimensional nature of the associated materials spaces. However, machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of these spaces in a data-efficient manner, typically through active learning-based workflows. In this work, we combine such an active learning scheme with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). Specifically, we train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and utilize the model to make and improve predictions on ternary structures. To do so, we perform reduced optimization over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the manifold learning scheme known as Diffusion Maps. Inspired by statistical mechanics, we derive and apply a dropout-informed acquisition function to select ensembles from which to sample additional structures. During each iteration of our active learning scheme, a representative number of crystals that minimize the acquisition function is selected, their energies are computed with DFT, and our dGCN model is retrained. We demonstrate that both of our reduced optimization techniques can be used to improve predictions of the formation free energy, the target property that determines HEA stability, in the ternary alloy space with a significantly reduced number of costly DFT calculations compared to a high-fidelity model. However, the manner in which these two disparate schemes converge to the target property differs: the physics-based scheme appears akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both active learning schemes can be extended further to incorporate greater number of elements, surface structures, and adsorbate motifs.

2024