Stability and Dynamics of Zeolite-Confined Gold Nanoclusters.

Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood.

Toward an ab Initio Description of Adsorbate Surface Dynamics.

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet.

Real-time visual intelligence for defect detection in pharmaceutical packaging.

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production.

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis.

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH.

Dynamic structural evolution of MgO-supported palladium catalysts: from metal to metal oxide nanoparticles to surface then subsurface atomically dispersed cations.

Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states-which influence the metal nuclearities, oxidation states, and catalytic properties. In this investigation, we report the results of X-ray absorption spectroscopy, scanning transmission electron microscopy, and other physical characterization techniques, bolstered by density functional theory, to elucidate the structural transformations of a set of MgO-supported palladium catalysts under oxidative treatment conditions. As the calcination temperature increased, the as-synthesized supported metallic palladium nanoparticles underwent oxidation to form palladium oxides (at approximately 400 °C), which, at approximately 500 °C, were oxidatively fragmented to form mixtures of atomically dispersed palladium cations.

Reversible Intrapore Redox Cycling of Platinum in Platinum-Ion-Exchanged HZSM-5 Catalysts.

Isolated platinum(II) ions anchored at acid sites in the pores of zeolite HZSM-5, initially introduced by aqueous ion exchange, were reduced to form platinum nanoparticles that are stably dispersed with a narrow size distribution (1.3 ± 0.4 nm in average diameter).

Reduction of Cofed Carbon Dioxide Modifies the Local Coordination Environment of Zeolite-Supported, Atomically Dispersed Chromium to Promote Ethane Dehydrogenation.

The reduction of CO is known to promote increased alkene yields from alkane dehydrogenations when the reactions are cocatalyzed. The mechanism of this promotion is not understood in the context of catalyst active-site environments because CO is amphoteric, and even general aspects of the chemistry, including the significance of competing side reactions, differ significantly across catalysts. Atomically dispersed chromium cations stabilized in highly siliceous MFI zeolite are shown here to enable the study of the role of parallel CO reduction during ethylene-selective ethane dehydrogenation.

A reactive molecular dynamics model for uranium/hydrogen containing systems.

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency.

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts.

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NH ( = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF.

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials.

Machine learning potentials (MLPs) capable of accurately describing complex potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions.

Elucidating correlated defects in metal organic frameworks using theory-guided inelastic neutron scattering spectroscopy.

Metal organic frameworks (MOFs) that incorporate metal oxide cluster nodes, exemplified by UiO-66, have been widely studied, especially in terms of their deviations from the ideal, defect-free crystalline structures. Although defects such as missing linkers, missing nodes, and the presence of adventitious synthesis-derived node ligands (such as acetates and formates) have been proposed, their exact structures remain unknown. Previously, it was demonstrated that defects are correlated and span multiple unit cells.

Elucidating the Initial Steps in α-Uranium Hydriding Using First-Principles Calculations.

Hydrogen embrittlement of uranium, which arises due to the formation of a structurally weak pyrophoric hydride, poses a major safety risk in material applications. Previous experiments have shown that hydriding begins on the top or near the surface (, subsurface) of α-uranium. However, the fundamental molecular-level mechanism of this process remains unknown.

Interconversion of Atomically Dispersed Platinum Cations and Platinum Clusters in Zeolite ZSM-5 and Formation of Platinum -Dicarbonyls.

Catalysts composed of platinum dispersed on zeolite supports are widely applied in industry, and coking and sintering of platinum during operation under reactive conditions require their oxidative regeneration, with the platinum cycling between clusters and cations. The intermediate platinum species have remained only incompletely understood. Here, we report an experimental and theoretical investigation of the structure, bonding, and local environment of cationic platinum species in zeolite ZSM-5, which are key intermediates in this cycling.

Atomically Dispersed Platinum in Surface and Subsurface Sites on MgO Have Contrasting Catalytic Properties for CO Oxidation.

Atomically dispersed metals on metal oxide supports are a rapidly growing class of catalysts. Developing an understanding of where and how the metals are bonded to the supports is challenging because support surfaces are heterogeneous, and most reports lack a detailed consideration of these points. Herein, we report two atomically dispersed CO oxidation catalysts having markedly different metal-support interactions: platinum in the first layer of crystalline MgO powder and platinum in the second layer of this support.

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts.

Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically dispersed platinum supported on crystalline MgO (chosen for its well-defined bonding sites) as a prototypical example, we demonstrate how systematic density functional theory calculations for assessing all the potentially stable platinum sites, combined with automated analysis of extended X-ray absorption fine structure (EXAFS) spectra, leads to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation.

Cs-RHO Goes from Worst to Best as Water Enhances Equilibrium CO Adsorption via Phase Change.

The strong affinity of water to zeolite adsorbents has made adsorption of CO from humid gas mixtures such as flue gas nearly impossible under equilibrated conditions. Here, in this manuscript, we describe a unique cooperative adsorption mechanism between HO and Cs cations on Cs-RHO zeolite, which actually facilitates the equilibrium adsorption of CO under humid conditions. Our data demonstrate that, at a relative humidity of 5%, Cs-RHO adsorbs 3-fold higher amounts of CO relative to dry conditions, at a temperature of 30 °C and CO pressure of 1 bar.

Near-Surface Gas-Phase Methoxymethanol Is Generated by Methanol Oxidation over Pd-Based Catalysts.

Catalytic conversion of alcohols underlies many commodity and fine chemical syntheses, but a complete mechanistic understanding is lacking. We examined catalytic oxidative conversion of methanol near atmospheric pressure using operando small-aperture molecular beam time-of-flight mass spectrometry, interrogating the gas phase 500 μm above Pd-based catalyst surfaces. In addition to a variety of stable C species, we detected methoxymethanol (CHOCHOH)─a rarely observed and reactive C oxygenate that has been proposed to be a critical intermediate in methyl formate production.

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency.

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g.

Circumventing Scaling Relations in Oxygen Electrochemistry Using Metal-Organic Frameworks.

It has been well-established that unfavorable scaling relationships between *OOH, *OH, and *O are responsible for the high overpotentials associated with oxygen electrochemistry. A number of strategies have been proposed for breaking these linear constraints for traditional electrocatalysts (e.g.

Two-Dimensional Conductive Ni-HAB as a Catalyst for the Electrochemical Oxygen Reduction Reaction.

Catalytic systems whose properties can be systematically tuned via changes in synthesis conditions are highly desirable for the next-generation catalyst design and optimization. Herein, we present a two-dimensional (2D) conductive metal-organic framework consisting of M-N units (M = Ni, Cu) and a hexaaminobenzene (HAB) linker as a catalyst for the oxygen reduction reaction. By varying synthetic conditions, we prepared two Ni-HAB catalysts with different crystallinities, resulting in catalytic systems with different electric conductivities, electrochemical activity, and stability.

Nature of Lone-Pair-Surface Bonds and Their Scaling Relations.

We investigate the (surface) bonding of a class of industrially and biologically important molecules in which the chemically active orbital is a 2 p electron lone pair located on an N or O atom bound via single bonds to H or alkyl groups. This class includes water, ammonia, alcohols, ethers, and amines. Using extensive density functional theory (DFT) calculations, we discover scaling relations (correlations) among molecular binding energies of different members of this class: the bonding energetics of a single member can be used as a descriptor for other members.

Understanding Catalytic Activity Trends in the Oxygen Reduction Reaction.

Despite the dedicated search for novel catalysts for fuel cell applications, the intrinsic oxygen reduction reaction (ORR) activity of materials has not improved significantly over the past decade. Here, we review the role of theory in understanding the ORR mechanism and highlight the descriptor-based approaches that have been used to identify catalysts with increased activity. Specifically, by showing that the performance of the commonly studied materials (e.

Understanding trends in C-H bond activation in heterogeneous catalysis.

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold.