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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Carbon flowers as electrocatalysts for the reduction of oxygen to hydrogen peroxide

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 high-performance 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 O 2 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·g cat -1 ·h -1 using a bulk electrolysis setup. In conclusion, 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Speciation of Nanocatalysts Using X-ray Absorption Spectroscopy Assisted by Machine Learning

Structure and morphology of supported nanoparticle catalysts play important roles in many industrial reactions. Recent progress has identified key aspects of structure-activity relationships at nanoscale and novel methods to study the local environment of the active sites. X-ray absorption fine structure (XAFS) spectroscopy, despite being a leading technique for this purpose, is hampered significantly by its ensemble-averaging nature which often leads to a bias towards a single "representative" structure. Learning heterogeneous distributions of nanostructures at the inter- and intra-particle level from the average XAFS spectrum is a formidable challenge that can be overcome in some cases, described in this Perspective. Here we also discuss emerging machine learning techniques for extracting the information about heterogeneity of metal species from XAFS data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗
DOE OSTI2024

Unveiling Synergetic Photocatalytic Activity from Heterometallic Ti/Ce Clusters

Titanium-oxo clusters, with their robust structure and suitable optical and electronic properties, have been widely investigated as photocatalysts. Heterometallic Ti/M-oxo clusters provide additional tunability and functionality, which enable systematic structure-activity investigations to elucidate reaction mechanisms and improve catalyst de-sign. Incorporating cerium into Ti-oxo clusters can provide additional redox (Ce IV /Ce III ) and oxygen harvesting ability, but to date only limited number of structurally defined titanium-cerium (Ti/Ce) clusters have been reported due to their synthetic challenges. Herein, we report the synthesis and photocatalytic properties of two structurally defined Ti/Ce-oxo clusters, Ti 8 Ce 2 (BA) 16 and Ti 9 Ce 4 (BA) 20 , as well as a TiCe-BA cluster with a calculated formula of Ti 20 Ce 9 O 36 (BA) 42 . Photocatalytic study of these clusters demonstrates that the amount of Ce 3+ species greatly impacts its photocatalytic oxidation performance, and their superior photocatalytic reactivity towards aerobic alcohol oxidation can be contributed to the synergistic effects of the multiple radical species generated upon light absorption. Furthermore, this work represents a significant milestone in the construction of stable Ti/Ce-oxo clusters, enriching the current library of known heterometallic Ti/M-oxo clusters, and providing a series of crystalline materials with great promise of photo-luminescence and photovoltaic chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Design, synthesis, evaluation and X-ray structural studies of potent HIV-1 protease inhibitors containing substituted oxaspirocyclic carbamates as the P2 ligands

Here, we report here the design, synthesis and evaluation of a series of HIV-1 protease inhibitors that incorporate substituted oxaspirocyclic carbamate derivatives to serve as the P2 ligands. Various substituted ligand derivatives were synthesized in a racemic manner, using a tandem Prins/pinacol reaction as the key reaction. This reaction sets the relative stereochemistry of the oxaspirocyclic template in a highly diastereoselective manner. Reaction of the resulting ketone with enantiopure (S)-tert-butyl sulfinamide provided a convenient pathway to resolve the oxaspirocyclic ketone derivatives. The absolute stereochemical identity was determined by X-ray crystallography. The structure-activity studies demonstrate the effect of the stereochemistry of the oxaspirocyclic ring systems as well as the substitution effect on the aromatic ring. Several inhibitors exhibited potent HIV-1 protease inhibitory activity. One of these inhibitors displayed subnanomolar HIV-1 protease affinity and also exhibited potent antiviral activity. A high-resolution X-ray crystal structure of this inhibitor-bound HIV-1 protease show that the oxaspirocyclic P2 ligand forms an unconventional C–H⋯O bond with the backbone carboxyl group of Gly48’ and an interesting N–H … π interaction with the aromatic ring in the S2 subsite of HIV-1 protease active site.

Antiviral↗
DOE OSTI2021

Boosting the activity of non-platinum group metal electrocatalyst for the reduction of oxygen via dual-ligated atomically dispersed precursors immobilized on carbon supports

This paper describes the use of both atomically dispersed precursors (ADPs) and conductive carbon dispersion towards the synthesis of iron-based single atom electrocatalysts for the oxygen reduction reaction (ORR). For non-platinum group metal (non-PGM) catalysts, single iron, cobalt or manganese atoms coordinated with nitrogen are the most active structures towards the ORR. Achieving a high density of active sites made of single atoms is still challenging, requiring careful controls of pyrolysis to reduce the sintering of metal active sites. Herewith, we present a new strategy to synthesize iron-based single atom ORR electrocatalysts using a two pronged approach. We first designed a dual-ligated metal organic framework (MOF) precursor. This MOF was then immobilized onto Ketjen black carbon that serves as a conductive dispersion medium for creating the highly dispersed single atom sites. In this work, we demonstrate a near complete dispersion of the iron sites without obvious formation of nanoparticles. The activity of the resulting electrocatalyst exhibited an onset potential of 0.96 V and a half-wave potential of 0.84 V vs. reversible hydrogen electrode (RHE).

42 ENGINEERING↗
DOE OSTI2026

Exploration of Nirmatrelvir Derivatives as Optimized SARS‐CoV‐2 Antivirals

Nirmatrelvir (NMV) is a SARS‐CoV‐2 antiviral component of the approved COVID‐19 therapeutic Paxlovid. It is a reversible covalent inhibitor of SARS‐CoV‐2 main protease (M Pro ) that is effluxed from human cells by P‐glycoprotein (P‐gp). To identify NMV analogs with improved potency and reduced P‐gp efflux, a structure–activity relationship campaign was conducted. Warheads alternative to nitrile for engaging the active site cysteine were tested showing aldehyde and dichloroacetamide with better enzyme inhibition potency. Crystal structure of MPI‐136−M Pro shows its aldehyde warhead forming a thiohemiacetal with active Cys145 of M Pro . Several S4 binders were explored revealing that an O‐to‐S shift at the N ‐terminal amide leads to better enzyme inhibition. By exploring different combinations of S2, S3, and S4 binders, two inhibitors with better enzyme inhibition potency than NMV were found. Crystal structure of MPI‐148, with ( S )‐2‐azaspiro[4,5]decane‐3‐carboxylate as an alternative S2 binder, shows extensive hydrogen‐bond networks for locking the inhibitor in active site, explaining high affinity of NMV analogs. Further characterization of cellular M Pro engagement and antiviral potency against SARS‐CoV‐2 revealed four inhibitors with greater potency than NMV in P‐gp‐expressing cells. Studies with the P‐gp inhibitor CP‐100356 showed that these compounds were less sensitive to P‐gp inhibition than NMV, consistent with reduced P‐gp‐mediated efflux.

Alugubelli, Yugendar R. [Texas A&M Drug Discovery ↗
DOE OSTI2024

Redox dynamics and surface structures of an active palladium catalyst during methane oxidation

Catalysts based on palladium are among the most effective in the complete oxidation of methane. Despite extensive studies and notable advances, the nature of their catalytically active species and conceivable structural dynamics remains only partially understood. Here, we combine operando transmission electron microscopy (TEM) with near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT) calculations to investigate the active state and catalytic function of Pd nanoparticles (NPs) under methane oxidation conditions. We show that the particle size, phase composition and dynamics respond appreciably to changes in the gas-phase chemical potential. In combination with mass spectrometry (MS) conducted simultaneously with in situ observations, we uncover that the catalytically active state exhibits phase coexistence and oscillatory phase transitions between Pd and PdO. Aided by DFT calculations, we provide a rationale for the observed redox dynamics and demonstrate that the emergence of catalytic activity is related to the dynamic interplay between coexisting phases, with the resulting strained PdO having more favorable energetics for methane oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Chemical screening by time-resolved X-ray scattering to discover allosteric probes

Abstract Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis ( k VR ), capture structure–activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF–aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF–aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Sulfonamidoboronic Acids as “Cross-Class” Inhibitors of an Expanded-Spectrum Class C Cephalosporinase, ADC-33, and a Class D Carbapenemase, OXA-24/40: Strategic Compound Design to Combat Resistance in Acinetobacter baumannii

Acinetobacter baumannii is a Gram-negative organism listed as an urgent threat pathogen by the World Health Organization (WHO). Carbapenem-resistant A. baumannii (CRAB), especially, present therapeutic challenges due to complex mechanisms of resistance to β-lactams. One of the most important mechanisms is the production of β-lactamase enzymes capable of hydrolyzing β-lactam antibiotics. Co-expression of multiple classes of β-lactamases is present in CRAB; therefore, the design and synthesis of “cross-class” inhibitors is an important strategy to preserve the efficacy of currently available antibiotics. To identify new, nonclassical β-lactamase inhibitors, we previously identified a sulfonamidomethaneboronic acid CR167 active against Acinetobacter-derived class C β-lactamases (ADC-7). The compound demonstrated affinity for ADC-7 with a K i = 160 nM and proved to be able to decrease MIC values of ceftazidime and cefotaxime in different bacterial strains. Herein, we describe the activity of CR167 against other β-lactamases in A. baumannii: the cefepime-hydrolysing class C extended-spectrum β-lactamase (ESAC) ADC-33 and the carbapenem-hydrolyzing OXA-24/40 (class D). These investigations demonstrate CR167 as a valuable cross-class (C and D) inhibitor, and the paper describes our attempts to further improve its activity. Five chiral analogues of CR167 were rationally designed and synthesized. The structures of OXA-24/40 and ADC-33 in complex with CR167 and select chiral analogues were obtained. The structure activity relationships (SARs) are highlighted, offering insights into the main determinants for cross-class C/D inhibitors and impetus for novel drug design.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

In-situ and wavelength-dependent photocatalytic strain evolution of a single Au nanoparticle on a TiO 2 film

Photocatalysis is a promising technique due to its capacity to efficiently harvest solar energy and its potential to address the global energy crisis. However, the structure–activity relationships of photocatalyst during wavelength-dependent photocatalytic reactions remains largely unexplored because it is difficult to measure under operating conditions. Here we show the photocatalytic strain evolution of a single Au nanoparticle (AuNP) supported on a TiO 2 film by combining three-dimensional (3D) Bragg coherent X-ray diffraction imaging with an external light source. The wavelength-dependent generation of reactive oxygen species (ROS) has significant effects on the structural deformation of the AuNP, leading to its strain evolution. Density functional theory (DFT) calculations are employed to rationalize the induced strain caused by the adsorption of ROS on the AuNP surface. These observations provide insights of how the photocatalytic activity impacts on the structural deformation of AuNP, contributing to the general understanding of the atomic-level catalytic adsorption process.

14 SOLAR ENERGY↗
DOE OSTI2024

Recent Advances on Computational Modeling of Supported Single-Atom and Cluster Catalysts: Characterization, Catalyst–Support Interaction, and Active Site Heterogeneity

To satisfy the need for catalyst materials with high activity, selectivity, and stability for energy conversion, material design and discovery guided by theoretical insights are a necessity. In the past decades, the rise in theoretical investigations into the properties of catalyst materials, reaction mechanisms, and catalyst design principles has shed light on the catalysis field. Quantitative structure–activity relationships have been developed through incorporating spectroscopic simulations, electronic structure calculations, and reaction mechanistic studies. Here, in this review, we report the state-of-the-art computational approaches to catalyst materials characterization for supported single-atom and cluster catalysts utilizing spectroscopic simulations, i.e., XANES simulation, and material properties investigation via electronic-structure calculations. Furthermore, approaches regarding reaction mechanisms, focusing on active site heterogeneity, are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Enhanced Catalysis under 2D Silica: A CO Oxidation Study

Abstract Interfacially confined microenvironments have recently gained attention in catalysis, as they can be used to modulate reaction chemistry. The emergence of a 2D nanospace at the interface between a 2D material and its support can promote varying kinetic and energetic schemes based on molecular level confinement effects imposed in this reduced volume. We report on the use of a 2D oxide cover, bilayer silica, on catalytically active Pd(111) undergoing the CO oxidation reaction. We “uncover” mechanistic insights about the structure–activity relationship with and without a 2D silica overlayer using in situ IR and X‐ray spectroscopy and mass spectrometry methods. We find that the CO oxidation reaction on Pd(111) benefits from confinement effects imposed on surface adsorbates under 2D silica. This interaction results in a lower and more dispersed coverage of CO adsorbates with restricted CO adsorption geometries, which promote oxygen adsorption and lay the foundation for the formation of a reactive surface oxide that produces higher CO 2 formation rates than Pd alone.

Eads, Calley N.↗
DOE OSTI2021

Enhanced Catalysis under 2D Silica: A CO Oxidation Study

Interfacially confined microenvironments have recently gained attention in catalysis, as they can be used to modulate reaction chemistry. The emergence of a 2D nanospace at the interface between a 2D material and its support can promote varying kinetic and energetic schemes based on molecular level confinement effects imposed in this reduced volume. We report on the use of a 2D oxide cover, bilayer silica, on catalytically active Pd(111) undergoing the CO oxidation reaction. We “uncover” mechanistic insights about the structure-activity relationship with and without a 2D silica overlayer using in situ IR and X-ray spectroscopy and mass spectrometry methods. We find that the CO oxidation reaction on Pd(111) benefits from confinement effects imposed on surface adsorbates under 2D silica. Finally, this interaction results in a lower and more dispersed coverage of CO adsorbates with restricted CO adsorption geometries, which promote oxygen adsorption and lay the foundation for the formation of a reactive surface oxide that produces higher CO 2 formation rates than Pd alone.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A first principles analysis of potential-dependent structural evolution of active sites in Fe-N-C catalysts

Fe-N-C (iron–nitrogen–carbon) electrocatalysts have emerged as potential alternatives to precious metal-based materials for the oxygen reduction reaction (ORR). However, the structure of these materials under electrochemical conditions is not well understood, and their poor stability in acidic environments poses a formidable challenge for successful adoption in commercial fuel cells. To provide molecular-level insights into these complex phenomena, we combine periodic density functional theory (DFT) calculations, exhaustive treatment of coadsorption effects for ORR reaction intermediates, including O and OH, and comprehensive analysis of solvation stabilization effects to construct voltage-dependent ab initio thermodynamic phase diagrams that describe the in situ structure of the active sites. These structures are further linked to activity and stability descriptors that can be compared with experimental parameters such as the half-wave potential for ORR and the onset potential for carbon corrosion and CO 2 evolution. The results indicate that pyridinic Fe sites at zigzag carbon edges, as well as other edge sites, exhibit high activity for ORR compared to sites in the bulk. However, edges neighboring the active sites are prone to instability via overoxidation and consequent site loss. The results suggest that it could be beneficial to synthesize Fe-N-C catalysts with small sizes and large perimeter edge lengths to enhance ORR activity, while voltage fluctuations should be limited during fuel cell operation to prevent carbon corrosion of overoxidized edges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗