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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
DOE OSTI2020

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗
DOE OSTI2016

Discovery of Fe–Ce Oxide/BiVO 4 Photoanodes through Combinatorial Exploration of Ni–Fe–Co–Ce Oxide Coatings

An efficient photoanode is a prerequisite for a viable solar fuels technology. The challenges to realizing an efficient photoanode include the integration of a semiconductor light absorber and a metal oxide electrocatalyst to optimize corrosion protection, light trapping, hole transport, and photocarrier recombination sites. In order to efficiently explore metal oxide coatings, we employ a high throughput methodology wherein a uniform BiVO 4 film is coated with 858 unique metal oxide coatings covering a range of metal oxide loadings and the full (Ni-Fe-Co-Ce)O x pseudo-quaternary composition space. Photoelectrochemical characterization of the photoanodes reveals that specific combinations of metal oxide composition and loading provide up to a 13-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Through mining of the high throughput data we identify composition regions that form improved interfaces with BiVO 4 . Of particular note, integrated photoanodes with catalyst compositions in the range Fe (0.4-0.6) Ce (0.6-0.4) O x exhibit high interface quality and excellent photoelectrochemical power conversion. Furthermore, for scaled-up inkjet-printed electrodes and photoanodic electrodeposition of this composition on BiVO 4 we can confirm the discovery and the synthesis-independent interface improvement of (Fe-Ce)O x coatings on BiVO 4 .

14 SOLAR ENERGY↗
DOE OSTI2019

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Metal–oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal–oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure–property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal–oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4–5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal–oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a >37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.

transition metal catalysis↗
DOE OSTI2016

Live Cell Discovery of Microbial Vitamin Transport and Enzyme-Cofactor Interactions

The rapid completion of microbial genomes is inducing a conundrum in functional gene discovery. Novel methods are critically needed to shorten the gap between characterizing a microbial genome and experimentally validating bioinformatically-predicted functions. Of particular importance are transport mechanisms, used to shuttle nutrients and metabolites across cell mem-branes, such as B vitamins, which are indispensable to metabolic reactions crucial to the survival of diverse microbes ranging from members of environmental microbial communities to human pathogens. Methods to accurately assign function and specificity for a wide range of experimentally unidentified and/or predicted membrane-embedded transport proteins, and characterization of intra-cellular enzyme-cofactor/nutrient associations are needed to enable a significantly improved understanding of microbial biochemis-try and physiology, how microbes associate with others, and how they sense and respond to environmental perturbations. Chemical probes derived from B vitamins B1, B2, and B7 have allowed us to experimentally address the aforementioned needs by identifying B vitamin transporters and intracellular protein-cofactor associations through live cell labeling of the filamentous anoxygenic pho-toheterotroph, Chloroflexus aurantiacus J-10-fl, known for both B vitamin biosynthesis and environmental salvage. Our probes provide a unique opportunity to directly link cellular activity and protein function back to ecosystem and/or host dynamics by iden-tifying B vitamin transport and disposition mechanisms required for survival.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Discovery of a Selective Covalent Inhibitor of Lysophospholipase-like 1 (LYPLAL1) as a Tool to Evaluate the Role of this Serine Hydrolase in Metabolism

Lysophospholipase-like 1 (LYPLAL1) is an uncharacterized metabolic serine hydrolase. Human genome-wide association studies link variants of the gene encoding this enzyme to fat distribution, waist-to-hip ratio, and nonalcoholic fatty liver disease. We describe the discovery of potent and selective covalent small-molecule inhibitors of LYPLAL1 and their use to investigate its role in hepatic metabolism. In hepatocytes, selective inhibition of LYPLAL1 increased glucose production supporting the inference that LYPLAL1 is a significant actor in hepatic metabolism. The results provide an example of how a selective chemical tool can contribute to evaluating a hypothetical target for therapeutic intervention, even in the absence of complete biochemical characterization.

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

Discovery of Gold Nanoparticles in Marcellus Shale

A high density of gold (Au) nanoparticles has been observed on the surfaces of the coexisting opal nanospheres in kerogen-bearing shales from the Marcellus Formation. Our analyses of the Au nanoparticles and associated minerals indicate that this represents a new formation type of colloidal gold nanoparticles. Additionally, the opal nanospheres are a new kind of natural opal-A that is characteristic of the mesoporous texture formed through oil-in-water emulsion processes. High-resolution transmission electron microscopy (TEM) combined with energy-dispersive X-ray spectroscopy analysis directly reveals spherical gold nanoparticles and gold nanorods associated with mesoporous opal nanospheres. Our newly found textures indicate that opal nanospheres may have facilitated the co-precipitation of pure gold nanoparticles in organic-bearing, reduced environments. Furthermore, our TEM observations provide distinct images of the initial phase of colloidal gold and silica, which may contribute to the formation of Au deposits involving "invisible" gold, secondary supergene enrichments, or high-grade gold accumulations. Finally, the discovery of Au nanoparticles in hydrocarbon-rich shale formations such as Marcellus (which contains 0.6-4.1 wt % gold/opal) suggests a potential for co-production of both gas/oil and gold-two valuable commodities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Discovery of Manganese-Based Solar Fuel Photoanodes via Integration of Electronic Structure Calculations, Pourbaix Stability Modeling, and High-Throughput Experiments

The solar photoelectrochemical generation of hydrogen and carbon-containing fuels comprises a critical energy technology for establishing sustainable energy resources. The photoanode, which is responsible for solar-driven oxygen evolution, has persistently limited technology advancement due to the lack of materials that exhibit both the requisite electronic properties and operational stability. Efforts to extend the lifetime of solar fuel devices increasingly focus on mitigating corrosion in the highly oxidizing oxygen evolution environment, motivating our development of a photoanode discovery pipeline that combines electronic structure calculations, Pourbaix stability screening, and high-throughput experiments. By applying the pipeline to ternary metal oxides containing manganese, we have identified a promising class of corrosion-resistant materials and discover five oxygen evolution photoanodes, including the first demonstration of photoelectrocatalysis with Mn-based ternary oxides and the introduction of alkaline earth manganates as promising photoanodes for establishing a durable solar fuels technology.

10 SYNTHETIC FUELS↗
DOE OSTI2015

Discovery of a Potent Parenterally Administered Factor XIa Inhibitor with Hydroxyquinolin-2(1 H )-one as the P2' Moiety

Structure–activity relationship optimization of phenylalanine P1' and P2' regions with a phenylimidazole core resulted in a series of potent FXIa inhibitors. Introducing 4-hydroxyquinolin-2-one as the P2' group enhanced FXIa affinity and metabolic stability. Incorporation of an N-methyl piperazine amide group to replace the phenylalanine improved both FXIa potency and aqueous solubility. Combination of the optimization led to the discovery of FXIa inhibitor 13 with a FXIa K i of 0.04 nM and an aPTT EC2x of 1.0 μM. Dose-dependent efficacy (EC 50 of 0.53 μM) was achieved in the rabbit ECAT model with minimal bleeding time prolongation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Discovery of Novel Tricyclic Heterocycles as Potent and Selective DPP-4 Inhibitors for the Treatment of Type 2 Diabetes

In our efforts to develop second generation DPP-4 inhibitors, we endeavored to identify distinct structures with long-acting (once weekly) potential. Taking advantage of X-ray cocrystal structures of sitagliptin and other DPP-4 inhibitors, such as alogliptin and linagliptin bound to DPP-4, and aided by molecular modeling, we designed several series of heterocyclic compounds as initial targets. During their synthesis, an unexpected chemical transformation provided a novel tricyclic scaffold that was beyond our original design. Capitalizing on this serendipitous discovery, we have elaborated this scaffold into a very potent and selective DPP-4 inhibitor lead series, as highlighted by compound 17c.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Discovery of Stable and Selective Antibody Mimetics from Combinatorial Libraries of Polyvalent, Loop-Functionalized Peptoid Nanosheets

The ability of antibodies to bind a wide variety of analytes with high specificity and high affinity makes them ideal candidates for therapeutic and diagnostic applications. However, the poor stability and high production cost of antibodies have prompted exploration of a variety of synthetic materials capable of specific molecular recognition. Unfortunately, it remains a fundamental challenge to create a chemically diverse population of protein-like, folded synthetic nanostructures with defined molecular conformations in water. Here we report the synthesis and screening of combinatorial libraries of sequence-defined peptoid polymers engineered to fold into ordered, supramolecular nanosheets displaying a high spatial density of diverse, conformationally constrained peptoid loops on their surface. These polyvalent, loop-functionalized nanosheets were screened using a homogeneous Förster resonance energy transfer (FRET) assay for binding to a variety of protein targets. Peptoid sequences were identified that bound to the heptameric protein, anthrax protective antigen, with high avidity and selectivity. These nanosheets were shown to be resistant to proteolytic degradation, and the binding was shown to be dependent on the loop display density. This work demonstrates that key aspects of antibody structure and function-the creation of multivalent, combinatorial chemical diversity within a well-defined folded structure-can be realized with completely synthetic materials. This approach enables the rapid discovery of biomimetic affinity reagents that combine the durability of synthetic materials with the specificity of biomolecular materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. Here in this paper, we describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of the library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of D-Ala-D-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

A Fluorescence Displacement Assay for Antidepressant Drug Discovery Based on Ligand-Conjugated Quantum Dots

The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) protein plays a central role in terminating 5-HT neurotransmission and is the most important therapeutic target for the treatment of major depression and anxiety disorders. We report an innovative, versatile, and target-selective quantum dot (QD) labeling approach for SERT in single Xenopus oocytes that can be adopted as a drug-screening platform. Our labeling approach employs a custom-made, QD-tagged indoleamine derivative ligand, IDT318, that is structurally similar to 5-HT and accesses the primary binding site with enhanced human SERT selectivity. Incubating QD-labeled oocytes with paroxetine (Paxil), a high-affinity SERT-specific inhibitor, showed a concentration- and time-dependent decrease in QD fluorescence, demonstrating the utility of our approach for the identification of SERT modulators. Furthermore, with the development of ligands aimed at other pharmacologically relevant targets, our approach may potentially form the basis for a multitarget drug discovery platform.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

In Silico Discovery of Multistep Chemistry Initiated by a Conical Intersection: The Challenging Case of Donor–Acceptor Stenhouse Adducts

Detailed mechanistic understanding of multistep chemical reactions triggered by internal conversion via a conical intersection is a challenging task that emphasizes limitations in theoretical and experimental techniques. We present a discovery-based, hypothesis-free computational approach based on first-principles molecular dynamics to discover and refine the switching mechanism of donor–acceptor Stenhouse adducts (DASAs). We simulate the photochemical experiment in silico, following the “hot” ground state dynamics for 10 ps after photoexcitation. Using state-of-the-art graphical processing units-enabled electronic structure calculations we performed in total ~2 ns of nonadiabatic ab initio molecular dynamics discovering (a) critical intermediates that are involved in the open-to-closed transformation, (b) several competing pathways which lower the overall switching yield, and (c) key elements for future design strategies. Finally, our dynamics describe the natural evolution of both the nuclear and electronic degrees of freedom that govern the interconversion between DASA ground-state intermediates, exposing significant elements for future design strategies of molecular switches.

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

Bioinformatic Discovery of a Cambialistic Monooxygenase

Dinuclear monooxygenases mediate challenging C–H bond oxidation reactions throughout nature. Many of these enzymes are presumed to exclusively utilize diiron cofactors. Herein we report the bioinformatic discovery of an orphan dinuclear monooxygenase that preferentially utilizes a heterobimetallic manganese–iron (Mn/Fe) cofactor to mediate an O 2 -dependent C–H bond hydroxylation reaction. Unlike the structurally similar Mn/Fe-dependent monooxygenase AibH2, the diiron form of this enzyme (SfbO) exhibits a nascent enzymatic activity. This behavior raises the possibility that many other dinuclear monooxygenases may be endowed with the capacity to harness cofactors with a variable metal content.

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

Reaction Discovery Using Spectroscopic Insights from an Enzymatic C–H Amination Intermediate

Engineered hemoproteins can selectively incorporate nitrogen from nitrene precursors like hydroxylamine, O-substituted hydroxylamines, and organic azides into organic molecules. Although iron-nitrenoids are often invoked as the reactive intermediates in these reactions, their innate reactivity and transient nature have made their characterization challenging. Here we characterize an iron-nitrosyl intermediate generated from NH 2 OH within a protoglobin active site that can undergo nitrogen-group transfer catalysis, using UV–vis, electron paramagnetic resonance (EPR) spectroscopy, and high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) techniques. The mechanistic insights gained led to the discovery of aminating reagents—nitrite (NO 2 – ), nitric oxide (NO), and nitroxyl (HNO)—that are new to both nature and synthetic chemistry. Based on the findings, we propose a catalytic cycle for C–H amination inspired by the nitrite reductase pathway. Furthermore, this study highlights the potential of engineered hemoproteins to access natural nitrogen sources for sustainable chemical synthesis and offers a new perspective on the use of biological nitrogen cycle intermediates in biocatalysis.

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

A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction–diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., T max ). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model’s potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

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

Discovery of an Interlocked and Interwoven Molecular Topology in Nanocarbons via Dynamic C–C Bond Formation

Topologically complex carbon nanostructures are an exciting but largely unexplored class of materials due to their challenging synthesis. Previous methods are low yielding because they rely on irreversible C sp 2 −C sp 2 bond formation, which necessitates complex templating strategies to enforce entanglement. Here, reversible zirconocene coupling of alkynes is developed as a new method to access complex molecular topologies, where dynamic C−C bond formation facilitates entanglement under thermodynamic control, allowing the use of simple precursors without the need for preassembly. This strategy enables the scalable, high-yield synthesis of three topologically distinct nanocarbons, including the serendipitous discovery of a structure containing a new topological motif that was not previously identified or realized synthetically. This motif, consisting of an unusual combination of interlocking and interweaving, was recognized to be generalizable to a new topological class of molecules, introduced here as perplexanes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗