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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Thermally Stable Co@C3N4 Single-Atom Catalysts for CO Oxidation: Atomic-Level Insights into Structure and Activity

Single Co atoms supported on C3N4 (Co@C3N4) have demonstrated high activity and selectivity in photocatalysis. However, the investigation of structure–function relationships and reaction mechanisms under photocatalytic conditions is very challenging due to the complex conditions of light absorption, charge transfer, and catalysis. In this study, we employed thermal CO oxidation as a prototypical probe reaction to benchmark the intrinsic catalytic performance and track the active-site evolution of Co@C3N4. Single Co atoms were identified and shown to be the catalytically active sites for CO oxidation based on control experiments and isotope-labeling experiments. The Co sites remained atomically dispersed before, during, and after the reaction with temperatures up to 400 °C, as established by in situ X-ray absorption fine structure (XAFS) combined with density functional theory (DFT), FDMNES simulations, and dynamic-time-warping (DTW)-assisted X-ray absorption near edge structure (XANES) matching. Together with theoretical calculations, the integrated analysis reveals a stable coordination environment under reaction conditions, which correlates with sustained activity, establishing Co@C3N4 single-atom catalysts as thermally stable CO oxidation catalysts. Beyond these findings, the current study provides a workflow for unambiguously assigning active sites in Co@C3N4 for thermal CO oxidation. This workflow will aid the understanding of their behavior in photocatalysis in the future, where light-driven dynamics obscure direct structure–function links. Notably, this study provides fundamental insights for the rational design of robust single-atom catalysts and a foundation for the broader application of Co@C3N4 catalysts in oxidation reactions.

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

Structure and Function of BorB, the Type II Thioesterase from the Borrelidin Biosynthetic Gene Cluster

α/β hydrolases make up a large and diverse protein superfamily. In natural product biosynthesis, cis-acting thioesterase α/β hydrolases can terminate biosynthetic assembly lines and release products by hydrolyzing or cyclizing the biosynthetic intermediate. Thioesterases can also act in trans, removing aberrant intermediates and restarting stalled biosynthesis. Knockout of this "editing" function leads to reduced product titers. The borrelidin biosynthetic gene cluster from Streptomyces parvulus Tü4055 contains a hitherto uncharacterized stand-alone thioesterase, borB. In this work, we demonstrate that purified BorB cleaves acyl substrates with a preference for propionate, which supports the hypothesis that it is also an editing thioesterase. The crystal structure of BorB shows a wedgelike hydrophobic substrate binding crevice that limits substrate length. To investigate the structure-function relationship, we made chimeric BorB variants using loop regions from characterized homologues with different specificities. BorB chimeras slightly reduced activity, arguing that the modified region is a not major determinant of substrate preference. Overall, the structure-function relationships described here contribute to the process of elimination for understanding thioesterase specificity and, ultimately, engineering and applying trans-acting thioesterases in biosynthetic assembly lines.

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

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Machine-Learned Structure-Property Relationship in Metallic Glasses (Final Report)

The goal of the proposed work was to gain the ability to predict properties from the known coordinates (relative positions) of atoms in a metallic glass, presuming that information about which regions of the glass are fertile for rearrangment (defects) are encoded in this structural information. This goal is important to the science of metallic glasses. This research of applied machine learning methods to automate the development of a relationship between structural measures and thermally and stress-activated events in the metallic glass. In this way, the work aimed to building a bridge between structure and properties for this class of materials. The work is expected to have timely impact on guiding the tuning of internal structures of metallic glasses for desired properties. It also represents an advance in applying machine learning techniques to discerning the properties of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of the Molecular Structure of Surface Vanadia on Activity and Regenerability of VO $x$ /In 2 O 3 Catalysts for CO 2 -Assisted Oxidative Dehydrogenation of Propane

Our recent work has reported that higher propylene selectivity and improved stability can be achieved by combining redox-active VO $x$ and basic In 2 O 3 for CO 2 -assisted oxidative dehydrogenation of propane (CO 2 -ODHP). In the present work, we continued to explore the stability and regenerability of V/In catalysts. In particular, our interest lies in identifying the effect of mono- and polyvanadate on catalytic performance and regenerability. A V/In catalyst with an increased proportion of monovanadate was prepared using the Schlenk line under moisture-free conditions (V/In–S), while the fully polymerized vanadate catalyst was prepared through a regular impregnation (V/In) for comparison. The Schlenk-line-prepared catalyst, namely, V/In–S, not only exhibits a 17–30% enhanced propylene yield at high temperatures (500–540 °C) over V/In but also presents improved stability and regenerability with nearly 88% activity recovered after regeneration in O 2 . Detailed characterizations have been performed to reveal the catalyst structure–performance relationship, including chemisorption (NH 3 /CO 2 -temperature-programmed desorption, NH 3 /CO 2 -TPD), H 2 -temperature-programmed reduction (H 2 -TPR), and spectroscopic studies [Raman spectroscopy, UV–vis diffuse reflectance spectroscopy (UV–vis DRS), near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS), and high-sensitivity low-energy ion scattering (HS-LEIS)]. Characterization results demonstrate that compared with polyvanadates, monovanadates lead to strengthened interaction with In 2 O 3 and a more stabilized V/In surface and subsurface, as well as improved redox properties of VO $x$ . These advantages give rise to the observed enhancement in activity, stability, and regenerability. In conclusion, these findings advance the understanding of the relationship between the activity/stability and the molecular structure of surface oxide species (vanadia) and the interplay between acid–base interactions and redox properties of mixed metal-oxide catalysts for efficient CO 2 -ODHP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Transactional Knowledge Graph Generation To Model Adversarial Activities

A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. Traditionally, KGs are used to describe metadata about entities and to provide additional context to target application results. Many real-world domains also involve temporal interactions between entities in addition to the metadata data. Modeling these attributed transactions is a critical requirement when using KGs in complex real-world applications. Modeling adversarial activities is one such application that develops methodology and tools to produce realistic large-scale background activity graphs that include embedded Weapons of Mass Destruction (WMD) activity patterns. We present a novel platform for constructing a transactional knowledge graph from a diverse set of sources. We present the core components and architecture of the framework, and a use case for generating a background knowledge graph and WMD activity template to evaluate network alignment and subgraph matching algorithms.

Purohit, Sumit↗
DOE OSTI2022

High‐Field NMR, Reactivity, and DFT Modeling Reveal the γ‐Al 2 O 3 Surface Hydroxyl Network**

Abstract Aluminas are strategic materials used in many major industrial processes, either as catalyst supports or as catalysts in their own right. The transition alumina γ‐Al 2 O 3 is a privileged support, whose reactivity can be tuned by thermal activation. This study provides a qualitative and quantitative assessment of the hydroxyl groups present on the surface of γ‐Al 2 O 3 at three different dehydroxylation temperatures. The principal [AlOH] configurations are identified and described in unprecedented detail at the molecular level. The structures were established by combining information from high‐field 1 H and 27 Al solid‐state NMR, IR spectroscopy and DFT calculations, as well as selective reactivity studies. Finally, the relationship between the hydroxyl structures and the molecular‐level structures of the active sites in catalytic alkane metathesis is discussed.

Merle, Nicolas↗
DOE OSTI2022

High‐Field NMR, Reactivity, and DFT Modeling Reveal the γ‐Al 2 O 3 Surface Hydroxyl Network**

Abstract Aluminas are strategic materials used in many major industrial processes, either as catalyst supports or as catalysts in their own right. The transition alumina γ‐Al 2 O 3 is a privileged support, whose reactivity can be tuned by thermal activation. This study provides a qualitative and quantitative assessment of the hydroxyl groups present on the surface of γ‐Al 2 O 3 at three different dehydroxylation temperatures. The principal [AlOH] configurations are identified and described in unprecedented detail at the molecular level. The structures were established by combining information from high‐field 1 H and 27 Al solid‐state NMR, IR spectroscopy and DFT calculations, as well as selective reactivity studies. Finally, the relationship between the hydroxyl structures and the molecular‐level structures of the active sites in catalytic alkane metathesis is discussed.

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

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗
DOE OSTI2021

A measure of active interfaces in supported catalysts for high-temperature reactions

Formulating knowledge of structure-function relationships in heterogeneous catalysis is central to the design of efficient catalysts; yet, the elucidation of dominant reaction sites has remained as a challenge. Here, in this paper, we present a methodology that can be used to visualize metal-gas and metal-oxide-gas interfaces in three dimensions and to quantify their catalytic activity levels. As a case study, CH 4 oxidation occurring in a Pt/CeO 2 system is chosen. By employing thermally robust Pt@CeO 2 model catalysts with size-tunable and monodisperse cores, and gas-permeable shells, we reconstruct a series of structures in 3D via electron tomography and match the information to activity data and theoretical calculations. This strategy reveals that two different interfaces catalyze the CH 4 oxidation and that their contribution to the overall rate changes with the Pt size, temperature, and gas atmosphere. Our results provide an analytic platform on which to explore reaction pathways and mechanisms applicable to multiple reactions and materials.

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

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Revealing Structural Evolution of Single Atom Catalysts during Electrochemical CO 2 Reduction by in Situ X-ray Absorption Spectroscopy

Investigating the structural variation of single-atom catalysts (SACs) is crucial to reveal the reaction mechanism under working conditions. Different in situ techniques, especially X-ray absorption spectroscopy (XAS), have been reported to study the structural changes in active sites. However, a systematic study of the relationship between the coordination environment and the catalytic ability of different kinds of SACs is still lacking. Herein, we established isolated transition metal atoms (Fe, Co, Ni, Cu) on N-doped carbon (M-N-C) and employed them in electrocatalytic CO 2 reduction reaction (CO 2 RR). Significantly, Ni-N-C exhibits the highest selectivity (similar to 97.9%) toward CO at -0.8 V vs RHE. In situ XAS characterization discloses the coordination number dependent catalytic performance. Further, the decreased average coordination number of Ni in Ni-N-C at the voltage point with maximum Faradaic efficiency was observed. Density functional theory further provides the possible mechanism of CO 2 -to-CO over the undercoordinated Ni-N-C structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Quantifying the Influences of Epoxide Binding in Epoxide/CO 2 Ring Opening Copolymerization Catalysis

Understanding and predicting the effect of epoxide structure on the rate of polymerization in epoxide/CO 2 ring opening copolymerization catalysis is a long-standing challenge. Here, a known highly active Co(III)K(I) catalyst is used to investigate the influences of six different epoxides' binding strengths on their rates of copolymerization. Since calculations and experiments indicate that studying the catalytically relevant Co(III)−epoxide adduct directly is experimentally challenging, epoxide−catalyst binding interactions are quantified using a Co(II)K(I) complex to model the key catalytic intermediate. Epoxide−catalyst coordination is investigated using UV−vis spectroscopy titrations which provide fast and effective determination of association or binding constants. The epoxide−catalyst equilibrium constants show a clear exponential correlation with copolymerization rates and a new catalyst performance linear free energy relationship is revealed. Epoxides exhibiting stronger catalyst binding constants show higher copolymerization rates. The structure−activity correlation is consistent with the polymerization kinetics, mechanism and DFT calculations. Both the methods to investigate epoxide−catalyst coordination and the linear free energy relationship are shown to apply to the series of six epoxides and a second Co(III)K(I) catalyst. These structure− performance relationships are likely applicable to other transition metal catalysts and should expedite future epoxide and catalyst selection to make useful poly(carbonate) materials.

catalysts↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

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

Carotenoid cleavage enzymes evolved convergently to generate the visual chromophore

The retinal light response in animals originates from the photoisomerization of an opsin-coupled 11-cis-retinaldehyde chromophore. This visual chromophore is enzymatically produced through the action of carotenoid cleavage dioxygenases. Vertebrates require two carotenoid cleavage dioxygenases, β-carotene oxygenase 1 and retinal pigment epithelium 65 (RPE65), to form 11-cis-retinaldehyde from carotenoid substrates, whereas invertebrates such as insects use a single enzyme known as Neither Inactivation Nor Afterpotential B (NinaB). RPE65 and NinaB couple trans–cis isomerization with hydrolysis and oxygenation, respectively, but the mechanistic relationship of their isomerase activities remains unknown. Here we report the structure of NinaB, revealing details of its active site architecture and mode of membrane binding. Structure-guided mutagenesis studies identify a residue cluster deep within the NinaB substrate-binding cleft that controls its isomerization activity. Our data demonstrate that isomerization activity is mediated by distinct active site regions in NinaB and RPE65—an evolutionary convergence that deepens our understanding of visual system diversity.

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

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2020

Synthesis, aphicidal activity and conformation of novel insect kinin analogues as potential eco‐friendly insecticides

Abstract BACKGROUND The discovery of ecofriendly insecticides through a new strategy for aphid control is important because of the substantial resistance and unexpected eco‐toxicity to honeybees caused by traditional insecticides. The insect kinins, a class of multifunctional insect neuropeptides, are considered for potential application in pest control. In our previous work we developed several series of insect kinin analogues and found a promising lead II‐1 with good aphicidal activity. To seek further eco‐friendly aphicides, the optimization of II‐1 is carried out in this study. RESULTS Fifteen novel Yaa 3 modified analogues based on the lead II‐1 were synthesized. The aphicidal tests indicated that IV‐3 , IV‐5 and IV‐10 exhibited significant activity against the soybean aphid Aphis glycines with LC 50 values of 0.0029, 0.0072 and 0.0086 mmol L –1 , respectively, higher than that of lead II‐1 and the commercial Pymetrozine. The molecular modeling results showed that analogues II‐1 , IV‐3 , IV‐5 , IV‐7 and IV‐10 formed a β‐turn‐like conformation, while the conformation of analogues IV‐1 , IV‐2 and IV‐9 seemed to be linear. Some structural elements favorable for the activity were proposed based on the conformation‐activity relationship of the analogues. CONCLUSION Insect kinin analogues derived from lead II‐1 by modifying the hydrolysis site Yaa 3 with natural, sterically hindered α‐ and β‐amino acids showed great potential as eco‐friendly insecticides. Inspiringly, the most active analogue IV‐3 can be a candidate for further development. The β‐turn‐like conformation and the orientation of the aromatic rings of the side chain of Phe 2 and Trp 4 may be critical factors beneficial to activity. © 2019 Society of Chemical Industry

Zhang, Chuanliang↗