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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

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

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chemical composition of material extractives influences microbial growth and dynamics on wetted wood materials

The impact of material chemical composition on microbial growth on building materials remains relatively poorly understood. We investigate the influence of the chemical composition of material extractives on microbial growth and community dynamics on 30 different wood species that were naturally inoculated, wetted, and held at high humidity for several weeks. Microbial growth was assessed by visual assessment and molecular sequencing. Unwetted material powders and microbial swab samples were analyzed using reverse phase liquid chromatography with tandem mass spectrometry. Different wood species demonstrated varying susceptibility to microbial growth after 3 weeks and visible coverage and fungal qPCR concentrations were correlated (R 2 = 0.55). Aspergillaceae was most abundant across all samples; Meruliaceae was more prevalent on 8 materials with the highest visible microbial growth. A larger and more diverse set of compounds was detected from the wood shavings compared to the microbial swabs, indicating a complex and heterogeneous chemical composition within wood types. Several individual compounds putatively identified in wood samples showed statistically significant, near-monotonic associations with microbial growth, including C 11 H 16 O 4 , C 18 H 34 O 4 , and C 6 H 15 NO. A pilot experiment confirmed the inhibitory effects of dosing a sample of wood materials with varying concentrations of liquid C 6 H 15 NO (assuming it presented as Diethylethanolamine).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Abstract Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Electrochemical lithium extraction from hectorite ore

Electrochemical technologies add a unique dimension for ore refinement, representing tunable methods that can integrate with renewable energy sources and existing downstream process flows. However, the development of electrochemical extraction technologies has been impeded by the technological maturity of hydro- and pyro-metallurgy, as well as the electrical insulating properties of many metal oxide ores. The fabrication and use of carbon/insulating material composite electrodes has been a longstanding method to enable electrochemical activation. Here, using real hectorite ore, we employ this technical approach to fabricate hectorite-carbon black composite electrodes (HCCEs) and achieve electrochemical activation of hectorite. Anodic polarization results in lithium-ion release through a multi-step chemical and electrochemical mechanism that results in 50.7 ± 4.4% removal of lithium from HCCE, alongside other alkaline ions. This technical proof-of-concept study underscores that electrochemical activation of ores can facilitate lattice deterioration and ion removal from ores.

25 ENERGY STORAGE↗
DOE OSTI2025

Navigating the membrane maze for lithium extraction

Here, research now demonstrates that solution-processable polymer membranes with hydrophilic subnanometre pores can selectively extract lithium ions from salt-lake brines, offering a potentially sustainable approach to meet the growing demand for lithium in energy storage applications.

Darling, Seth B. [Argonne National Laboratory (ANL↗
DOE OSTI2020

Graphene-extracted membrane lipids facilitate the activation of integrin α v β 8

Despite the remarkable electrochemical properties of graphene, strong van der Waals attraction between graphene and biomolecules often causes cytotoxicity, which hinders its applications in the biomedical field. Unfortunately, surface passivation of graphene might stimulate undesired immune response as the nanomaterial triggers cytokine production through membrane receptor activation. In this work, we use all-atom Molecular Dynamics (MD) simulations to unravel the underlying mechanism of graphene-induced inside-out activation of integrin αvβ8, a prominent membrane receptor expressed in immune cells. We model the transmembrane (TM) domains of integrin αvβ8 in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer and observe the structural changes in the integrin–membrane complex when interacting with a graphene nanosheet across the membrane. We find that the β8 TM domain interacts with the graphene nanosheet directly or indirectly through extracted lipids, facilitating the pulling of a β8 subunit away from an αv subunit and thus leading to the disruption of the TM domain association by breaking the hydrophobic cluster in the cytoplasmic domains of the αv and β8 subunits. Alanine substitution of two conserved phenylalanine residues on the αv subunit at this hydrophobic cluster further reveals the importance of a stable T-shaped structure in retaining integrin in its inactive state. Our results agree with previous studies on the interactions between other integrin subtypes and their endogenous activators, suggesting an intriguing role that the graphene nanosheet may play in the integrin-related signal transduction during its interaction with the membrane.

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

Using resonant energy X-ray diffraction to extract chemical order parameters in ternary semiconductors

II–IV–V 2 materials, ternary analogs to III–V materials, are emerging for their potential applications in devices such as LEDs and solar cells. Controlling cation ordering in II–IV–V 2 materials offers the potential to tune properties at nearly fixed compositions and lattice parameters. While tuning properties at a fixed lattice constant through ordering has the potential to be a powerful tool used in device fabrication, cation ordering also creates challenges with characterization and quantification of ordering. In this work, we investigate two different methods to quantify cation ordering in ZnGeP 2 thin films: a stretching parameter calculated from lattice constants , and an order parameter determined from the cation site occupancies ( S ). We use high resolution X-ray diffraction (HRXRD) to determine and resonant energy X-ray diffraction (REXD) to extract S . REXD is critical to distinguish between elements with similar Z -number ( e.g. Zn and Ge). We found that samples with a corresponding to the ordered chalcopyrite structure had only partially ordered S values. The optical absorption onset for these films occurred at lower energy than expected for fully ordered ZnGeP 2 , indicating that S is a more accurate descriptor of cation order than the stretching parameter. Since disorder is complex and can occur on many length scales, metrics for quantifying disorder should be chosen that most accurately reflect the physical properties of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An exact inversion method for extracting orientation ordering by small-angle scattering

Here, we outline a nonparametric inversion strategy for determining the orientation distribution function (ODF) of sheared interacting rods using small-angle scattering techniques. With the presence of direct inter-rod interaction and fluid mechanical forces, the scattering spectra are no longer characterized by the azimuthal symmetry in the coordinates defined by the principal directions of simple shear conditions, which severely compounds the reconstruction of ODFs based on currently available methods developed for dilute systems. Using a real spherical harmonic expansion scheme, the real-space ODFs are uniquely determined from the anisotropic scattering spectra and their numerical accuracy is verified computationally. Our method can be generalized to extract ODFs of uniaxially anisotropic objects under different flow conditions in a properly transformed reference frame with suitable basis vectors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

36 MATERIALS SCIENCE↗