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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-driven computational prediction and experimental realization of exotic perovskite-related polar magnets

Rational design of technologically important exotic perovskites is hampered by the insufficient geometrical descriptors and costly and extremely high-pressure synthesis, while the big-data driven compositional identification and precise prediction entangles full understanding of the possible polymorphs and complicated multidimensional calculations of the chemical and thermodynamic parameter space. Here we present a rapid systematic data-mining-driven approach to design exotic perovskites in a high-throughput and discovery speed of the A 2 BB ’O 6 family as exemplified in A 3 TeO 6 . The magnetoelectric polar magnet Co 3 TeO 6 , which is theoretically recognized and experimentally realized at 5 GPa from the six possible polymorphs, undergoes two magnetic transitions at 24 and 58 K and exhibits helical spin structure accompanied by magnetoelastic and magnetoelectric coupling. We expect the applied approach will accelerate the systematic and rapid discovery of new exotic perovskites in a high-throughput manner and can be extended to arbitrary applications in other families.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stereodivergent photobiocatalytic radical cyclization through the repurposing and directed evolution of fatty acid photodecarboxylases

Despite their intriguing photophysical and photochemical activities, naturally occurring photoenzymes have not yet been repurposed for new-to-nature activities. Here we engineered fatty acid photodecarboxylases (FAPs) to catalyse unnatural photoredox radical C–C bond formation by leveraging strongly oxidizing excited-state flavoquinone cofactor. Through genome mining, rational engineering and directed evolution, we developed a panel of radical photocyclases (RAPs) to facilitate decarboxylative radical cyclisation with excellent chemo-, enantio-, and diastereoselectivities. Our high-throughput experimental workflow allowed for the directed evolution of FAPs. An orthogonal set of RAPs was engineered to access all four possible stereoisomers of stereochemical dyad, affording fully diastereo- and enantiodivergent biotransformations in asymmetric radical biocatalysis. Molecular dynamics simulations show that our evolved RAPs allow near-attack conformations to be easily accessed, enabling chemoselective radical cyclisation. The development of stereoselective RAPs provides unnatural C–C bond forming activities in natural photoenzyme families, which can be used to tame the stereochemistry of free radical-mediated reactions.

Ju, Shuyun (ORCID:000000020322104X)↗
DOE OSTI2019

Data-driven design of metal–organic frameworks for wet flue gas CO 2 capture

Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2 -binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2 /N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

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

Coherent electrical control of a single high-spin nucleus in silicon

Nuclear spins are highly coherent quantum objects. In large ensembles, their control and detection via magnetic resonance is widely exploited, for example, in chemistry, medicine, materials science and mining. Nuclear spins also featured in early proposals for solid-state quantum computers and demonstrations of quantum search and factoring algorithms. Scaling up such concepts requires controlling individual nuclei, which can be detected when coupled to an electron. However, the need to address the nuclei via oscillating magnetic fields complicates their integration in multi-spin nanoscale devices, because the field cannot be localized or screened. Control via electric fields would resolve this problem, but previous methods relied on transducing electric signals into magnetic fields via the electron–nuclear hyperfine interaction, which severely affects nuclear coherence. In this work, we demonstrate the coherent quantum control of a single 123 Sb (spin-7/2) nucleus using localized electric fields produced within a silicon nanoelectronic device. The method exploits an idea proposed in 1961 but not previously realized experimentally with a single nucleus. Our results are quantitatively supported by a microscopic theoretical model that reveals how the purely electrical modulation of the nuclear electric quadrupole interaction results in coherent nuclear spin transitions that are uniquely addressable owing to lattice strain. The spin dephasing time, 0.1 seconds, is orders of magnitude longer than those obtained by methods that require a coupled electron spin to achieve electrical driving. These results show that high-spin quadrupolar nuclei could be deployed as chaotic models, strain sensors and hybrid spin-mechanical quantum systems using all-electrical controls. Integrating electrically controllable nuclei with quantum dots could pave the way to scalable, nuclear- and electron-spin-based quantum computers in silicon that operate without the need for oscillating magnetic fields.

42 ENGINEERING↗
DOE OSTI2020

The changing mouse embryo transcriptome at whole tissue and single-cell resolution

During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system 1 . Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis -element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A trade-off between plant and soil carbon storage under elevated CO 2

Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO 2 ) emitted by human activities each year, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO 2 . Although plant biomass often increases in elevated CO 2 (eCO 2 ) experiments, SOC has been observed to increase, remain unchanged or even decline. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections. In this study we synthesized data from 108 eCO 2 experiments and found that the effect of eCO 2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO 2 , SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO 2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Polypeptide organic radical batteries

In only a few decades, lithium-ion batteries have revolutionized technologies, enabling the proliferation of portable devices and electric vehicles, with substantial benefits for society. However, the rapid growth in technology has highlighted the ethical and environmental challenges of mining lithium, cobalt and other mineral ore resources, and the issues associated with the safe usage and non-hazardous disposal of batteries. Only a small fraction of lithium-ion batteries are recycled, further exacerbating global material supply of strategic elements. A potential alternative is to use organic-based redox-active materials to develop rechargeable batteries that originate from ethically sourced, sustainable materials and enable on-demand deconstruction and reconstruction. Making such batteries is challenging because the active materials must be stable during operation but degradable at end of life. Further, the degradation products should be either environmentally benign or recyclable for reconstruction into a new battery. Here we demonstrate a metal-free, polypeptide-based battery, in which viologens and nitroxide radicals are incorporated as redox-active groups along polypeptide backbones to function as anode and cathode materials, respectively. These redox-active polypeptides perform as active materials that are stable during battery operation and subsequently degrade on demand in acidic conditions to generate amino acids, other building blocks and degradation products. Here, such a polypeptide-based battery is a first step to addressing the need for alternative chemistries for green and sustainable batteries in a future circular economy.

25 ENERGY STORAGE↗
DOE OSTI2022

Compositionally complex doping for zero-strain zero-cobalt layered cathodes

We report the high volatility of the price of cobalt and the geopolitical limitations of cobalt mining have made the elimination of Co a pressing need for the automotive industry. Owing to their high energy density and low-cost advantages, high-Ni and low-Co or Co-free (zero-Co) layered cathodes have become the most promising cathodes for next-generation lithium-ion batteries. However, current high-Ni cathode materials, without exception, suffer severely from their intrinsic thermal and chemo-mechanical instabilities and insufficient cycle life. Here, in this paper, by using a new compositionally complex (high-entropy) doping strategy, we successfully fabricate a high-Ni, zero-Co layered cathode that has extremely high thermal and cycling stability. Combining X-ray diffraction, transmission electron microscopy and nanotomography, we find that the cathode exhibits nearly zero volumetric change over a wide electrochemical window, resulting in greatly reduced lattice defects and local strain-induced cracks. In-situ heating experiments reveal that the thermal stability of the new cathode is significantly improved, reaching the level of the ultra-stable NMC-532. Owing to the considerably increased thermal stability and the zero volumetric change, it exhibits greatly improved capacity retention. This work, by resolving the long-standing safety and stability concerns for high-Ni, zero-Co cathode materials, offers a commercially viable cathode for safe, long-life lithium-ion batteries and a universal strategy for suppressing strain and phase transformation in intercalation electrodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale

Many industrial chemicals that are produced from fossil resources could be manufactured more sustainably through fermentation. In this work, we describe the development of a carbon-negative fermentation route to producing the industrially important chemicals acetone and isopropanol from abundant, low-cost waste gas feedstocks, such as industrial emissions and syngas. Using a combinatorial pathway library approach, we first mined a historical industrial strain collection for superior enzymes that we used to engineer the autotrophic acetogen Clostridium autoethanogenum. Next, we used omics analysis, kinetic modeling and cell-free prototyping to optimize flux. Finally, we scaled-up our optimized strains for continuous production at rates of up to ~3 g/L/h and ~90% selectivity. Life cycle analysis confirmed a negative carbon footprint for the products. Unlike traditional production processes, which result in release of greenhouse gases, our process fixes carbon. These results show that engineered acetogens enable sustainable, high-efficiency, high-selectivity chemicals production. We expect that our approach can be readily adapted to a wide range of commodity chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unlocking saponin biosynthesis in soapwort

Abstract Soapwort ( Saponaria officinalis ) is a flowering plant from the Caryophyllaceae family with a long history of human use as a traditional source of soap. Its detergent properties are because of the production of polar compounds (saponins), of which the oleanane-based triterpenoid saponins, saponariosides A and B, are the major components. Soapwort saponins have anticancer properties and are also of interest as endosomal escape enhancers for targeted tumor therapies. Intriguingly, these saponins share common structural features with the vaccine adjuvant QS-21 and, thus, represent a potential alternative supply of saponin adjuvant precursors. Here, we sequence the S . officinalis genome and, through genome mining and combinatorial expression, identify 14 enzymes that complete the biosynthetic pathway to saponarioside B. These enzymes include a noncanonical cytosolic GH1 (glycoside hydrolase family 1) transglycosidase required for the addition of d- quinovose. Our results open avenues for accessing and engineering natural and new-to-nature pharmaceuticals, drug delivery agents and potential immunostimulants.

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

A universal language for finding mass spectrometry data patterns

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here, in this study, we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Damiani, Tito [Czech Academy of Sciences (CAS), Pr↗
DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗
DOE OSTI2022

Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor

Abstract The emerging field of material-based data science requires information-rich databases to generate useful results which are currently sparse in the stress engineering domain. To this end, this study uses the’materials-aware’ text-mining toolkit, ChemDataExtractor, to auto-generate databases of yield-strength and grain-size values by extracting such information from the literature. The precision of the extracted data is 83.0% for yield strength and 78.8% for grain size. The automatically-extracted data were organised into four databases: a Yield Strength, Grain Size, Engineering-Ready Yield Strength and Combined database. For further validation of the databases, the Combined database was used to plot the Hall-Petch relationship for, the alloy, AZ31, and similar results to the literature were found, demonstrating how one can make use of these automatically-extracted datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor

The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery.

14 SOLAR ENERGY↗
DOE OSTI2022

A thermoelectric materials database auto-generated from the scientific literature using ChemDataExtractor

An auto-generated thermoelectric-materials database is presented, containing 22,805 data records, automatically generated from the scientific literature, spanning 10,641 unique extracted chemical names. Each record contains a chemical entity and one of the seminal thermoelectric properties: thermoelectric figure of merit, ZT; thermal conductivity, κ; Seebeck coefficient, S; electrical conductivity, σ; power factor, PF; each linked to their corresponding recorded temperature, T. The database was auto-generated using the automatic sentence-parsing capabilities of the chemistry-aware, natural language processing toolkit, ChemDataExtractor 2.0, adapted for application in the thermoelectric-materials domain, following a rule-based sentence-simplification step. Data were mined from the text of 60,843 scientific papers that were sourced from three scientific publishers: Elsevier, the Royal Society of Chemistry, and Springer. To the best of our knowledge, this is the first automatically-generated database of thermoelectric materials and their properties from existing literature. The database was evaluated to have a precision of 82.25% and has been made publicly available to facilitate the application of data science in the thermoelectric-materials domain, for analysis, design, and prediction.

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

A database of synthetic inelastic neutron scattering spectra from molecules and crystals

Abstract Inelastic neutron scattering (INS) is a powerful tool to study the vibrational dynamics in a material. The analysis and interpretation of the INS spectra, however, are often nontrivial. Unlike diffraction, for which one can quickly calculate the scattering pattern from the structure, the calculation of INS spectra from the structure involves multiple steps requiring significant experience and computational resources. To overcome this barrier, a database of INS spectra consisting of commonly seen materials will be a valuable reference, and it will also lay the foundation of advanced data-driven analysis and interpretation of INS spectra. Here we report such a database compiled for over 20,000 organic molecules and over 10,000 inorganic crystals. The INS spectra are obtained from a streamlined workflow, and the synthetic INS spectra are also verified by available experimental data. The database is expected to greatly facilitate INS data analysis, and it can also enable the utilization of advanced analytics such as data mining and machine learning. Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Dataset of theoretical multinary perovskite oxides

Perovskite oxides (ternary chemical formula ABO 3 ) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed $A_xA^{'}_{1-x}B_yB^{'}_{1-y}O_3$ compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ compositions were generated in the a - b + a - Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH f ) and decomposition enthalpies (ΔH d ) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated $A_{0.5}A^{'}_{0.5}B_{0.5}B^{'}_{0.5}O_3$ perovskite structures using MP-compatible DFT calculations.

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

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗