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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Leafweb: Leaf Gas Exchange and Pulse-Amplitude Modulated Fluorometry for C4 Species, June 2026 Release

This dataset contains leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry for 98 C4 species. The C4 photosynthetic pathway employs specialized CO2 concentration mechanisms and Kranz anatomy to enrich CO2 concentration around Rubisco, the enzyme that catalyzes carbon fixation in the Calvin-Benson cycle to suppress photorespiration and increase the use efficiencies of light, nitrogen, and water as compared to the C3 photosynthetic pathways. Large-scale C4 photosynthetic datasets are relatively scarce, which has affected C4 photosynthesis research. To improve C4 photosynthetic data availability, Leafweb organized an effort to systematically collect, compile, standardize, and organize measurements of leaf gas exchange and/or Pulse-Amplitude Modulated (PAM) fluorometry of C4 species. This derived a C4 photosynthetic dataset containing measurements made by independent researchers in multiple countries in various environments (field, garden, or greenhouse). It covers three biochemical subtypes – the nicotinamide adenine dinucleotide phosphate-malic enzyme (NADP-ME), nicotinamide adenine dinucleotide-malic enzyme (NAD-ME), and phosphoenolpyruvate carboxykinase (PEP-CK) subtypes. This dataset is useful for using Artificial Intelligence / Machine Learning and mechanistic models to study C4 photosynthesis and compare across different biochemical subtypes. This dataset contains 3 compressed (*.zip) folders containing 1,892 data files in comma-separate values (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Zhou, Haoran [Tianjin University, China]↗
DOE OSTI2023

A critical review of existing and emerging technologies and systems to optimize solid waste management for feedstocks and energy conversion

Solid waste generation and its accumulation is increasing at an alarming pace due to population growth and urbanization posing severe risks to health, safety, and natural ecosystems. Herein this review strategically addresses the challenges and solutions to increasing the sustainability footprint of solid waste management (SWM) systems by revealing multipronged approaches that reduce solid waste and handling costs while generating revenue and reducing greenhouse gas and related emissions. For example, the United States sends ~150 million tons of waste to landfills, which is composed of over 75% organic and recyclable materials having a potential to be diverted to alternative scenarios. The emergence of an automated upstream and downstream sorting process for solid waste to increase material diversion from landfills is a promising approach for creating sustainable SWM. The utilization of artificial-intelligence-enabled smart and automated systems at the home and industrial scales, comprehensive public re-education including awareness of the adverse effects of landfilled waste on the ecosystem, and more eco-friendly product development are required to significantly reduce landfills and their negative footprint.

09 BIOMASS FUELS↗
DOE OSTI2023

Bound-preserving finite element approximations of the Keller–Segel equations

We report this paper aims to develop numerical approximations of the Keller–Segel equations that mimic at the discrete level the lower bounds and the energy law of the continuous problem. We solve these equations for two unknowns: the organism (or cell) density, which is a positive variable, and the chemoattractant density, which is a non-negative variable. We propose two algorithms, which combine a stabilized finite element method and a semi-implicit time integration. The stabilization consists of a nonlinear artificial diffusion that employs a graph-Laplacian operator and a shock detector that localizes local extrema. As a result, both algorithms turn out to be nonlinear and can generate cell and chemoattractant numerical densities fulfilling lower bounds. However, the first algorithm requires a suitable constraint between the space and time discrete parameters, whereas the second one does not. We design the latter to attain a discrete energy law on acute meshes. We report some numerical experiments to validate the theoretical results on blowup and nonblowup phenomena. In the blowup setting, we identify a locking phenomenon that relates the L ∞ (Ω)-norm to the L 1 (Ω)-norm limiting the growth of the singularity when supported on a macroelement.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2025

2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research: Position Papers

This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

In this work, we apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes a-pinene and ß-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ~9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ~50-60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

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

Mineral reactivity determines root effects on soil organic carbon

Modern conceptual models of soil organic carbon (SOC) cycling focus heavily on the microbe-mineral interactions that regulate C stabilization. However, the formation of ‘stable’ (i.e. slowly cycling) soil organic matter, which consists mainly of microbial residues associated with mineral surfaces, is inextricably linked to C loss through microbial respiration. Therefore, what is the net impact of microbial metabolism on the total quantity of C held in the soil? To address this question, we constructed artificial root-soil systems to identify controls on C cycling across the plant-microbe-mineral continuum, simultaneously quantifying the formation of mineral-associated C and SOC losses to respiration. Here we show that root exudates and minerals interacted to regulate these processes: while roots stimulated respiratory C losses and depleted mineral-associated C pools in low-activity clays, root exudates triggered formation of stable C in high-activity clays. Moreover, we observed a positive correlation between the formation of mineral-associated C and respiration. This suggests that the growth of slow-cycling C pools comes at the expense of C loss from the system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Anion-exchanged and quaternary ammonium functionalized MIL-101-Cr metal-organic framework (MOF) for ReO 4 – /TcO 4 – sequestration from groundwater

There are few effective technologies for the sequestration of highly water-soluble pertechnetate (TcO 4 – ) from contaminated water despite the urgency of environmental and public health concerns. Here, anion exchanged and cetyltrimethylammonium bromide (CTAB) functionalized MIL-101-Cr-NO 3 were investigated for perrhenate (ReO 4 – ), a surrogate of TcO 4 – , sequestration from artificial groundwater. Cl – , I – , and CF 3 SO 3 – exchanged MIL-101-Cr proved more effective at ReO 4 – removal than the parent MIL-101-Cr-F. Compared to the parent framework, CTAB functionalized MIL-101-Cr-NO 3 increased ReO 4 – removal capacity from 39 to 139 mg/g, improved the reaction kinetics from ~30 to <10 min to reach full adsorption capacity and the selectivity for ReO 4 – over competing NO 3 – , CO 3 2– , SO 4 2– , and Cl – . Spectroscopic data indicated that the chemical speciation of Re in the exchanged MIL-101-Cr remained ReO 4 – , indicating synergistic sequestration through both anion exchange and non-ion exchange binding with the positively charged ligand of CTAB. These studies foreshadow potential applications of MOFs for the remediation of 99 TcO 4 – from contaminated environments.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

PCAST: Report to the President on Supercharging Research: Harnessing Artificial Intelligence to Meet Global Challenges

The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On April 29, 2024, PCAST sent a report to the President with recommendations to help the U.S. harness the full potential of AI to equitably and responsibly supercharge scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Preface to the special issue: photosynthesis-inspired biohybrid and biomimetic systems

Research of Photosynthetic-Inspired Biohybrid and Biomimetic Systems has gained significant momentum in recent years as an approach to both study and use solar energy in new photon-to-fuels schemes that mimic photosynthesis. This intense interest arises from the incredible opportunity that photosynthetic-inspired biohybrids provide, which is to harness the optimal qualities of both artificial and natural photosynthetic systems byusing evolvedprotein architectures from biology to address synthetic challenges,and by incorporatingsynthetic molecules to creatively probe and utilize Nature’s mechanisms.

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

SOMAS: a platform for data-driven material discovery in redox flow battery development

Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

25 ENERGY STORAGE↗
DOE OSTI2023

Dynamic Oscillation and Motion of Oil‐in‐Water Emulsion Droplets

The interplay of interfacial tensions on droplets results in a range of self‐powered motions that mimic those of living systems and serve as a tunable model to understand their complex non‐equilibrium behavior. Spontaneous shape deformations and oscillations are crucial features observed in nature but difficult to incorporate in synthetic artificial systems. Here, we report sessile oil‐in‐water emulsions that exhibit rapid oscillating behavior. The oscillations depend on the nature and concentration of the surfactant, the chemical composition of the oil, and the wettability of the solid substrate. The rapid changes in the contact angle per oscillation are observed using side‐view optical microscopy. We propose that the changes in the interfacial tension of the oil droplets is due to the partitioning of the surfactant into the oil phase and the movement of self‐emulsified oil out of the parent droplets giving rise to the rhythmic variation in droplet contact‐line. The ability to control and understand droplet oscillation can help model similar oscillations in out‐of‐equilibrium systems in nature and reproduce biomimetic behavior in artificial systems for various applications, such as microfluidic lab‐on‐a‐chip and adaptive materials.

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

COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics

The COLUMBUS program system provides the tools for performing high-level multireference (MR) computations, including the multireference configuration interaction (MRCI) method and its multireference averaged quadratic coupled cluster (MR-AQCC) extension, allowing computations on a wide range of fascinating atomic and molecular systems, including the treatment of open-shells and complicated excited state phenomena. The inclusion of spin−orbit coupling (SOC) directly within the MRCI step enables the description of systems containing heavy elements, such as lanthanides and actinides, whose properties are strongly influenced by SOC. Analytic energy gradients and nonadiabatic couplings at the correlated MRCI level provide the foundation for a variety of dynamics studies, giving insight into ultrafast photochemistry. New and ongoing method developments in COLUMBUS include the computation of spin densities, improved descriptions of ionic states, enhancements to the AQCC method, and the porting of COLUMBUS to graphical processing units (GPUs). New external interfaces enable an enhanced description of electronic resonances and molecules in strong laser fields. This work highlights these new developments while providing a detailed account of the diverse applications of COLUMBUS in recent years.

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

Photobiocatalysis for Abiological Transformations

Harnessing biocatalysts for novel abiological transformations is a longstanding goal of synthetic chemistry. Combining the merits of biocatalysis and photocatalysis allows for selective transformations fueled by visible light and offers many advantages including new reactivity, high enantioselectivity, greener syntheses, and high yields. Photoinduced electron or energy transfer enables synthetic methodologies that complement conventional two electron processes or offer orthogonal pathways for developing new reactions. Enzymes are well suited and can be tuned by directed evolution to exert control over radical intermediates thereby suppressing undesirable reactions and delivering high chemo- and stereoselectivities. Within the past decade, the combination of biocatalysis and photocatalysis was mainly focused on exploiting light-regenerated cofactors to increase native enzymatic activity. However, recent developments have demonstrated that the combination can unlock new-to-nature chemistry. Particularly, the discovery and application of new strategies are well poised to expand the applications of photobiocatalysis. In the past three years, our lab has been studying the combinations of photocatalysis and biocatalysis which can be applied to create new synthetic methodologies and solve challenges in synthetic organic chemistry. Our efforts have expanded the strategies for combining external photocatalysts with enzymes through the construction of a synergistic cooperative stereoconvergent reduction system consisting of photosensitized energy transfer and ene-reductase catalyzed alkene reduction. Additionally, our efforts have also extended the capability of cofactor-dependent photoenzymatic systems to include enantioselective bimolecular radical hydroalkylations of alkenes by irradiating electron donor-acceptor complexes comprised of enzymatic redox active cofactors and unnatural substrates. In this Account, we highlight strategies developed by our group and others for combining biocatalysis and photocatalysis with the aim of introducing nonnatural reactivity to enzymes. Presently, strategies applied to achieve this goal include the repurposing of newly discovered photoactive enzymes, the elucidation of new photoreactivity within cofactor-dependent enzymes, the combination of external photocatalysts with enzymes, and the construction of artificial photoenzymes. By demonstrating the successful application of these strategies for achieving selective new-to-nature transformations, we hope to spur interest in the expanding the scope of photobiocatalytic systems through the use and extension of these strategies and creation of new strategies. In addition, we hope to elucidate the intuition in synergizing the unique capabilities of biocatalysis and photocatalysis so that photobiocatalysis can be recognized as a potential solution to difficult challenges in synthetic organic chemistry.

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

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.

Kim, Haseong↗
DOE OSTI2021

Optical-Frequency Magnetic Polarizability in a Layered Semiconductor

The optical response of crystals is most commonly attributed to electric dipole interactions between light and matter. Although metamaterials support “artificial” magnetic resonances supported by mesoscale structuring, there are no naturally occurring materials known to exhibit a nonzero optical-frequency magnetic polarizability. Here, we experimentally demonstrate and quantify a naturally occurring nonzero magnetic polarizability in a layered semiconductor system: two-dimensional (Ruddlesden-Popper phase) hybrid organic-inorganic perovskites. These results demonstrate the only known material with an optical-frequency permeability that differs appreciably from vacuum, informing future efforts to find, synthesize, or exploit atomic-scale optical magnetism for novel optical phenomena such as negative index of refraction and electromagnetic cloaking.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Responsibly Harnessing the Power of AI

Artificial Intelligence (AI) based applications are on the cusp of offering the public and private sectors tools of tremendous potential that will likely transform the world in much the same way that previous technological revolutions have. To harness these tools, a vast and rapidly increasing assortment of ethical AI guidelines and principles are being developed to manage the myriad of risks these tools pose. Documents, frameworks, standards, and regulations have been developed by private industry, research institutions, governments and nongovernmental organizations, international standards bodies and more. This work details representative approaches to ethically developing, managing, and operating current AI technologies. The various approaches are analyzed for commonality, divergence, and implementation strategy to help develop approaches for managing AI tools in the nuclear landscape. This work will support the creation of a common vocabulary and a deeper understanding of the ethical/ responsible AI landscape and advance the considerations required for structured frameworks in the international safeguards domain.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗