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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 OSTI2025

Prospects of Nanoscience with Nanocrystals: 2025 Edition

Abstract Nanocrystals (NCs) of various compositions have made important contributions to science and technology, with their impact recognized by the 2023 Nobel Prize in Chemistry for the discovery and synthesis of semiconductor quantum dots (QDs). Over four decades of research into NCs has led to numerous advancements in diverse fields, such as optoelectronics, catalysis, energy, medicine, and recently, quantum information and computing. The last 10 years since the predecessor perspective “Prospect of Nanoscience with Nanocrystals” was published in ACS Nano have seen NC research continuously evolve, yielding critical advances in fundamental understanding and practical applications. Mechanistic insights into NC formation have translated into precision control over NC size, shape, and composition. Emerging synthesis techniques have broadened the landscape of compounds obtainable in colloidal NC form. Sophistication in surface chemistry, jointly bolstered by theoretical models and experimental findings, has facilitated refined control over NC properties and represents a trusted gateway to enhanced NC stability and processability. The assembly of NCs into superlattices, along with two-dimensional (2D) photolithography and three-dimensional (3D) printing, has expanded their utility in creating materials with tailored properties. Applications of NCs are also flourishing, consolidating progress in fields targeted early on, such as optoelectronics and catalysis, and extending into areas ranging from quantum technology to phase-change memories. In this perspective, we review the extensive progress in research on NCs over the past decade and highlight key areas where future research may bring further breakthroughs.

Chemistry↗
DOE OSTI2026

Geometrical optics without singularities: using the ray time as the coordinate space

Geometrical optics (GO) is widely used for reduced modelling of waves in plasmas, but it fails near reflection points, where it predicts a spurious singularity of the wave amplitude. We show how to avoid this singularity by adopting a different representation of the wave equation. Instead of the physical coordinate 𝑥 and the wavevector 𝑘, we use the ray time 𝜏 as the new canonical coordinate and the ray energy ℎ as the associated canonical momentum. To derive the envelope equation in the 𝜏-representation, we construct the Weyl symbol calculus on the (𝜏,ℎ) space and show that the corresponding Weyl symbols are related to their (𝑥,𝑘) counterparts by the Airy transform. This allows us to express the coefficients in the envelope equation through the known properties of the original dispersion operator. When necessary, solutions of this equation can be mapped to the 𝑥-space using a generalised metaplectic transform. However, the field per se might not even be needed in practice. Instead, knowing the corresponding Wigner function usually suffices for linear and quasilinear calculations. As a Weyl symbol itself, the Wigner function can be mapped analytically, using the aforementioned Airy transform. We show that the standard Airy patterns that form in regions where conventional GO fails are successfully reproduced within metaplectic GO (MGO) simply by remapping the field from the 𝜏-space to the 𝑥-space. An extension to mode-converting waves is also presented. This formulation, which we call generalised MGO, can be particularly useful, for example, for reduced modelling of the O–X conversion in inhomogeneous plasma near the critical density, an effect that is important for fusion applications and also occurs in the ionosphere. Overall, MGO can replace GO for any practical purposes, because it better handles cutoffs and is similar otherwise.

plasma waves↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2026

CRADA Number NFE-24-10495 with Algaeo, LLC (CRADA Final Report)

This study investigated the potential for a synthetic consortium of mutualistic terrestrial microbes— comprising the fungi Laccaria bicolor and Serendipita indica alongside bacterial Pseudomonas strains—to influence the growth and productivity of the freshwater microalgae Chlorella vulgaris. The project aimed to determine if microbial complexes engineered to enhance terrestrial plant growth could provide similar growth-promoting benefits or pathogen resistance within an aquatic algal system. Using a quantitative experimental design, C. vulgaris was co-cultured with the microbial mix under controlled laboratory conditions, with growth rates, biomass density, and metabolic activity monitored over a standard cultivation period. The results demonstrated no significant symbiotic relationship or growth enhancement between these terrestrial microbes and the microalgae, as the C. vulgaris maintained independent growth trajectories unaffected by the fungal or bacterial inoculants. We conclude that the specialized mutualisms of these fungi and bacteria are likely niche-specific to vascular plants and do not readily translate to the phycosphere of C. vulgaris. These findings are valuable to synthetic biologists and bioenergy researchers, as they define the functional boundaries of inter-kingdom microbial engineering and underscore the necessity of selecting niche-compatible species when designing consortia for industrial algal cultivation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Knowledge Beacons: Web services for data harvesting of distributed biomedical knowledge

The continually expanding distributed global compendium of biomedical knowledge is diffuse, heterogeneous and huge, posing a serious challenge for biomedical researchers in knowledge harvesting: accessing, compiling, integrating and interpreting data, information and knowledge. In order to accelerate research towards effective medical treatments and optimizing health, it is critical that efficient and automated tools for identifying key research concepts and their experimentally discovered interrelationships are developed. As an activity within the feasibility phase of a project called “Translator” (https://ncats.nih.gov/translator) funded by the National Center for Advancing Translational Sciences (NCATS) to develop a biomedical science knowledge management platform, we designed a Representational State Transfer (REST) web services Application Programming Interface (API) specification, which we call a Knowledge Beacon. Knowledge Beacons provide a standardized basic API for the discovery of concepts, their relationships and associated supporting evidence from distributed online repositories of biomedical knowledge. This specification also enforces the annotation of knowledge concepts and statements to the NCATS endorsed the Biolink Model data model and semantic encoding standards (https://biolink.github.io/biolink-model/). Implementation of this API on top of diverse knowledge sources potentially enables their uniform integration behind client software which will facilitate research access and integration of biomedical knowledge.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Randomly Layered Superstructure of In 2 O 3 Truncated Nano-Octahedra and Its High-Pressure Behavior

This study outlines the synthesis and characterization of a unique superlattice composed of vertex-truncated indium oxide (In 2 O 3 ) nano-octahedra, along with an exploration of its response to high-pressure conditions. Here, using a bright-field transmission electron microscope (BF-TEM), we determined an average circumradius of 15.2 nm for these octahedral building blocks. The resilience and response of the superlattice to pressure variations, peaking at 18.01 GPa, were examined by employing synchrotron-based Wide-Angle X-ray Scattering (WAXS) and Small-Angle X-ray Scattering (SAXS) techniques. The WAXS data revealed no phase transitions, reinforcing the stability of the 2D superlattice comprised of random layers in alignment with a 2D p31m symmetry. Notably, the SAXS data unveiled a pressure-induced, irreversible octahedron translation and ligand interaction occurring within the random layer. Through our examination of these pressure-sensitive behaviors, we identified a distinctive translation model inherent to octahedra and observed modulation in the superlattice cell parameter induced by pressure. This research signifies a noteworthy progression in deciphering the intricate behaviors of 2D superlattices under high-pressure conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

WfCommons: Data Collection and Runtime Experiments using Multiple Workflow Systems

Scientific workflows have become ubiquitous across scientific fields, and their execution methods and systems continue to be the subject of research and development. Most experimental evaluations of these workflows rely on workflow instances, which can be either real-world or synthetic, to ensure relevance to current application domains or explore hypothetical/future scenarios. The WfCommons project addresses this need by providing data and tools to support such evaluations. In this paper, we present an overview of WfCommons and describe two recent developments. Firstly, we introduce a workflow execution "tracer" for Nextflow, which significantly enhances the set of real-world instances available in WfCommons. Secondly, we describe a workflow instance "translator" that enables the execution of any real-world or synthetic WfCommons workflow instance using Dask. Our contributions aim to provide researchers and practitioners with more comprehensive resources for evaluating scientific workflows.

Casanova, Henri↗
DOE OSTI2022

Dual Atomic Coherence in the Self-Assembly of Patchy Heterostructural Nanocrystals

Advances in the synthesis and self-assembly of nanocrystals have enabled researchers to create a plethora of different nanoparticle superlattices. But while many super lattices with complex types of translational order have been realized, rotational order of nanoparticle building blocks within the lattice is more difficult to achieve. Self-assembled superstructures with atomically coherent nanocrystal lattices, which are desirable due to their exceptional electronic and optical properties, have been fabricated only for a few selected systems. Here, we combine experiments with molecular dynamics (MD) simulations to study the self-assembly of heterostructural nanocrystals (HNCs), consisting of a near-spherical quantum dot (QD) host decorated with a small number of epitaxially grown gold nanocrystal (Au NC) "patches ". Self-assembly of these HNCs results in face-centered-cubic (fcc) superlattices with well-defined orientational relationships between the atomic lattices of both QD hosts and Au patches. MD simulations indicate that the observed dual atomic coherence is linked to the number, size, and relative positions of gold patches. Here this study provides a strategy for the design and fabrication of NC superlattices with large structural complexity and delicate orientational order.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Effects of HLA single chain trimer design on peptide presentation and stability

MHC class I “single-chain trimer” molecules, coupling MHC heavy chain, β 2 -microglobulin, and a specific peptide into a single polypeptide chain, are widely used in research. To more fully understand caveats associated with this design that may affect its use for basic and translational studies, we evaluated a set of engineered single-chain trimers with combinations of stabilizing mutations across eight different classical and non-classical human class I alleles with 44 different peptides, including a novel human/murine chimeric design. While, overall, single-chain trimers accurately recapitulate native molecules, care was needed in selecting designs for studying peptides longer or shorter than 9-mers, as single-chain trimer design could affect peptide conformation. In the process, we observed that predictions of peptide binding were often discordant with experiment and that yields and stabilities varied widely with construct design. We also developed novel reagents to improve the crystallizability of these proteins and confirmed novel modes of peptide presentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

BGC Atlas: a web resource for exploring the global chemical diversity encoded in bacterial genomes

Secondary metabolites are compounds not essential for an organism’s development, but provide significant ecological and physiological benefits. These compounds have applications in medicine, biotechnology and agriculture. Their production is encoded in biosynthetic gene clusters (BGCs), groups of genes collectively directing their biosynthesis. The advent of metagenomics has allowed researchers to study BGCs directly from environmental samples, identifying numerous previously unknown BGCs encoding unprecedented chemistry. Here, we present the BGC Atlas (https://bgc-atlas.cs.uni-tuebingen.de), a web resource that facilitates the exploration and analysis of BGC diversity in metagenomes. The BGC Atlas identifies and clusters BGCs from publicly available datasets, offering a centralized database and a web interface for metadata-aware exploration of BGCs and gene cluster families (GCFs). We analyzed over 35 000 datasets from MGnify, identifying nearly 1.8 million BGCs, which were clustered into GCFs. The analysis showed that ribosomally synthesized and post-translationally modified peptides are the most abundant compound class, with most GCFs exhibiting high environmental specificity. We believe that our tool will enable researchers to easily explore and analyze the BGC diversity in environmental samples, significantly enhancing our understanding of bacterial secondary metabolites, and promote the identification of ecological and evolutionary factors shaping the biosynthetic potential of microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Directed self-assembly of chiral liquid crystals into biomimetic bouligand structures in thin film

The Bouligand structure, renowned for its helicoidal arrangement and enhanced mechanical properties, has attracted significant research interest for its ability to impart enhanced strength to intrinsically soft materials. Biomimetic approaches have centered on fibrous structures in bulk materials, but translating this architecture into thin-film regime for miniaturized-wearable devices with programmable functions remains challenging. Here, we direct the self-assembly of cholesteric liquid crystals (CLCs) into hierarchical helical structures using chemically patterned surfaces. Alternating surface anchoring regions align uniform lying-down helices at the nanoscale, guiding a secondary microscale helical structure exhibiting both left- and right-handed twists. This mimetic Bouligand structure in CLCs enables optical modulation under applied field and strain with enhanced mechanical response. Simulations reveal the structural evolution from initial Bouligand configuration in LC layers to alternating twist helices. This research provides a basis for designing and manufacturing miniaturized or wearable devices with nanometer-scale precision in regulating electro-optical and mechanical properties. Bouligand structures, which offer strength in natural materials, are of interest but difficult to obtain. Here, the authors report the development of such structures by directed self-assembly of cholesteric single crystals into hierarchical helical structures, with the secondary structure having right and left-handed twists.

Bouligand structure↗
DOE OSTI2020

Understanding the Gap between Academic Research and Industrial Requirements in Rechargeable Zinc-Ion Batteries

Rechargeable Zn-ion batteries (RZIBs) are considered to be promising energy storage systems for large-scale applications owing to their relatively high energy densities and inherently low costs, environmental benignity, as well as safety. With increasing interest and effort being devoted to this field, RZIBs are being tremendously advanced and are very likely to be commercialized in the near future. However, some challenges remain that need to be addressed. Moreover, reported results are quite often overstated through artificial exaggeration and cannot be translated to industrial applications as they were obtained under ideal testing conditions. Therefore, in this perspective, here we discuss key parameters that need to be carefully considered when translating laboratory-developed RZIBs into commercial reality. We also present a critical mini-review on RZIBs and devote effort to issues that are overlooked in academic research. We believe that this perspective provides new or seldom-discussed insight for future work and bridges the gap between academia and industry.

25 ENERGY STORAGE↗
DOE OSTI2026

Integrating science for water security governance

Hydrological extremes are intensifying globally, increasing the complexity of decisions required to ensure water security. Advances in hydrological science, modeling, and data systems have expanded the technical frontier of water research, yet uptake of scientific insights in policy and management decisions remains limited. This persistent science–policy gap is not primarily a failure of knowledge generation or robustness, but an institutional challenge shaped by how scientific and governance systems are organized, coordinated, and connected to support the effective use of scientific knowledge. These challenges are particularly pronounced in multi-level and transboundary water governance, where decisions span jurisdictions and require coordination across institutional and political boundaries. We synthesize research at the science–policy interface and evidence from water security initiatives to show how institutional arrangements, scientific tool development, and research practices enable or constrain the sustained use of scientific knowledge in water-security governance processes. Building on these insights, we develop ‘shared decision infrastructure’ as a framing to describe how scientific knowledge is embedded within the institutional, relational, and procedural arrangements that connect science to decision-making processes over time. We translate this framing into a practical intervention roadmap centered on institutional design, tool translation, sustained co-production, and outcome-oriented evaluation to support the integration of science into ongoing governance processes. By positioning science as shared decision infrastructure, the roadmap clarifies how researchers can design scientific efforts that support more coordinated, accountable, and adaptive water security decisions amid deepening uncertainty.

M whitney, Kristen [NASA Goddard Space Flight Cent↗
DOE OSTI2021

Characterizing Long COVID: Deep Phenotype of a Complex Condition

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Funding: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Bioinspiration in light harvesting and catalysis

Capturing and converting solar energy into fuels and feedstocks is a global challenge that spans numerous disciplines and fields of research. Billions of years of evolution have allowed natural organisms to hone strategies for harvesting light from the sun and storing energy in the form of carbon–carbon and carbon–hydrogen bonds. Photosynthetic antenna proteins capture solar photons and funnel photoexcitations to reaction centres with high yields, and enzymes catalyze multi-electron reactions, facilitating chemical transformations not yet efficiently implemented using artificially engineered catalysts. Researchers in renewable energy often look to nature to understand the mechanisms at work and, if possible, to explore their translation into artificial systems. Here, we review advances in bioinspiration across the fields of biological light harvesting and chemical energy conversion. We examine how multi-photon and multi-electron reactions in biology can inspire new methods in photoredox chemistry to achieve novel, selective and complex organic transformations; how carbonic-dehydrogenase-inspired design principles enable catalytic reactions such as the conversion of CO 2 into useful products such as fuels; and how concepts from photosynthetic antenna complexes and reaction centres can benefit artificial light-harvesting materials. We then consider areas in which bioinspiration could enable advances in the rational design of molecules and materials, the expansion of the synthetic capabilities of catalysts and the valorization of molecular building blocks. Here, we highlight the challenges that must be overcome to realize these advances and propose new directions that may use bioinspiration to achieve them.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing high-fidelity neural network potentials through low-fidelity sampling

The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.

97 MATHEMATICS AND COMPUTING↗