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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗
DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Sub-melt nanosecond pulsed-laser induced densification and strain-field relaxation in single-crystal diamond

Dislocations and polishing-induced defect networks in synthetic diamond introduce local strain fields and broaden Raman features, limiting performance in optical, thermal, and electronic applications. Laser annealing is emerging as a promising approach to repair surface and near-surface defects in diamond without entering the melt regime, yet surface densification, defect-state modification, and associated structural changes have not been well quantified. In this work, we show that sub-melt nanosecond pulsed-laser annealing (PLA) induces near-surface densification and defect-mediated strain relaxation in single-crystal Chemical Vapor Deposition (CVD) diamond. Single- and two-pulse PLA were applied, and structural evolution was quantified using co-registered ISO 25,178 white-light interferometry, depth-resolved Raman spectroscopy, and cross-sectional STEM with geometric phase analysis (GPA). Across a 5 × 6 grid (n = 30), responsive regions exhibit large reductions in local slope (Sdq 45–65%), developed area (Sdr 60–90%), height spread (Sp, Sz 30–65%), void volume (Vv 57–60%), and roughness amplitude (Sa, Sq 48–57%), consistent with densification of ∼4–6.5 nm. Raman profiling shows narrowing of the diamond line and improved spectral uniformity to depths of ∼2–3 μm. Given that the Raman probing depth significantly exceeds the densified layer thickness, this response is interpreted as consistent with long-range strain-field redistribution originating from the near-surface region. STEM-GPA strain maps further support this interpretation, showing smoother strain fields, suppressed hotspots, and redistribution of localized strain concentrations following PLA. These results are consistent with defect-mediated strain relaxation and densification-driven modification of the near-surface energy state. The approach provides a scalable pathway for improving near-surface structural quality in diamond relevant to electronic, photonic, and quantum applications.

Areal surface metrology (ISO 25,178)↗
DOE OSTI2024

Multi-scale signaling and tumor evolution in high-grade gliomas

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Scalable fabrication of a tough and recyclable spore-bearing biocomposite thermoplastic polyurethane

Thermoplastic polyurethanes (TPUs) are a class of versatile thermoplastic elastomers, but most of their products lack a proper recycling strategy or have no end-of-life solutions. To pursue a sustainable end-of-life solution for TPU-based products, self-disintegrating biocomposite TPUs have recently been developed by embedding spores of TPU-degrading bacteria into TPUs via melt extrusion. Herein, we improve upon spore-bearing biocomposites and demonstrate industrially relevant manufacturing conditions for fabricating biocomposite TPUs. To minimize the coloration of biocomposite TPUs, spore production was modified. The innate brown color of the resulting materials was diminished by reducing FeSO 4 in sporulation media, generating white spores without compromising spore productivity, viability, morphology or heat-shock tolerance. Biocomposite TPUs containing white spores displayed a 45 % increase in toughness compared to TPUs without spores, while retaining ∼ 90 % spore viability post processing. Furthermore, biocomposite TPU fabrication was demonstrated using a scalable continuous extruder followed by injection molding. Biocomposite TPUs generated by these industry-relevant processes exhibited comparable toughness improvement and spore viability to biocomposite TPU prepared using a lab scale microcompounder, while enhancing productivity by 30-fold. Finally, spore addition significantly improved the recyclability of biocomposite TPUs, enabling 80 % toughness retention after 5 rounds of iterative melt processing. Additionally, no negative effect on the lifespan of the generated TPUs was observed over 1 year of storage. Overall, this study confirms that spore-bearing biocomposite TPUs are promising for practical applications, offering an accessible method to enhance toughness and sustainability of commercial TPUs through the incorporation of spore-based living fillers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Metal oxide-promoted calcium cuprate catalysts for diol oxidative dehydrocyclization to lactones

Here, this work investigates structure-function relationships in electronically tunable, redox-active, basic Cu-Ca mixed metal oxide catalysts for oxidative dehydrocyclization of liquid diols to lactones. Compositional screening identified Ni 2+ and Zn 2+ as effective promoters that increase the surface Cu 2+ population by ∼1.7× and Cu-normalized activity for liquid 1,4-butanediol conversion to γ-butyrolactone by ∼3–4×. In situ Raman spectroscopy, in situ X-ray absorption spectroscopy (XAS), in situ diffuse-reflectance Fourier transform infrared spectroscopy (DRIFTS), ex situ X-ray diffraction (XRD), and H 2 -temperature-programmed reduction (H 2 -TPR) show that Ni 2+ or Zn 2+ incorporation promotes the formation of Ca 0.82 Cu 1.00 O 2 nanoparticles under mild calcination conditions. This cuprate phase features stronger and shorter Cu–O bonds (1.90 Å) than inactive bulk CuO (1.95 Å) and square-planar Cu 2+ O 4 sites with enhanced d z2 electrophilicity, strengthening alkoxy adsorption. Pyridine-DRIFTS confirms the purely basic nature of the catalyst surface, while methanol-DRIFTS indicates Cu 2+ surface enrichment with Ni or Zn promotion, where Cu–O(Ca)–Cu sites can exist as amorphous domains or a truncation layer on crystalline nanoparticles.

09 BIOMASS FUELS↗
DOE OSTI2025

Quantifying the effects of artificial aging on the ignition and self-propagating reactions of Ni(V)/Al multilayers

Bimetallic, reactive multilayers are uniformly structured materials composed of alternating nanoscale layers that may be ignited to produce self-propagating intermetallic-formation reactions. When reactive multilayers age, there is a change in local composition and loss of stored chemical energy due to mass transport and rearrangement at the interfaces. Here, to quantify the long-term reliability of commercial Ni(V)/Al multilayers, the effects of accelerated aging on both ignition sensitivity and self-propagating reactions have been examined. Thermally aged samples were characterized using transmission electron microscopy, differential scanning calorimetry, and laser ignition combined with high-speed videography. The analytically quantifiable nature of both continuous wave laser ignition and reactive wave propagation enabled calculations of Arrhenius rate constants for as-received and various heat-treated multilayers. With heat treatment, there is a change in the intermixed thickness and interfacial chemistry that decreases the activation energy for point ignition but increases it for self-propagating reactions. This finding implies that with increased thermal aging, the reaction becomes easier to facilitate in the solid state but harder in the liquid phase.

Energetic material↗
DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗