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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

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Utilizing Single-Crystalline Transformations for Precise Atom Placement in Multicomponent Cluster-Based Coordination Networks

The assembly of cluster or superatom building-blocks into extended solids has revolutionized materials design, enabling the synthesis of modular semiconductors with well-defined structures and tunable electronic, magnetic or optical properties. This strategy has recently advanced the synthesis of complex metal oxides with multifunctional or emergent behaviors, but precise atom placement of multiple elements with similar chemistries or preferred coordination environments remains a significant challenge. Here, in this study, we present a strategy for synthesizing polyoxometalate (POM)-based coordination networks with up to three different cations in precisely defined positions. Our approach leverages a single-crystal-to-single-crystal (SCSC) transformation in which the spatial placement of cations is governed by their availability at distinct stages of crystallization and transformation. Specifically, [ZP 5 W 30 O 110 ] (15-n)- (Z = Na + , K + , Ca 2+ , Ag + , Bi 3+ , Y 3+ , any Ln 3+ , Th 4+ ) is coordinatively assembled with various bridging metal cations (Y 3+ , any Ln 3+ , Th 4+ ). By using the encapsulated cation (Z) to "label" the POM, we track the phase-transformation and confirm the retention of single crystallinity. The integrated use of POM labeling and SCSC transformation enables rational control over cation distribution and establishes a versatile strategy for constructing multicomponent materials with high compositional and spatial precision.

Chen, Linfeng [Univ. of California, San Diego, CA ↗
DOE OSTI2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

Abadjiev, Daniel [Chicago U.]↗
DOE OSTI2025

Structure-performance relationships in lignin-based transesterification vitrimers: The role of lignin structural features

Lignin has been hailed as an ideal renewable alternative for petrochemical-based prepolymers in material synthesis for a sustainable and circular economy, due to its abundant aromatic network and high carbon content. However, the properties and performance of lignin-derived macromolecules are strongly influenced by the lignin itself. While numerous studies have explored the impact of lignin content on the thermomechanical performance of lignin-based vitrimers, literature on how the inherent structural features of lignin affect these properties is scanty. In this study, hardwood organosolv lignin was fractionated in ethyl acetate, ethanol, and acetone to obtain lignin fractions with varying structural characteristics. These fractions were then modified through carboxylation and crosslinked with epoxidized soybean oil (ESO) at a hydroxyl to epoxy group ratio of 1:1 to produce lignin-based transesterification vitrimers (LVs). The thermal properties (i.e. glass transition temperature and thermal stability), tensile strength, storage modulus, and stress relaxation behavior of the LVs were studied and carefully related to the structural features of lignin. The results revealed a positive relationship between strong hydroxyl content in modified lignin and the tensile strength (5.10–9.71 MPa), storage modulus (1099.4 – 1372.8 MPa), crosslinking density, and stress relaxation of the LVs. Additionally, both the storage modulus and tensile strength exhibited a positive relationship with the ratio of rigid linkages in modified lignin, while lignin molecular weight was found to significantly impact the thermal properties of LVs (i.e Tg and thermal stability). This study not only highlights the valorization of lignin in vitrimer synthesis but also provide insights for designing lignin-based materials with tailored properties for specific applications.

Bio-based polymer↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2026

Molecularly engineered ZnO–carbon nanosheets from fumaric acid precursors for efficient photocatalytic water purification

The photocatalytic breakdown of organic contaminants is crucial for the development of water purification technology. Zinc oxide (ZnO) is an extensively researched photocatalyst; however, its efficacy is hindered by rapid charge recombination and limited utilization of UV irradiation. Resolving these issues necessitates integrating ZnO with conductive carbon phases via scalable, low-temperature synthesis. We provide a molecularly designed sol–gel method that converts zinc–fumarate coordination networks into two-dimensional ZnO–carbon nanosheets utilizing solely aqueous precursors and mild annealing temperatures (400–600 °C). This method utilizes fumaric acid as a dicarboxylate linker and polyvinylpyrrolidone (PVP) as a structural carbon source to produce ultrathin wurtzite ZnO nanosheets embedded inside an amorphous carbon matrix. The resultant ZnO-C hybrid achieves nearly complete methylene blue degradation within 10 min under UV-A illumination, demonstrating first-order kinetics and outstanding recyclability. Compared with commercial ZnO, the ZnO–C nanosheets exhibit comparable rapid photocatalytic degradation, enhanced adsorption behavior, a porous nanosheet morphology, and an integrated ZnO–carbon interfacial structure. These findings provide a viable molecular-templating approach to fabricating various metal oxide–carbon photocatalysts and underscore substantial enhancements in semiconductor efficacy in eco-friendly water treatment systems.

Ozcan, Muca [ORNL] (ORCID:0000000320020474)↗
DOE OSTI2024

Evolution of the regulatory subunits for the heteromeric acetyl-CoA carboxylase

The committed step for de novo fatty acid (FA) synthesis is the ATP-dependent carboxylation of acetyl-coenzyme A catalysed by acetyl-CoA carboxylase (ACCase). In most plants, ACCase is a multi-subunit complex orthologous to prokaryotes. However, unlike prokaryotes, the plant and algal orthologues are comprised both catalytic and additional dedicated regulatory subunits. Novel regulatory subunits, biotin lipoyl attachment domain-containing proteins (BADC) and carboxyltransferase interactors (CTI) (both three-gene families inArabidopsis) represent new effectors specific to plants and certain algal species. The evolutionary history of these genes in autotrophic eukaryotes remains elusive, making it an ongoing area of research. Analyses of potential protein–protein and co-occurrence interactions, informed by gene network patterns using the STRING database, inArabidopsis thalianaandChlamydomonas reinhardtiiunveil intricate gene associations with ACCase, suggesting a complex interplay between FA synthesis and other cellular processes. Among both species, a higher number of co-expressed genes was identified inArabidopsis, indicating a wider potential regulatory network of ACCase in plants. This review investigates the extent to which these genes arose in autotrophic eukaryotes and provides insights into their evolutionary trajectory. This article is part of the theme issue ‘The evolution of plant metabolism’.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2024

Simultaneous control of the electron temperature and safety factor profiles in DIII-D using model-based optimal control techniques

Future tokamak power plants will likely operate using a single, well-defined plasma scenario, either in steady state or for very long pulse lengths. In order to enhance the robustness of the scenario, feedback controllers for a variety of plasma properties will be necessary to counteract any disturbances and ensure safe operation. However, only a limited set of actuators will be available to control many different quantities. Because of this, it is necessary to develop controllers that are able to regulate multiple plasma properties using a limited set of actuators. To this end, a controller has been developed for the simultaneous regulation of both the electron temperature and safety factor profiles in DIII-D. This algorithm uses a linear quadratic integral control synthesis approach based on a linearized model of the dynamics of the two profiles. Two neural network surrogate models, NubeamNet and MMMnet, are included to improve the fidelity of the model. Furthermore, the controller has been tested in simulation using COTSIM, and has demonstrated the ability to simultaneously track changes in both the electron temperature and safety factor targets, including changes in both the magnitude and the shape of the profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Frustrated Magnetism in FeGe 3 O 4 with a Chiral Trillium Network

The discovery of new magnetic ground states in geometrically frustrated lattices remains a central challenge in materials science. Here, we report the synthesis, structural characterization, and frustrated magnetic properties of FeGe 3 O 4 , a newly identified compound that crystallizes in the noncentrosymmetric cubic space group P 2 1 3. In this structure, Fe atoms form an intricate double-trillium lattice with nearest-neighbor Fe−Fe distances of ∼4.2 Å, while Ge 2+ ions mediate magnetic interactions through Fe− Ge−Fe pathways. Field-dependent magnetization at 2 K shows a pronounced nonlinearity, reaching a maximum moment of 2.55(3) μ B /Fe 2+ at 70 kOe without evidence of saturation. Magnetic susceptibility, heat capacity, and neutron scattering collectively reveal the onset of short-range magnetic interactions near 5 K, with no longrange ordering detected down to 0.06 K. Specific heat measurements demonstrate strong frustration: only ∼34% of the expected magnetic entropy is recovered at 2.4 K. Taken together, these results establish FeGe 3 O 4 as a rare example of a geometrically frustrated trillium lattice magnet, offering a promising platform for exploring exotic quantum magnetic phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Selective Sorbent Design: CaS Aerogel for Rapid Remediation of Aqueous Pb (II)

Heavy metals are a persistent environmental problem due to their high toxicity, even at very low concentrations (parts per billion, ppb). The removal of such diluted heavy metals is challenging because of the competition the counterions (Ca 2+ , Na + , Mg 2+ , etc.) present in natural water bodies. The design of sorbents capable of removing ions below the action limit (15 ppb for Pb 2+ ) requires a strong driving force for selective uptake and rapid removal. In this work, we report the synthesis of porous CaS aerogels (surface area = 143.6 m 2 /g) by oxidative assembly of CaS nanoparticles and describe their use in selective Pb 2+ ion remediation from water. Despite the presence of amorphous CaCO 3 (up to 50 wt %) in the gel network, the gels demonstrated a capacity of 17.1 mmol Pb/g aerogel (3543 mg/g), and this could be augmented to 22.5 mmol Pb/g aerogel (4593 mg/g) by modifying the synthesis to reduce CaCO 3 content to ca. 15 wt %. Moreover, the selectivity of CaS aerogels toward Pb 2+ ions is high, as evidenced by little-to-no change in the distribution constant (K d ∼ 10 4 ) in the presence of competing ions (1 M) such as Na + , Mg 2+ , and Ca 2+ . During remediation with low concentrations (100 ppb) of Pb 2+ with CaS aerogels, the level of Pb 2+ dropped to 5.4 ppb (below the 15 ppb EPA limit) within 1 h with a 95.4% removal efficiency. In contrast to the CO 2 supercritically dried aerogels, lower surface area ambient dried gels (xerogels) only remove 40% of the lead ions from a 100 ppb solution, saturating within 1 h. The efficiency and rapidity of selective Pb 2+ uptake using CdS aerogels arise from a combination of a strong thermodynamic driving force for cation exchange (K eq = 2.5 × 10 27 ) and chemisorption along with favorable kinetics associated with the high surface area porous architecture. These results show that formation of high surface area metal chalcogenide aerogels by oxidative assembly to form nanocrystalline architectures, as previously demonstrated for II−VI and IV−VI semiconductors, can be extended to the more highly ionic alkaline earth sulfides.

Aerogels↗
DOE OSTI2024

A Graph Neural Network Surrogate Model for hls4ml

Recent advancements in use of machine learning (ML) techniques on field-programmable gate arrays (FPGAs) have allowed for the implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area are strictly bounded. The hls4ml framework is a procedure that converts trained ML model software to a synthesis result to can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it may not be possible to successfully convert a model into a synthesis result, or the resource consumption of the model may exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model using a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of a given model when passed through the hls4ml pipeline, without needing to run the pipeline.

Plotnikov, Dennis↗
DOE OSTI2025

Time-resolved tracking of cellulose biosynthesis and assembly during cell wall regeneration in live Arabidopsis protoplasts

Cellulose, the most abundant polysaccharide on earth composing plant cell walls, is synthesized by coordinated action of multiple enzymes in cellulose synthase complexes embedded within the plasma membrane. Multiple chains of cellulose fibrils form intertwined extracellular matrix networks. It remains largely unknown how newly synthesized cellulose is assembled into an intricate fibril network on cell surfaces. Here, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly onArabidopsis thalianaprotoplast surfaces as the primary cell wall regenerates. Our observations provide the basis for a model of cellulose fibril network development in protoplasts driven by an interplay of multiscale dynamics that includes rapid diffusion and coalescence of nascent cellulose fibrils, processive elongation of single fibrils, and cellulose fibrillar network rearrangement during maturation. This study provides fresh insights into the dynamic and mechanistic aspects of cell wall synthesis at the single-cell level.

Science & Technology - Other Topics↗
DOE OSTI2025

Topotactic Phase Transformation of Lithiated Spinel to Layered LiMn0.5Ni0.5O2: The Interaction of 3-D and 2-D Li-ion Diffusion

This study investigates the structural evolution of LiMn0.5Ni0.5O2 cathode materials for Li-ion batteries as a function of synthesis temperature and its effect on electrochemical performance. It is demonstrated that, as the synthesis temperature increases from 400 to 900 ?C, a gradual topotactic transformation occurs between a lithiated spinel structure, denoted herein as “lithium-excess spinel” LxS-LiMn0.5Ni0.5O2 (or LxS-LMNO), and the well-known layered LiMn0.5Ni0.5O2 structure prepared at high temperature, HT-LiMn0.5Ni0.5O2 (HT-LMNO). The electrochemical capacity of the LiMn0.5Ni0.5O2 electrodes follows a parabolic trend with increasing synthesis temperature, which is attributed primarily to the gradual transformation of 3-dimensional (3-D) to 2-dimensional (2-D) diffusion pathways for the Li ions. When synthesized at 400 °C, LxS-LiMn0.5Ni0.5O2 electrodes perform well, benefitting from the 3-D network of channels within the LxS structure. By contrast, when prepared at 500-700 °C, LiMn0.5Ni0.5O2 electrodes operate poorly, which is attributed to the formation of locally disordered structural arrangements that impede Li-ion diffusion. Such an increase in local disorder in the mid-temperature synthesis range is attributed to the structural frustration between the lithium-excess spinal and layered end-members. The transformation from the locally disordered to more ordered layered components between 700 °C and 900 °C enhances electrochemical performance. The study opens new avenues for designing next-generation Mn-rich cathode materials by fine-tuning the synthesis conditions as well as the composition and structure of LxS-LMNO electrodes.

energy storage↗
DOE OSTI2026

Energetic Copolymers From LLM-105 and Aliphatic Isocyanates

Energetic polymers typically feature fuel-rich backbones with pendant oxidizing explosophores, which are used to modify pressure and temperature characteristics in energetic formulations. However, these pendant explosophores generally increase sensitivity and decrease the thermal stability of the polymer in the condensed phase. Direct polymerization of insensitive high explosives (IHEs) bearing polymerizable functionalities, such as amines, provides a synthetic platform for deriving tunable energetic materials. Here, this work demonstrates the first direct polymerization of the IHE 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105) with aliphatic isocyanate comonomers to yield an energetic copolyurea (PUa1) and an energetic copolyurea network (PUa2). 1 H and 13 C nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy (FTIR) confirm copolyurea synthesis. PUa1 exhibited branching reactions commonly found in polyurea syntheses, and these side reactions were suppressed in PUa2 through use of a polyfunctional isocyanate. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) demonstrate the energetic IHE based polyurea preserves the energetic decomposition of the parent monomer LLM-105 in PUa1 (1203 J g −1 ) and PUa2 (687 J g −1 ), albeit with reduced thermal stability with peak decomposition temperatures of 235°C and 233°C, respectively. This work presents a new approach for generating energetic polymers from IHE cores with tunable energetic, thermal, and structural characteristics.

Chemistry↗
DOE OSTI2026

Digital Twin-Enabled Adaptive Control for Hydroelectric Systems: Turbine Governor and Voltage Regulation

This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2025

Precursor reaction pathway leading to BiFeO 3 formation: insights from text-mining and chemical reaction network analyses

BiFeO 3 (BFO) is a next-generation non-toxic multiferroic material with applications in sensors, memory devices, and spintronics, where its crystallinity and crystal structure directly influence its functional properties. Designing sol–gel syntheses that result in phase-pure BFO remains a challenge due to the complex interactions between metal complexes in the precursor solution. Here, we combine text-mined data and chemical reaction network (CRN) analysis to obtain novel insight into BFO sol–gel precursor chemistry. We perform text-mining analysis of 340 synthesis recipes with the emphasis on phase-pure BFO and identify trends in the use of precursor materials, including that nitrates are the preferred metal salts, 2-methoxyethanol (2 ME) is the dominant solvent, and adding citric acid as a chelating agent frequently leads to phase-pure BFO. Our CRN analysis reveals that the thermodynamically favored reaction mechanism between bismuth nitrate and 2ME interaction involves partial solvation followed by dimerization, contradicting assumptions in previous literature. We suggest that further oligomerization, facilitated by nitrite ion bridging, is critical for achieving the pure BFO phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Heterogenous catalysis for oxygen tolerant photoredox atom transfer radical polymerization and small-molecule dehalogenation

Heterogeneous photocatalysts (PCs) have garnered attention for their sustainability and cost-effectiveness. Despite the existence of various types of these PCs, their synthesis often involves complex, multi-step procedures and laborious purification. Herein, we propose a simple method for attaching small-molecule photocatalytic species onto crosslinked 3-D polymer networks as insoluble scaffolds to create robust heterogeneous PCs. The highly swellable poly(ethylene glycol)-based ChemMatrix (CM) resin, known for its amphiphilic properties and high functional group loading, facilitated the covalent immobilization of the photoredox dye Eosin Y (EY), but also streamlined functionalization with Ir( III ) complexes. The resulting heterogeneous CM-EY demonstrated efficient photocatalytic performance in open-to-air dual photoredox catalysis of atom transfer radical polymerization (photo-ATRP) under green light. This was confirmed by the well-controlled synthesis of polymers with molecular masses ranging from 20 kDa to 300 kDa and low dispersities. Furthermore, CM-EY exhibited excellent photostability and recyclability over multiple cycles of ATRP. The heterogeneous catalysis of photo-ATRP provided high temporal control and enabled benign conditions for synthesizing protein-polymer hybrids (PPH). When combined with the initiator-modified CM (CM-BIB), CM-EY facilitated the solid-phase synthesis of homopolymers and block copolymers with recyclable performance. However, the coordinatively bound Ir@CM showed decreased catalytic activity and efficiency toward photoredox dehalogenation due to the leaching of active species during recycling. This study highlights the advantages of the covalent linking of catalysts to solid supports over non-covalent interactions, underscoring the potential of functionalized polymer resin as a promising scaffold. Such an approach offers customization and tunability, presenting opportunities for innovation in green chemistry.

Kapil, Kriti↗
DOE OSTI2026

Evaluation of Iron‐Phosphate Glass–Ceramic Waste Form for Electrorefiner Salt Waste Simulant Dechlorinated With Phosphoric Acid

The importance of glass and glass–ceramic nuclear waste forms has been reaffirmed in recent years by the growing interest in nuclear power as a reliable energy source. Determination of processing methods for the disposal of halide-containing wastes will be essential for the advancement of nuclear technologies such as non-aqueous fuel reprocessing. Phosphate-based dechlorination and subsequent vitrification of radioactive salt waste into an iron-phosphate waste form have been identified as a potential processing scheme for electrochemical processing waste. The impact of H 3 PO 4 -based dechlorination of complex salt mixtures on the vitrification process and structure of the final iron-phosphate waste form has not yet been investigated. In this work, iron-phosphate glass–ceramics were made from simulant salt waste (48LiCl–33KCl–19NaCl mol%) dechlorinated with the H3PO4-based method. The glass-forming region was compared to that of traditionally prepared Na 2 O–Fe 2 O 3 –P 2 O 5 systems. For a candidate glass-forming composition, the processing scheme presented here was determined to favor Fe 3+ species. The O/P molar ratio was consistent for the candidate composition when dechlorinated at 400°C and 600°C in air and argon environments, indicating glass network connectivity was maintained despite variations in processing parameters. The results presented here validate processing schemes requiring iron-phosphate waste form synthesis following H 3 PO 4 -based dechlorination.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗