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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optically tunable mesoscale CdSe morphologies via inorganic phototropic growth

Inorganic phototropic growth using only spatially conformal illumination generated Se–Cd films that exhibited precise light-defined mesoscale morphologies including highly ordered, anisotropic, and periodic ridge and trench nanotextures over entire macroscopic substrates. Growth was accomplished via a light-induced electrochemical method using an optically and chemically isotropic solution, an unpatterned substrate, and unstructured, incoherent, low-intensity illumination in the absence of chemical directing agents or physical templates and masks. The morphologies were defined by the illumination inputs: the nanotexture long axes aligned parallel to the optical E-field vector, and the feature sizes and periods scaled with the wavelength. Optically based modeling of the growth closely reproduced the experimental results, confirming the film morphologies were fully determined by the light–matter interactions during growth. Solution processing of the Se–Cd films resulted in stoichiometric, crystalline CdSe films that also exhibited ordered nanotextures, demonstrating that inorganic phototropic growth can effect tunable, template-free generation of ordered CdSe nanostructures over macroscopic length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Ultrathin, ternary alloy PtRuFe nanowires, and methods of making same

The present invention provides a method of producing ternary metal-based nanowire networks. The method comprises combining an aqueous mixture of a platinum hydrate, a ruthenium hydrate, and an iron hydrate with a solution of hexadecyltrimethylammonium bromide in chloroform to form an inverse micellar network; adding a reducing agent to reduce metal ions within the inverse micellar network; and isolating the nanowires. The relative amounts of the platinum, ruthenium and iron in the mixture correlate to the atomic ratio of the platinum, ruthenium and iron in the ternary nanowires. The diameters of the ternary nanowires are from about 0.5 nm to about 5 nm.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Stabilizing a low temperature phase change material based on Glaubers salt

The aim of this research is to enhance the performance of Glauber's salt (sodium sulfate decahydrate, SSD) as a phase change material (PCM) for thermal energy storage applications, as well as for shipping of temperature-sensitive materials. The study investigates the effects of modifying SSD with potassium chloride (KCl) and ammonium chloride (NH 4 Cl) to achieve lower phase transition temperatures of between +6 °C and + 13 °C. Sodium polyacrylate (PAAS) is employed as a thickening agent to prevent phase separation, and borax is used as a nucleating agent to suppress supercooling. Various ratios of KCl:NH 4 Cl are tested, and the impact of PAAS concentration on phase segregation is explored. The results show that a 2 wt% concentration of PAAS effectively prevents phase separation. Samples with a KCl:NH 4 Cl ratio of 1:1.83 exhibit stable phase transition behavior and maintain latent heat values within the range of 130–140 J/g after 30 thermal cycles. Maintaining a total KCl and NH 4 Cl proportion of 10 wt% is crucial to achieve the desired lower melting temperature. As a result, the study highlights the significance of thermal cycling in improving the stability of the PCM. The optimized SSD-based eutectic PCM formulations hold promise for applications in the cold chain industry.

25 ENERGY STORAGE↗
DOE OSTI2026

GridCoPilot for Thermal Events: An LLM-Based Platform for Power Grid Reliability Analysis

Large Language Models show promise for translating natural language into database queries, but deploying such systems in safety-critical domains requires high reliability. We present an application of GridCoPilot to thermal event analysis (heatwaves and coldwaves) that affect power grid reliability. Our approach uses a LangChain SQL Agent to translate natural language queries into auditable SQL statements, with deterministic visualization routines that parse the structured query results. We introduce structural framing as a design principle, we integrate a NERC-region-level event library with county-level meteorology and decompose the combined data into three relational tables (event metadata, county-level event details, and a county-to-NERC subregion mapping), using prompt-guided joins to direct the model toward correct multi-table queries. For two core analytical patterns (identifying worst events by region and by region-year), the system achieved 100% SQL accuracy across all 16 NERC subregions and both event types (64 queries total). These results validate the approach for target use cases, though performance on diverse natural language formulations requires further investigation. We discuss design trade-offs, failure modes including JSON output truncation, and pathways for extending this approach to other hazard domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Developing Microalgae-Based Non-Isocyanate Polyurethane Foam Materials

Polyurethane is a versatile material with a wide range of applications in the production of foam products such as car seats, mattresses, footwear, insulation foam, and more. The traditional process for producing polyurethane foam involves the fast reaction of isocyanate with water, which generates CO2 as a blowing agent. However, the use of isocyanate has raised concerns due to its potential for causing allergic reactions and aspiration symptoms. This has led to increased regulation of isocyanate, leading to a growing interest in developing alternatives. Non-isocyanate polyurethane (NIPU) has emerged as a promising alternative to traditional toxic PU production. The most promising approach for producing NIPU is the aminolysis of cyclic carbonate with diamine. The NIPU produced through this process has a polyhydroxyurethane structure, which is similar to traditional PU chemistry. We have demonstrated that microalgal oil is a favored feedstock, as it can provide high reactivity for industrial applications. However, the NIPU production process does not generate gas, and a blowing agent is required to achieve the desired foam structure. One of the challenges in producing NIPU foam is to match the bubble formation and curing rates. We have developed a novel approach to producing NIPU foam that utilizes carbamate as a bifunctional blowing and crosslinking agent. We have used vegetable oil and microalgal oil as feedstocks to produce various NIPU for characterization. The development of microalgal NIPU provides an exciting opportunity for reducing the environmental impact of foam production and improving the safety of the materials used in the production process. The production of value-added NIPU foam can also drive down the cost of microalgal biorefinery, advancing the commercialization of microalgal biofuel.

algae↗
DOE OSTI2021

Investigation of interlayer adhesion of 3D printable cementitious material from the aspect of printing process

The safety of structure printed by extrusion-based 3D concrete printing (3DCP) is significantly influenced by interlayer bonding, which is governed by fresh material properties and processes, including mixing, printing, and post-processing phases. Much literature focused on improving interlayer bond strength via material tailoring/bonding agent addition but gave insufficient attention to other phases' impacts. This study investigates the effects of parameters on interlayer bond strength from different phases in 3DCP, including superplasticizer dosage, printing speed, and curing condition. In the mixing phase, the superplasticizer dosage was increased to reduce I{sub thix} and increase surface moisture content (SMC), consequently improving interface microstructure and interlayer bond strength. In the printing phase, similar results were observed by increasing pumping speed to increase material shear rate. In the post-processing phase, proper curing also improved interlayer bond strength. While SMC has a critical impact on interlayer bond strength, material thixotropic index (I{sub thix}) probably has a stronger influence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Powertrain Autoresearch (TETA Autoresearch: RouteE Powertrain Experiments) [SWR-26-090]

Powertrain Autoresearch is an experiment repository based on the template set in teta-autoresearch containing branch history of LLM workers investigating powertrain ML model improvements. This repository contains an instance of teta-autoresearch applied to ML model exploration for RouteE Powertrain. An AI agent and human-in-the-loop iteratively explored feature space, ML model type, and hyperparameter spaces in an attempt to improve over existing models. The repo catalogs the result for review at USRSE 2026 Conference. Related: https://github.com/NatLabRockies/teta-autoresearch.

Reinicke, Nicholas [National Laboratory of the Roc↗
DOE OSTI2026

Scalable Fabrication of a Fibrous Amine-functionalized Matrix (FAM) Sorbent for Critical Mineral Recovery

We report a novel flat sheet Fibrous Amine-functionalized Matrix (FAM) sorbent platform designed for efficient and selective capture of CM from dilute solutions. The FAM sorbent features crosslinked amine microfilms coated onto/within a glass fiber matrix, providing fast mass transfer and excellent mechanical stability. Systematic batch and flow-through tests with FAM revealed rapid metal uptake kinetics and high capacity for representative species, achieving ~90 mg/g of Gallium, ~100 mg/g of Cobalt, and ~90 mg/g for Neodymium. Moreover, multiple eluents, including mineral acids and complexing agents, enabled highly effective desorption of adsorbed metals, demonstrating the feasibility of regenerating FAM sorbents. Importantly, tests with authentic coal ash leachate demonstrated strong selectivity toward U.S. Department of Energy (DOE)-listed CM and rare earth elements over abundant base cations, confirming the robustness of FAM in realistic complex solutions. The flat sheet geometry was amenable to scaling into durable spiral wound modules, highlighting the potential for future regeneration and reuse. This work establishes FAM sorbents as a promising platform for the recovery of CM from wastewaters, advancing both resource sustainability and environmental stewardship.

critical mineral recovery↗
DOE OSTI2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (F v /F m ) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of F v /F m values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

oats↗
DOE OSTI2026

CodeScribe Agent

SF-26-086 CodeScribe introduces a structured, multi-stage pipeline that combines deterministic program analysis with LLM-powered translation to enable incremental, testable Fortran-to-C++ migration. First, `code-scribe index` traverses the project directory tree and produces `scribe.yaml` metadata files recording all modules, subroutines, and functions at each level, giving the LLM accurate structural context instead of a hallucinated codebase model. Second, `code-scribe draft` performs the deterministic portion of translation — converting Fortran types to C++ equivalents, replacing `use` statements with `#include` and `using namespace` directives, and detecting constructs requiring special handling — while embedding`scribe-prompt` annotations that guide the LLM through non-trivial cases such as statement-function-to-lambda conversions and `extern "C"` wrapper generation. Third, `code-scribe translate` applies project-specific TOML-based few-shot prompt templates and submits the composed prompt to a pluggable LLM backend (OpenAI, Anthropic, Argonne ARGO, any OpenAI-compatible endpoint, or local Hugging Face checkpoints), producing a C++ source file, a header, and a Fortran-C++ interface file for each translated routine so the codebase compiles and runs correctly throughout the migration. Beyond translation, CodeScribe includes a tool-using coding agent (`code-scribe agent`) with read, bash, edit, and write capabilities, and a bounded loop mode (`code-scribe loop`) that runs repeated stateless agent sessions over a task file with restricted tool access — enabling sustained, auditable software development workflows for broader scientific computing tasks.

Dhruv, Akash [Argonne National Laboratory (ANL), A↗
DOE OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Analysis of Orthogonal Efflux and Permeation Properties of Compounds Leads to the Discovery of New Efflux Pump Inhibitors

Optimization of compound permeation into Gram-negative bacteria is one of the most challenging tasks in the development of antibacterial agents. Two permeability barriers–the passive diffusion barrier of the outer membrane (OM) and active drug efflux–act synergistically to protect cells from the antibacterial action of compounds. In Escherichia coli (E. coli) and relatives, these two barriers sieve compounds based on different physicochemical properties that are defined by their interactions with OM porins and efflux pumps, respectively. In this study, we critically tested the hypothesis that the best substrates and inhibitors of efflux pumps are compounds that can effectively permeate the OM and are available at relatively high concentrations in the periplasm. For this purpose, we filtered a large subset of the ZINC15 database of commercially available compounds for compounds containing a primary amine, a chemical feature known to facilitate the uptake through E. coli general porins. The assembled library was screened by ensemble docking to AcrA, the periplasmic component of the AcrAB-TolC efflux pump, followed by experimental testing of the top predicted binders for antibacterial activities, efflux recognition, and inhibition. In this work, we found that the filtered primary amine library is a rich source of compounds with efflux-inhibiting activities and identified efflux pump inhibitors with novel chemical scaffolds effective against E. coli AcrAB-TolC and efflux pumps of multidrug-resistant clinical isolates of Acinetobacter baumannii. However, primary amines are not required for the recognition of compounds by efflux pumps and their efflux-inhibitory activities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Evaluation of the recombinant proteins RlpB and VacJ as a vaccine for protection against Glaesserella parasuis in pigs

Abstract Background Glaesserella parasuis , the causative agent of Glӓsser’s disease, is widespread in swine globally resulting in significant economic losses to the swine industry. Prevention of Glӓsser’s disease in pigs has been plagued with an inability to design broadly protective vaccines, as many bacterin based platforms generate serovar or strain specific immunity. Subunit vaccines are of interest to provide protective immunity to multiple strains of G. parasuis . Selected proteins for subunit vaccination should be widespread, highly conserved, and surface exposed. Results Two candidate proteins for subunit vaccination (RlpB and VacJ) against G. parasuis were identified using random mutagenesis and an in vitro organ culture system. Pigs were vaccinated with recombinant RlpB and VacJ, outer membrane proteins with important contributions to cellular function and viability. Though high antibody titers to the recombinant proteins and increased interferon-γ producing cells were found in subunit vaccinated animals, the pigs were not protected from developing systemic disease. Conclusions It appears there may be insufficient RlpB and VacJ exposed on the bacterial surface for antibody to bind, preventing high RlpB and VacJ specific antibody titers from protecting animals from G. parasuis . Additionally, this work confirms the importance of utilizing the natural host species when assessing the efficacy of vaccine candidates.

Hau, Samantha J.↗
DOE OSTI2020

HPC4Mfg with ACS

Distillation in the chemical industry accounts for roughly 10% of energy use in the U.S. While porous mass separating agents (MSAs) appear capable of achieving the same separations for a fraction of the energy, the fundamental lack of a well-understood relationship between the behavior of fluid mixtures confined in MSA pores and the selectivity of MSA-based processes presents a major barrier to their widespread industrial application. This work is the first study to systematically and self-consistently explore a range of parameters describing various molecule-material interactions in MSA-based separations. Through high performance computing (HPC), this study has yielded a fundamental understanding of the influence of confined fluid behavior on selectivity. The results also serve as a knowledge base for subsequent investigations needed to transform the framework for rational design of MSA-based separation processes. This will significantly reduce the energy required for separations central to chemical manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Privacy-Aware RAG-Enabled LLMs for Collaborative AI in Organizations

Recent advancements in Large Language Models (LLMs) based on Transformer architectures have significantly improved capabilities in natural language processing and generation. However, deploying LLMs for inter-organizational communication poses challenges, in ensuring privacy and facilitating effective collaboration. This paper introduces a novel decentralized inference meta-agent chatbot that leverages privacy-aware Retrieval-Augmented Generation (RAG)-enabled LLMs for collaborative AI communication across organizations. Built on Microsoft’s Autogen, the platform enables LLMs to autonomously refine responses, enhancing accuracy and relevance. It incorporates advanced hallucination mitigation techniques using Uptrain and a privacy-focused RAG framework that employs synthetic document generation to protect sensitive information. Comprehensive evaluations demonstrate the platform’s effectiveness in maintaining contextual relevance and stringent privacy standards, effectively addressing critical challenges in LLM-enhanced collaborative AI communication. This work represents a significant step toward secure and efficient inter-organizational collaboration using advanced generative AI technologies.

97 - MATHEMATICS AND COMPUTING↗