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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 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↗
DOE OSTI2025

Preparing for successful protein crystallization experiments

Crystal-based structural methods, including X-ray crystallography, are frequently utilized for the determination of high-resolution structures of biomolecules. All crystal-based diffraction methods first require the preparation of biomolecular crystals, and careful sample preparation for crystallization experiments can increase the frequency of success. In this article, strategies to optimize factors that can impact crystallization are presented, from which buffers and reducing agents are most favorable to which crystallization techniques could be used.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Zinc against COVID-19? Symptom surveillance and deficiency risk groups

A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Deactivation of SARS-CoV-2 by Boronic Acid-Functionalized Polymer

Boronic acid-modified polymers (BAMPs) can interact with glycoproteins and other glycosylated compounds through covalent binding of the boronic acid moieties to saccharide residues. As a first step toward evaluating the utility of BAMPs as SARS-CoV-2 antiviral agents, this COVID-19 rapid response LDRD was intended to examine the effect of BAMPs on SARS-CoV-2 spike glycoprotein and its subsequent binding with ACE2 receptor protein. Multiple different approaches were attempted in order to determine whether BAMPs based on poly(ethylene glycol) and poly(ethylenimine) bind the spike protein, but failed to produce a definitive answer. However, two different enzyme-linked immunosorbent assays clearly showed no discernable effect of boronic acid in inhibiting spike-ACE2 binding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Phosgene Neutralization Chemistry for the Explosive Destruction System (EDS)

This report summarizes activities at Sandia National Laboratories as part of the Explosive Destruction System (EDS) Phase 3 (P3) System design. An exploration of chemical neutralization strategies of phosgene was conducted for safe disposal of recovered mortars and M79 1000 lb. bombs filled with carbonyl dichloride "phosgene" or "CG agent" (molecular formula = COCl 2 ). The incumbent strategy utilized aqueous sodium hydroxide was found to be the worst-case scenario, producing enough CO 2 gas that would cause an unacceptable pressure and temperature spike. Several chemical neutralization strategies were evaluated based on criteria set by the operating envelope of the P3 design. In the end, it was determined that a pure solution of N-methyl ethanolamine (MeEA) or 90% monoethanolamine (MEA)(aqueous) provided the best balance reaction profile, cost, and safety.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electrochemical-assisted leaching of active materials from lithium ion batteries

The development of a circular economy for lithium-ion batteries (LIB) is essential to realize decarbonization and an electrified energy market. However, the current commercial operations that recover valuable constituents from LIBs require significant energy and reagents, which create toxic emissions and additional waste. We report an electrochemical-based method for leaching valuable metals from the active materials of mixed shredded LIBs. In this process, the use electrons, as green reagent, allows the use and regeneration of Fe 2+ in low concentrations as substitute for hydrogen peroxide as a reducing agent. Leaching in a membrane separated two-compartment electrochemical cell contributes to decrease the acid requirements as H+ can be generated electrochemically. With this design, leaching efficiencies over 96% for the active metals (Li, Co, Mn, and Ni) were demonstrated at pulp densities up to 240 g/L. Copper present in the active material is electrowon and recovered separately. Preliminary cost analyses demonstrate ca. 80% reduction in energy and chemical costs as compared to traditional hydrometallurgical routes.

25 ENERGY STORAGE↗