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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sub-volt conversion of activated biochar and water for H 2 production near equilibrium via biochar-assisted water electrolysis

Sluggish water oxidation reactions limit water electrolysis for H 2 production, which can be alleviated by the use of carbon-based ma- terials like agricultural wastes as reducing agents. Biochar from such biomass can reduce equilibrium cell potentials at standard condi- tions from 1.23 V to 0.21 V by avoiding direct water splitting at the anode. However, some challenges hinder biochar oxidation, including poor biochar binding, electrode caking, and surface passivation. We find that enhanced C/O ratio, crystallinity, and negative zeta potential improve biochar oxidation kinetics at mod- erate temperatures. Smaller particle sizes and better mixing pre- vent electrode caking, enhancing biochar stability. Here, we report sub-volt biochar-coupled H 2 production, often referred to as a bio- char-assisted water electrolysis (BAWE), yielding 250 mA/g cat H 2 current at 100% Faradaic efficiency. Over 1 mA current was observed at a near-equilibrium cell potential of 0.2 V cell potential. Using a single-junction solar cell-powered BAWE, 15 mA H 2 is generated at 1 Sun, resulting in 4.8% solar-to-hydrogen efficiency, equivalent to 35% when the energy of H 2 relative to H 2 O (without biochar) is assumed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Comprehensive defect suppression in perovskite nanocrystals for high-efficiency light-emitting diodes

Electroluminescence efficiencies of metal halide perovskite nanocrystals (PNCs) are limited by a lack of material strategies that can both suppress the formation of defects and enhance the charge carrier confinement. Here we report a one-dopant alloying strategy that generates smaller, monodisperse colloidal particles (confining electrons and holes, and boosting radiative recombination) with fewer surface defects (reducing non-radiative recombination). Doping of guanidinium into formamidinium lead bromide PNCs yields limited bulk solubility while creating an entropy-stabilized phase in the PNCs and leading to smaller PNCs with more carrier confinement. Further, the extra guanidinium segregates to the surface and stabilizes the undercoordinated sites. Furthermore, a surface-stabilizing 1,3,5-tris(bromomethyl)-2,4,6-triethylbenzene was applied as a bromide vacancy healing agent. The result is highly efficient PNC-based light-emitting diodes that have current efficiency of 108 cd A –1 (external quantum efficiency of 23.4%), which rises to 205 cd A –1 (external quantum efficiency of 45.5%) with a hemispherical lens.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Distributed Finite-Time Termination for Consensus Algorithm in Switching Topologies

Here, in this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value; however, the asymptotic convergence of these algorithms poses a challenge in practical settings where the response from agents is required in finite time. To this end, we propose a maximum-minimum protocol that propagates the global maximum and minimum values of agent states (while running the consensus algorithm) in the network. This article focuses on establishing that the global maximum and minimum values are strictly monotonic even for a dynamic topology, and they can be used to distributively ascertain the closeness to convergence in finite time. We rigorously show that each node can have access to the global maximum and minimum by running the proposed maximum-minimum protocol to realize a finite-time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS socket.io server.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DISTRI: Distributed Multi-Facility HPC Simulator (DISTRI) v2.1

DISTRI is an advanced network simulator designed for multi-facility computational infrastructures with agentic behavior. It simulates HPC facilities where computational resources act as autonomous agents, making intelligent decisions about job scheduling, load balancing, and resource allocation. The simulator focuses on developing and testing decentralized algorithms that promote resilience and efficiency in multi-facility environments. Key Features: - Agentic Resource Behavior: Processors and DTNs act as autonomous agents with decision-making capabilities - Pheromone-Based Load Balancing: Decentralized load balancing inspired by ant colony optimization - Dual Topology Support: Mesh (normal operations) and Dumbell (network testing) topologies - Comprehensive TCP Simulation: Realistic TCP implementations with multiple congestion control algorithms - Failure Resilience Testing: Processor failure simulation with automatic job reassignment - Extensive Visualization: Detailed performance analysis and metrics collection - Research-Ready: Designed for algorithm development and benchmarking

Bez, Jean Luca [Lawrence Berkeley National Laborat↗
DOE OSTI2024

Photocatalytic Generation of Singlet Oxygen by Graphitic Carbon Nitride for Antibacterial Applications

Carbon-based functional nanocomposites have emerged as potent antimicrobial agents and can be exploited as a viable option to overcome antibiotic resistance of bacterial strains. In the present study, graphitic carbon nitride nanosheets are prepared by controlled calcination of urea. Spectroscopic measurements show that the nanosheets consist of abundant carbonyl groups and exhibit apparent photocatalytic activity under UV photoirradiation towards the selective production of singlet oxygen. Therefore, the nanosheets can effectively damage the bacterial cell membranes and inhibit the growth of bacterial cells, such as Gram-negative Escherichia coli, as confirmed in photodynamic, fluorescence microscopy, and scanning electron microscopy measurements. The results from this research highlight the unique potential of carbon nitride derivatives as potent antimicrobial agents.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Functionalized Silicon Particles for Enhanced Half- and Full-Cell Cycling of Si-Based Li-Ion Batteries

Vinylene carbonate (VC) and polyethylene oxide (PEO) have been investigated as functional agents that mimic the solid electrolyte interphase (SEI) chemistry of silicon (Si). VC and PEO are known to contribute to the stability of Si-based lithium-ion batteries as an electrolyte additive and as a SEI component, respectively. In this work, covalent surface functionalization was achieved via a facile route, which involves ball-milling the Si particles with sacrificial VC and PEO. Thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), and magic angle spinning nuclear magnetic resonance (MAS NMR) spectroscopy indicate that the additives are strongly bound to Si. In particular, MAS NMR shows Si–R or Si–O–R groups, which confirm functionalization of the Si after milling in VC or PEO. Particle size analysis by dynamic light scattering reveals that the additives facilitate particle size reduction and that the functionalized particles result in more stable dispersions based on zeta potential measurements. Raman mapping of the electrodes fabricated from the VC and PEO-coated active material with a polyacrylic acid (PAA) binder reveals a more homogenous distribution of Si and the carbon conductive additive compared to the electrodes prepared from the neat Si. Furthermore, the VC-milled Si strikingly exhibited the highest capacity in both half- and full-cell configurations, with more than 200 mAh g –1 measured capacity compared to the neat Si in the half-cell format. This is linked to an improved electrode processing based on the Raman and zeta potential measurements as well as a thinner SEI (with more organic components for the functionalized Si relative to the neat Si) based on XPS analysis of the cycled electrodes. In conclusion, the effect of binder was also investigated by comparing PAA with P84 (polyimide type), where an increased capacity is observed in the latter case.

25 ENERGY STORAGE↗
DOE OSTI2023

High reactivity of mesoporous CeO 2 to dissociate chemical warfare agent sarin

We perform spectroscopic surface characterization on mesoporous ceria (CeO 2 ) under exposure of sarin gas, also known as GB. We find mesoporous CeO 2 displays extraordinary reactivity towards GB dissociation under high vacuum. The spectroscopic results combined with density functional theory (DFT) calculations suggest that the cleavage of the P–F and P–OR bonds are both possible pathways for GB to dissociate on the CeO 2 surfaces. The modeling reveals differences in dissociation pathways of GB on the crystallographic (111)- and (110)-surfaces of ceria. Here we also report some dissociation of GB on mesoporous CeO 2 under ambient conditions. The finding that mesoporous CeO 2 can degrade GB under mild conditions is encouraging and implies the strong potential of ceria-based materials to be applied in combating chemical warfare agents related to the GB series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Randomization-Based, Zero-Trust Cyberattack Detection Method for Hierarchical Systems

This paper demonstrates a novel randomization-based approach for verifying power system control signals with application to detecting cyberattacks. We consider fully connected hierarchical systems containing multiple local agents and a global "trust" agent. The global agent uses a time-varying randomized assignment scheme to identify corrupt network links based on principles of zero trust and majority rule. To evaluate the performance of this detection approach, we implement our algorithm in MATLAB and run it against nearly 43 million unique attack scenarios spanning a range of system sizes. For each scenario, the algorithm determines whether the identified corruptions satisfy a set of validity constraints reflecting network topology and uses that result to say whether the recovered state value for one or more local agents is malicious. We compare the algorithm's determination to the true state of the system to assess performance and find that classification accuracy converges to 100% as system size increases, suggesting that the validity constraints become more difficult to satisfy for larger systems. We further explore the scenarios that evade detection to understand practical implications for employing this detection approach.

cybersecurity↗
DOE OSTI2023

A Simplified Approach Based on Cellular Automata for Describing Direct Reduced Iron Production in Different Reducing Conditions

A quick computation approach based on cellular automata is developed and implemented to describe the reduction of iron ore pellets by a mixture of reducing agents featured by different H 2 /CO ratios. The evolution of oxygen concentration inside the pellet is followed from the beginning to the end of contact between reducing agent and pellet. The variation of thermal state of pellets and gas mixture is computed based on their initial temperature, considering the heat involved and the convective heat exchange between pellet and gas mixture. The use of cellular automata and finite‐difference method to solve the diffusion equation point out the absence of any diffusion coefficient value, allowing to make the model fit the experimental trial, because the problem is that it is not ruled just by diffusion but also by the concentration variation of reducing agent inside the pellet due to porosity increasing during reduction. The updating of the reducing agents concentration implies a sharp decrease of oxygen concentration that the cellular automata model considers. The developed model is able to provide the in‐line control of reduction process and could be used to adjust the chemical concentration and temperature of injected reducing agents.

Metallurgy & Metallurgical Engineering↗
DOE OSTI2025

Deep Reinforcement Learning-Based Control of Energy Storage for Interarea Oscillation Damping

With the increasing electricity consumption and lack of transmission investment, today's power systems are operated much closer to their limits, raising concerns of inter-area oscillations that deteriorate the system stability. Here, this article presents a novel energy storage placement and control approach for enhanced damping of interarea oscillations. Combining the residual analysis and dominant mode analysis, we are able to identify the advantageous locations for placing energy storage that achieve improved damping performance. To overcome the challenges, such as fixed control parameters and insufficient damping, we propose to use a deep reinforcement learning-based approach for energy storage control. A state-of-the-art guided surrogate-gradient-based evolutionary strategy is used to train a learning agent in a robust, efficient, and reproducible manner. Parallel computing is also adopted to speed up the training process. The proposed strategy has been tested on both medium and large-scale systems. The proposed methods have demonstrated their effectiveness in mitigating various interarea oscillations within a timeframe of 20 s, thereby averting system collapse and enhancing power grid stability effectively.

25 ENERGY STORAGE↗
DOE OSTI2026

Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays

This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.

deep reinforcement learning↗
DOE OSTI2025

Chemical Redox Agent-Driven Noncorrosive Formation of Nanoporous Mg Structures for Advanced Hydrogen Storage

Light metal-based nanomaterials are widely used for energy storage due to their high energy density and surface-to-volume ratio. However, their high reactivity is paradoxically both the source of advantageous properties and a hurdle to the fabrication of stable nanostructures. Here, in this study, we demonstrate the formation of nanoporous Mg via chemical redox agent-driven dealloying, which ensures minimized surface passivation and results in fine nanostructures with <50 nm of interconnected metallic ligament despite the labile chemical properties of Mg. The thin passivation layer protects the metallic ligaments from severe coarsening by suppressing surface diffusion. The hydrogen storage performance of nanoporous Mg is investigated as an exemplar for energy applications, and the hydrogen ab/desorption kinetics is substantially enhanced compared to other nano-Mg with similar dimensions. Mesoscale simulations highlight the significance of the bicontinuous structure compared to the particle-like counterpart. This work offers valuable insights into the unexplored realm of reactive metal-based nanoporous structures, highlighting their potential for sustainable energy storage and carrier media.

08 HYDROGEN↗
DOE OSTI2022

DNA Sequence-Based Identification of Fusarium : A Work in Progress

Accurate species-level identification of an etiological agent is crucial for disease diagnosis and management because knowing the agent’s identity connects it with what is known about its host range, geographic distribution, and toxin production potential. This is particularly true in publishing peer-reviewed disease reports, where imprecise and/or incorrect identifications weaken the public knowledge base. This can be a daunting task for phytopathologists and other applied biologists that need to identify Fusarium in particular, because published and ongoing multilocus molecular systematic studies have highlighted several confounding issues. Paramount among these are: (i) this agriculturally and clinically important genus is currently estimated to comprise more than 400 phylogenetically distinct species (i.e., phylospecies), with more than 80% of these discovered within the past 25 years; (ii) approximately one-third of the phylospecies have not been formally described; (iii) morphology alone is inadequate to distinguish most of these species from one another; and (iv) the current rapid discovery of novel fusaria from pathogen surveys and accompanying impact on the taxonomic landscape is expected to continue well into the foreseeable future. To address the critical need for accurate pathogen identification, our research groups are focused on populating two web-accessible databases (FUSARIUM-ID v.3.0 and the nonredundant National Center for Biotechnology Information nucleotide collection that includes GenBank) with portions of three phylogenetically informative genes (i.e., TEF1, RPB1, and RPB2) that resolve at or near the species level in every Fusarium species. The objectives of this Special Report, and its companion in this issue ( Torres-Cruz et al. 2022 ), are to provide a progress report on our efforts to populate these databases and to outline a set of best practices for DNA sequence-based identification of fusaria.

Plant Sciences↗
DOE OSTI2020

Detection of active and latent infections with microfluidic devices and systems thereof

The present invention relates to methods of detecting one or more targets of interest in a sample. In one instance, the target can be correlated to an active infection (e.g., by a virus and/or a bacterium). Methods can include treating the sample with a dissociation agent, thereby releasing the target of interest for more accurate detection (e.g., by use of a sedimentation-based centrifugal microfluidic devices). Also described herein are microfluidic devices and systems for use with a dissociation agent.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Protection against Chemical Warfare Agents and Biological Threats Using Metal–Organic Frameworks as Active Layers

The SARS-CoV-2 pandemic outbreak and the unfortunate misuse of toxic chemical warfare agents (CWAs) highlight the importance of developing functional materials to protect against these chemical and pathogen threats. Metal–organic frameworks (MOFs), which comprise a tunable class of crystalline porous materials built from inorganic nodes and organic linkers, have emerged as a class of heterogeneous catalysts capable of rapid detoxification of multiple classes of these harmful chemical or biological hazards. In particular, zirconium-based MOFs (Zr-MOFs) feature Lewis acidic nodes that serve as active sites for a wide range of catalytic reactions, including the hydrolysis of organophosphorus nerve agents within seconds in basic aqueous solutions. In addition, postsynthetic modification of Zr-MOFs enables the release of active species capable of reacting with and deactivating harmful pathogens. Despite this impressive performance, utilizing Zr- MOFs in powder form is not practical for application in masks or protective uniforms. To address this challenge, our team sought to develop MOF/fiber composite systems that could be adapted for use under realistic operating conditions to protect civilians, military personnel, and first responders from harmful pathogens and chemical warfare agents. Over the last several years, our group has designed and fabricated reactive and biocidal MOF/fiber composites that effectively capture and deactivate these toxic species. In this Account, we describe the evolution of these porous and reactive MOF/fiber composites and focus on key design challenges and considerations. First, we devised a scalable method for the integration of Zr-MOFs onto textile substrates using aqueous precursor solutions and without using pretreated textiles, highlighting the potential scalability of this method. Moving beyond standard textiles, we also developed a microbial synthesis strategy to prepare hierarchically porous MOF/bacterial cellulose nanofiber composite sponges that can both capture and detoxify nerve agents when exposed to contaminated gas flows. The mass loading of the MOF in the nanofibrous composite sponge is up to 90%, affording higher work capacities compared to those of textile-fiber-based composites with relatively lower MOF loadings. Next, we demonstrated that heterogeneous polymeric bases are suitable replacements for volatile liquid bases typically used in solution-phase reactions, and we showed that these composite systems are capable of effectively hydrolyzing nerve agents in the solid state by using only water that is present as humidity. Moreover, incorporating a reactive dye precursor into the composite affords a dual function sensing and detoxifying material that changes color from white to orange upon reaction with the byproduct following nerve agent hydrolysis, demonstrating the versatility of this platform for use in decontamination applications. We then created chlorine-loaded MOF/fiber composites that act as biocidal and reactive textiles that are capable of not only detoxifying sulfur-mustard-based chemical warfare agents and simulants but also deactivating both bacteria and the SARS-CoV-2 virus within minutes of exposure. Lastly, we synthesized a mixed-metal Ti/Zr-MOF coating on cotton fibers to afford a photoactive biocidal cloth that shows fast and broad-spectrum biocidal performance against viruses and Gram-positive and Gram-negative bacteria under visible light irradiation. Given the tunable, multifunctional nature of these MOF/fiber composites, we believe that this Account will offer new insights for the rational design and preparation of functional MOF/fiber composites and pave the way toward the development of next-generation reactive and protective textiles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distillable amine-based solvents for effective pretreatment of multiple biomass feedstocks

Exploring the potential of advanced distillable solvents as efficient biomass pretreatment agents is critical for biorefineries, enhancing fermentable sugar yields while enabling solvent recovery and recycling without suffering significant losses. Here, we employ distillable amine-based solvents for pretreating a wide range of lignocellulosic feedstocks, aiming to facilitate the industrial release of fermentable sugars from diverse feedstocks through enzymatic hydrolysis. Twenty-two diverse feedstocks, sourced from different geographical regions and representing various biomass categories, were surveyed for chemical (mainly carbohydrates and lignin) and lignin (S, G, and H units) profiles. Several solvents, including ethanolamine, ethanolammonium acetate, butylamine, butylammonium acetate, and triethylamine, were tested for the pretreatment of eight selected biomasses. Among these solvents, butylamine emerged as the most effective due to its favorable sugar release, excellent solvent removal rate, and low boiling point, facilitating solvent recovery and recycling. Extending butylamine pretreatment to all 22 feedstocks demonstrated desirable sugar yields and highly efficient solvent removal in the majority of the biomass sources tested. Agricultural residues and their mixtures showed particularly favorable sugar release. Despite minimal changes in cellulose crystallinity, XRD characterization of sorghum, poplar, and pine before and after butylamine pretreatment showed a decrease in intensity and a slight shift of certain peaks, indicating alterations in cellulose structure. Fourier-transform infrared spectroscopy and thermogravimetric analysis analyses suggested disruption of biomass linkages in hemicellulose and lignin, enhancing enzymatic digestibility. Scale-up experiments of the mixed agricultural feedstocks in a 1 L Parr reactor achieved over 90% glucose liberation and more than 99% butylamine removal, highlighting the scalability of the method. The resulting hydrolysates supported the growth of diverse bacterial and fungal strains, indicating downstream compatibility with commercial fermentation processes. This study presents butylamine as an effective, recoverable pretreatment solvent for a wide range of lignocellulosic feedstocks, offering a promising solution to key biorefinery challenges. The demonstrated scalability and compatibility with various biomass types and blends underscore its potential for industrial application, advancing sustainable biofuel and biochemical production.

biomass composition↗
DOE OSTI2024

Self-healing carbon fiber/epoxy laminates with particulate interlayers of a low-melting-point alloy

In order to prolong the service life of fiber-reinforced polymer composites, the implementation of self-healing ability with the micro-encapsulated healing agent has been extensively studied. However, such microcapsule-based self-healing composites typically suffer from degraded mechanical properties due to the liquid-phase inclusions, thereby limiting their proliferation. Here, in this study, a low-melting-point alloy is utilized as the particulate inclusions of carbon fiber/epoxy laminated composites. Field's Metal particles (melting point: 62 °C) are distributed between woven carbon fiber preforms followed by the resin impregnation to realize laminated composites with a Field's Metal-enhanced interlayer(s). The resulting laminated composites demonstrate the autonomic repair of interlaminar failure with a 40 % of healing efficiency. Most of all, the mechanical properties of these self-healing laminated composites are comparable to the conventional laminated composites attributed to the rigid inclusions that can be compressed to increase the fiber volume. Since the Field's Metal particle inclusions can bestow polymer composites with self-healing ability and the potential increase in mechanical properties, Field's Metal-enhanced fiber-reinforced polymer composites are expected to unlock the practical utility of self-healing composites.

A. polymer-matrix composites (PMCs)↗