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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2007

Human Activity Behavior and Gesture Generation in Virtual Worlds for Long- Duration Space Missions

A virtual worlds presentation technique with embodied, intelligent agents is being developed as an instructional medium suitable to present in situ training on long term space flight. The system combines a behavioral element based on finite state automata, a behavior based reactive architecture also described as subsumption architecture, and a belief-desire-intention agent structure. These three features are being integrated to describe a Brahms virtual environment model of extravehicular crew activity which could become a basis for procedure training during extended space flight.

Sierhuis, Maarten↗
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)↗
DOE OSTI2023

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning (RL) has shown significant success in sequential decision making in fields like autonomous vehicles, robotics, marketing and gaming industries. This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple, potentially conflicting, goals like occupant comfort, energy use and grid interactivity. However, for real world applications, RL has several drawbacks like requiring large training data and time, and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks. To address these issues, an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies. This approach is successful in reducing the training time, preventing the unstable early exploration behavior and improving upon an accepted rule-based policy - all of these make RL a more practical control approach for real world applications in the domain of building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluation of 134 Ce as a PET imaging surrogate for antibody drug conjugates incorporating 225 Ac

The in vivo generator 134 Ce/ 134 La has the potential to serve as a PET imaging surrogate for both alpha-emitting 225 Ac and 227 Th radionuclides due to the unique Ce III /Ce IV redox couple and the relatively long half-life of 134 Ce. Furthermore, the purpose of this study was to demonstrate the compatibility of 134 Ce with DOTA-based antibody drug conjugates, which would act as therapeutic agents when incorporating 225 Ac.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Restorable Neutralization of Poly(acrylic acid) Binders toward Balanced Processing Properties and Cycling Performance for Silicon Anodes in Lithium-Ion Batteries

Neutralization of poly(acrylic acid) (PAA)-based binders using lithium hydroxide is a common strategy for fabricating silicon anode laminates, which improves rheological properties of slurries toward high-quality electrode laminates. However, the significantly increased basicity causes degradation of Si particles while the irreversible conversion of carboxylic acid groups to lithium carboxylates undermines the binding strength, collectively leading to adverse cycling performance of the fabricated Si anodes. Herein, a novel neutralization process for PAA binders is developed. A weak base, ammonia (NH 3 ), was discovered as a neutralizing agent that still promotes rheological response of binder solutions but results in a reduced pH increase. Interestingly, the resulting ammonium carboxylate groups may cleave during the drying process to restore the neutralized PAA (PAA-NH 3 ) binders to their pristine states. The best-performing composition of 50% neutralization (PAA-50%NH 3 ) provides comparable rheological response as a PAA-Li binder as well as much improved cycling performance. Additionally, the half-cells using the PAA-50%NH 3 binder can deliver 60% capacity retention over 100 cycles at C/3 rate, affording a 23.8% increase compared to PAA-Li half-cells. This restorable neutralization process of PAA binders represents an innovative strategy of mitigating issues from slurry processing of Si particles to achieve concurrent improvements in high-quality lamination and cycling performance.

25 ENERGY STORAGE↗
DOE OSTI2022

A Micro-Mechanical Constitutive Model to Predict Hygrothermal Aging of Cross-Linked Polymers

Abstract A multi-physics material model is presented to describe the effects of temperature, oxygen, and humidity on the constitutive response of cross-linked polymers. The effect of hygrothermal damage on the mechanical integrity of the polymer matrix can be considered as the result of damage accumulation of two independent aging mechanism namely, i) thermo-oxidative, and ii) hydrolytic aging. In order to capture the mutual effects of thermo-oxidative and hydrolytic aging, an assumption has been made that each of the aging phenomenon can be superposed to each other. In fact, each of them works independently and as a result, they can compete with each other. Utilizing the theory of network decomposition, all phenomena and their correlation were modeled and thus, the strain energy function of the polymer matrix is written with respect to four independent mechanisms, i) the shrinking original matrix that has neither been attacked by water nor oxygen, ii) conversion of the first network to two new network due to the reduction and formation of cross-links, and iii) energy loss from network degradation due to attack of the water molecules to polymer active agents. Moreover, the proposed model is micro-mechanically based and is mainly relevant on thin samples due to our underlying assumption of homogeneous diffusion of oxygen and water throughout the matrix. The model has been validated against extensive data-sets obtained from experiments we specifically designed for concept validation.

Bahrololoumi, Amir↗
NASA NTRS1994

A robot control formalism based on an information quality concept

A relevance measure based on Jaynes maximum entropy principle is introduced. Information quality is the conjunction of accuracy and relevance. The formalism based on information quality is developed for one-agent applications. The robot requires a well defined working environment where properties of each object must be accurately specified.

Ekman, A.↗
NASA NTRS1995

Use of agents to implement an integrated computing environment

Integrated Product and Process Development (IPPD) embodies the simultaneous application to both system and quality engineering methods throughout an iterative design process. The use of IPPD results in the time-conscious, cost-saving development of engineering systems. To implement IPPD, a Decision-Based Design perspective is encapsulated in an approach that focuses on the role of the human designer in product development. The approach has two parts and is outlined in this paper. First, an architecture, called DREAMS, is being developed that facilitates design from a decision-based perspective. Second, a supporting computing infrastructure, called IMAGE, is being designed. Agents are used to implement the overall infrastructure on the computer. Successful agent utilization requires that they be made of three components: the resource, the model, and the wrap. Current work is focused on the development of generalized agent schemes and associated demonstration projects. When in place, the technology independent computing infrastructure will aid the designer in systematically generating knowledge used to facilitate decision-making.

Hale, Mark A.↗
DOE OSTI2020

Sol–Gel‐Based Advanced Porous Silica Materials for Biomedical Applications

Abstract Porous silica‐based materials have burgeoning applications ranging from fillers and additives, to adsorbents, catalysts, and recently therapeutic agents and vaccines in nanomedicine. The preponderance of these materials is made by sol–gel processing wherein soluble silica precursors are reacted to form amorphous networks composed of siloxane bonds. The facile sol–gel approach allows for an unlimited variety of binary tertiary and more complex chemical compositions including organic ligands and networks resulting in so‐called organic–inorganic hybrid materials. Here, a brief review of the recent progress in sol–gel‐derived silica materials prepared as particles, thin films, biosilica/silica bioreplicas (of molecules, cells and organisms), and their related preparation, properties, and bioapplications is provided. First, it highlights the recent achievements of mesoporous silica nanoparticles in biomedical applications, including therapeutic agent delivery, multimodal imaging and theranostics, and bone tissue engineering and repair. Second, the research in evaporation‐induced self‐assembly (EISA)‐based mesostructured silica thin films and cell‐directed EISA in bio/nano interfaces for various bioapplications, such as bioactive coatings, biosensing, and living cell immobilization, has been reviewed. Third, the pioneering work in biomimetic silicification/immobilization of biomolecules and bio‐organisms and silica bioreplication of complex bio‐organisms is summarized. Finally, it is concluded with personal perspectives on the directions of future work on this field.

Lei, Qi↗
DOE OSTI2020

Computation-Efficient Algorithm for Distributed Feedback Optimization of Distribution Grids

Feedback-based optimization algorithms use real-time measurements to update the optimal control for the underlying system which may not be fully identified. Recently, we have developed a distributed feedback-based algorithm [1] that avoids the requirement of fast communication between central computing and local actuator/sensor agents. This paper extends the work by greatly reducing the number of copies of variables involved in the distributed feedback-based algorithm, which results in faster convergence and lower communication requirement. The main idea is to leverage the specific structural properties of the admittance matrix for distribution systems with tree network topology. We also show the effectiveness of the proposed algorithm in simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗