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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

VHH antibody loop guides design of a synthetic macrocyclic peptide that potently blocks influenza virus membrane fusion

Abstract Miniaturizing biologically complex structural motifs to produce synthetic functional mimetics holds significant promise for development of new therapeutic modalities. Here, we demonstrate a unique approach using the key binding loop of the single variable domain of a heavy chain (V H H) llama antibody as a starting point for peptide design. V H H antibodies of camelids and sharks generally have longer, but more ligand-efficient complementarity determining region 3 (CDR3) loops and are relatively stable structures. We harnessed these attributes as templates for design of a series of synthetic macrocyclic peptides. The designed peptides exhibit nanomolar binding to influenza hemagglutinin (HA) and heterosubtypic in vitro neutralization breadth against influenza A viruses by inhibiting the low pH mediated HA conformational changes that lead to membrane fusion. X-ray structures of peptide-HA complexes reveal high structural mimicry with the parent V H H antibody. One such macrocycle peptide candidate is promising for further development of broad protection against influenza A group 1 viruses.

Kadam, Rameshwar U.↗
DOE OSTI2024

Machine learning-guided discovery of gas evolving electrode bubble inactivation

The adverse effects of electrochemical bubbles on the performance of gas-evolving electrodes are well known, but studies on the degree of adhered bubble-caused inactivation, and how inactivation changes during bubble evolution are limited. We study electrode inactivation caused by oxygen evolution while using surface engineering to control bubble formation. We find that the inactivation of the entire projected area, as is currently believed, is a poor approximation which leads to non-physical results. Using a machine learning-based image-based bubble detection method to analyze large quantities of experimental data, we show that bubble impacts are small for surface engineered electrodes which promote high bubble projected areas while maintaining low direct bubble contact. We thus propose a simple methodology for more accurately estimating the true extent of bubble inactivation, which is closer to the area which is directly in contact with the bubbles.

Lake, Jack R.↗
DOE OSTI2025

The inorganic chemist's guide to actinide radiation chemistry: a review

This review aims to provide an overview of the current state of radiation chemistry with respect to the actinide elements, thorium through californium. Despite the inherent radioactivity of the actinides, only a few studies explore the effects of ionizing radiation on their redox chemistry and surrounding environment. This fundamental knowledge gap, coupled with the current renaissance in actinide-based technologies such as nuclear power, space exploration, and medicine, underscores the importance of research in this interdisciplinary area. This review will focus on the interactions between reactive species formed by radiolysis with actinides and their complexes, offering an inorganic chemist's perspective on research in radiation chemistry. In addition, a thorough discussion of our current understanding of radiation-induced changes in actinide speciation in both aqueous solution and the solid-state will be provided, focusing on changes in oxidation state distribution, complexation, and secondary coordination effects within inorganic materials. Finally, this review will discuss challenges and opportunities for inorganic chemists to explore this unique intersection of fields.

Actinides↗
DOE OSTI2024

Advances in laser-based bremsstrahlung x-ray sources. II. Laser pulse propagation and guiding in nonuniform plasma media in the presence of self-focusing

An analytic Wentzel–Kramers–Brillouin model is presented of Gaussian laser pulse propagation through plasma with a quadratic transverse density profile and an arbitrarily varying, longitudinal density gradient under conditions of nonlinear self-focusing. From these solutions, it is shown that in the absence of nonlinear self-focusing and transverse nonuniformity, for exponential pre-plasma density profiles, the use of a low density coating of the laser target with electron density n0∼11 ncr (e.g., a CH foam of density 35 mg/cm3 for 1-micron laser light) maximizes laser intensity at best focus. Also, under laser and plasma conditions relevant to recent experiments on high-power laser systems, conditions are obtained for a Gaussian laser pulse to propagate stably through the pre-plasma medium. Such conditions would be expected to enhance the production of relativistic electrons from laser-target coupling, providing a possible explanation for the observed increase in MeV photon dose and enabling applications such as laser-based MeV X-ray radiography.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

WEST L-mode record long pulses guided by predictions using Integrated Modeling

A new record was set on the WEST Tokamak, designed to operate long duration plasmas in a tungsten (W) environment, with an injected energy of 1.15 GJ and a plasma duration 364s. Scenario development was supported by integrated modeling using the High Fidelity Plasma Simulator (HFPS), the European IMAS-coupled version of JETTO/JINTRAC, which integrates physics-driven modules into a unified framework. In particular, a reduced model for Lower-Hybrid heating and Current-Drive (LHCD) and the quasi-linear turbulent transport model TGLF are crucial for long pulses predictions up to the Last Closed Flux Surface (LCFS). Using this workflow, a 100 s reference discharge was modeled and plasma kinetic profiles and loop voltage were quantitatively well matched. In preparation for the recent long duration experiments, non-inductive current-drive actuators (I P , n e , P LHCD ) were varied to determine the operational domain going towards fully non-inductive discharges. In particular, decreasing the plasma current is shown to ease the access to such conditions, with a careful monitoring of (n e , P LHCD ) to avoid machine limitations. In addition, post-prediction experiments conducted within the investigated parameter range validated the predicted dependencies and were shown to be in quantitative agreement. Exploratory work on the use of ECCD for MHD stability purpose is also introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A review on machine learning-guided design of energy materials

Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
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↗