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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 OSTI2025

Amine Structure Governs Corrosion Rates of Copper Catalysts in Electrochemical Reactive Capture of CO 2

Reactive capture of CO 2 (RCC) offers an integrated approach that combines CO 2 capture with its direct electrochemical conversion, eliminating the need for CO 2 release from the capture agent. By avoiding the pH, pressure, and temperature swings required for the release step, RCC has the potential to reduce both energy consumption and capital costs compared to the conventional sequential process of CO 2 capture, release, concentration, and conversion. Amines, widely used in industrial CO 2 capture, face challenges in RCC systems due to their incompatibility with transition metal catalysts as well as their tendency to promote electrode corrosion and parasitic hydrogen evolution. Identifying suitable combinations of amines and catalysts is therefore critical to enabling integrated CO 2 capture and conversion. Here, this work systematically investigates the performance of four primary and four secondary amines for RCC on polycrystalline Cu catalysts. Among the eight tested amines, only dimethylamine showed no measurable Cu corrosion near the open circuit potential. In contrast, ammonia, methylamine, ethylamine, monoethanolamine, diethylamine, diethanolamine, and piperazine all induced Cu corrosion. Corrosion rates correlate with the pK a and steric hindrance of the amines, highlighting key parameters for catalyst–amine codesign. Grand canonical DFT calculations indicate a correlation between the adsorption strength of protonated amines, their pK a , and the extent of Cu corrosion, suggesting that both the surface binding of protonated amines and the lability of their protons play critical roles in corrosion acceleration near open circuit potentials. These finding suggest that amines with high pK a values and weak binding of their protonated forms to Cu surfaces are preferred, as they offer better corrosion resistance.

Choi, Jounghwan [Univ. of California, Los Angeles,↗
DOE OSTI2025

Assessment of Physical Security Modeling and Simulation in the Vulnerability Assessment Process

This report provides a comprehensive assessment of physical security modeling and simulation tools available for use in the vulnerability assessment (VA) process for nuclear facilities. It outlines the historical evolution of VA methodologies, emphasizing the transition from traditional layer-based approaches to a more holistic framework that integrates detection probabilities directly into combat simulations. The document details the critical components of the VA process, including the characterization of targets, threats, and protective measures, as well as the development of adversary scenarios that reflect both insider and outsider threats. It highlights the importance of performance assurance programs, emphasizing the need for continuous evaluation and testing of security systems to ensure their effectiveness against evolving threats. Additionally, the report discusses the significance of utilizing accredited modeling and simulation tools in accredited areas to accurately represent adversary actions and the corresponding responses of protective forces. By establishing a systematic approach to VA, this document aims to enhance the overall security posture of nuclear facilities, ensuring compliance with regulatory standards while effectively mitigating risks associated with potential adversarial actions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Self‐Assembled Metal–Dielectric Hybrid Metamaterials in Vertically Aligned Nanocomposite Form with Tailorable Optical Properties and Coupled Multifunctionalities

Metal–dielectric hybrid metamaterials have attracted increasing research interest in recent years because of their novel optical properties and promising applications in the fields of electronic and photonic devices. Dielectric permittivity is a key parameter that strongly influences the optical properties of materials. By self‐assembling the metallic and dielectric components in a pillar‐in‐matrix structure, a strong anisotropic structure forms and results in opposite signs of permittivity components (i.e., ε || > 0 and ε ⊥ < 0, or vice versa) and exotic optical responses including hyperbolic dispersion in the visible to near‐infrared region. Herein, the main approaches of tuning the permittivity in self‐assembled metal–dielectric vertically aligned nanocomposite (VAN) thin films are reviewed, including tuning the metal pillar density and geometry, film strain state and background pressure during growth, and seeking other metal‐free and complex structure designs. Future research directions are also proposed, including unique approaches to improve their thermal stability, integrate on flexible substrates toward wearable device fabrication, and explore real‐time tunable metamaterials.

Zhang, Di↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2021

AI-enabled Dynamic Finish Machining Optimization for Sustained Surface Integrity

While machining processes are typically leveraged to establish geometric features, many functional characteristics of advanced materials are directly determined by their machining-induced surface integrity (SI). Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters. We present a novel integrated approach based on model-informed artificial intelligence (AI), which optimizes ‘dynamic’ process parameters in real-time. Here, by maximizing the useful life of a cutting tool over which a required set of SI parameters can be maintained, our paradigm will enable significantly more efficient processing of next-generation materials and components.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Advancing Cross-Disciplinary Understanding of Land-Atmosphere Interactions

The evolution of disciplinary silos and increasingly narrow disciplinary boundaries have together resulted in one-sided approaches to the study of land-atmosphere interactions—a field that requires a bi-directional approach to understand the complex feedbacks and interactions that occur. The integration of surface flux and atmospheric boundary layer measurements is therefore essential to advancing our understanding. The Land-Atmosphere 2021 workshop (held virtually, June 10-11, 2021) involved almost 300 participants from around the world and promoted cross-discipline collaboration by way of talks from invited speakers, moderated discussions, breakout sessions, and a virtual poster session. The workshop focused on five main theme areas: “big picture” overview, instrumentation and remote sensing, modeling, water, and aerosols and clouds. In talks and breakout groups, there were frequent calls for more AmeriFlux sites to be instrumented for boundary layer height measurements, and for the development of some “super sites” where profiling instruments would be deployed. There was further agreement on the need for the standardization of various datasets. There was also a consensus that funding agencies need to be willing to support the sorts of large projects (including associated instrumentation) which can drive interdisciplinary work. Early-career scientists, in particular, expressed enthusiasm for working across disciplinary boundaries but noted that there need to be more financial support and training opportunities so they would be better prepared for interdisciplinary work. Investment in these career development opportunities would enable today's cohort of early-career scientists to advance the frontiers of interdisciplinary work over the next couple of decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Variational Monte Carlo calculations of n + H 3 scattering

A paramount goal in nuclear physics is to unify ab initio treatments of bound and unbound states. The position-space quantum Monte Carlo (QMC) methods have a long history of successful bound-state calculations in light systems but have seen minimal implementation in unbound systems. Here, we introduce a numerical method to improve the efficiency and accuracy of unbound-state calculations in QMC. As an initial application, we compute scattering observables for the smallest system available to probe three-body forces, the neutron-triton system, using variational Monte Carlo (VMC) wave functions. The method involves inferring long-range amplitudes in the wave function from integrals over the short-range region where all the particles interact. This approach using integral relations is well established in the literature; here, we develop it for the QMC framework. We validate our approach with a consistency check between short-range spectroscopic overlap functions computed from direct evaluation and from the integral relations; scattering amplitudes are long-range asymptotics of those overlaps. Comparison against published benchmark calculations using the same potential demonstrates that when applied to the current VMC wave functions, the integral method produces more accurate scattering observables than direct evaluation from the same variational wave function. However, it still differs noticeably from the exact results. Using additional interactions, we then present phase shifts and mixing parameters for the n + 3 H system. In particular, we present one of the first applications of the Norfolk family of local coordinate-space chiral potentials in unbound systems of A > 2. The Norfolk results accurately describe s-wave scattering but predict p-wave cross sections too large. Compared with previous QMC scattering calculations, the integral method avoids difficulties associated with the precise computation of energy differences and with convergence outside the interaction region, which is particularly severe in the variational calculation. Application of the integral method here paves the way for its use in Green's function Monte Carlo (GFMC) calculations. In GFMC, the wave functions are more accurate, but the high-precision convergence of their tails is slow, and there are additional difficulties in reading out amplitudes. Here, the integral methods will address both of those remaining problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Elastoplasticity models often introduce a scalar-valued yield function to implicitly represent the boundary between elastic and plastic material states. Here this paper introduces a new alternative where the yield envelope is represented by a manifold of which the topology and the geometry are learned from a set of data points in a parametric space (e.g. principal stress space, π-plane). Here, deep geometric learning enables us to reconstruct a highly complex yield envelope by breaking it down into multiple coordinate charts. The global atlas that consists of these coordinate charts in return allows us to represent the yield surface via multiple overlapping patches, each with a specific local parametrization. This setup provides several advantages over the classical implicit function representation approach. For instance, the availability of coordinate charts enables us to introduce an alternative stress integration algorithm where the trial stress may project directly on a local patch and hence circumvent the issues related to non-smoothness and the lack of convexity of yield surfaces. Meanwhile, the local parametric approach also enables us to predict hardening/softening locally in the parametric space, even without complete knowledge of the yield surface. Comparisons between the classical yield function approach on the non-smooth plasticity and anisotropic cam-clay plasticity model are provided to demonstrate the capacity of the models for highly precise yield surface and the feasibility of the implementation of the learned model in the local stress integration algorithm.

42 ENGINEERING↗
DOE OSTI2024

Exceptional electronic transport and quantum oscillations in thin bismuth crystals grown inside van der Waals materials

Confining materials to two-dimensional forms changes the behaviour of the electrons and enables the creation of new devices. However, most materials are challenging to produce as uniform, thin crystals. Here we present a synthesis approach where thin crystals are grown in a nanoscale mould defined by atomically flat van der Waals (vdW) materials. Here, by heating and compressing bismuth in a vdW mould made of hexagonal boron nitride, we grow ultraflat bismuth crystals less than 10 nm thick. Due to quantum confinement, the bismuth bulk states are gapped, isolating intrinsic Rashba surface states for transport studies. The vdW-moulded bismuth shows exceptional electronic transport, enabling the observation of Shubnikov–de Haas quantum oscillations originating from the (111) surface state Landau levels. By measuring the gate-dependent magnetoresistance, we observe multi-carrier quantum oscillations and Landau level splitting, with features originating from both the top and bottom surfaces. Our vdW mould growth technique establishes a platform for electronic studies and control of bismuth’s Rashba surface states and topological boundary modes1,2,3. Beyond bismuth, the vdW-moulding approach provides a low-cost way to synthesize ultrathin crystals and directly integrate them into a vdW heterostructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integration and Demonstration of Monitoring, Modeling, and Prediction of DV-1 Amendment Performance at the Bench Scale: DV-1 Amendment Demonstration

During fiscal years 2024 and 2025, the U.S. Department of Energy’s Hanford Field Office commissioned Pacific Northwest National Laboratory to conduct applied research aimed at reducing the cost, time, and uncertainty associated with in situ treatment of vadose zone contaminants at the Hanford Site. This report outlines the integration of three key research efforts into a meso-scale demonstration designed to advance field-scale solutions that aim to (1) optimize the delivery of chemical amendments to contaminated soils, (2) reduce uncertainty in amendment delivery performance assessment using advanced monitoring techniques, and (3) provide real-time insights into when and where amendment-induced precipitation reactions occur in the subsurface. To achieve these objectives, the tank-scale (~ 1 cubic meter) Geophysical Imaging of Flow and Transport (GIFT) system was developed. GIFT enables experimental testing of amendment delivery while incorporating automated multi-modal monitoring approaches, including pressure measurements, direct fluid sampling, and remote time-lapse geophysical imaging. The data generated from these monitoring techniques will serve as inputs for a generative artificial-intelligence-driven digital twin – a numerical simulation model designed to honor observed data while quantifying uncertainty in simulation accuracy. Using this simulator, researchers will refine an amendment injection strategy to maximize delivery efficiency within a low-permeability soil zone. Monitoring data will be interpreted through simulated outputs to enhance understanding of the injection process. The efficacy of this integrated approach will be evaluated through direct sampling at the conclusion of the experiment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Desalinating a Real Hyper-Saline Pre-Treated Produced Water via Direct-Heat Vacuum Membrane Distillation

Membrane distillation (MD) is an emerging thermal desalination technology capable of desalinating waters of any salinity. During typical MD processes, the saline feedwater is heated and acts as the thermal energy carrier; however, temperature polarization (as well as thermal energy loss) contributes to low distillate fluxes, low single-pass water recovery and poor thermal efficiency. An alternative approach is to integrate an extra thermal energy carrier as part of the membrane and/or module assembly, which can channel externally provided heat directly to the membrane-feedwater interface and/or along the feed channel length. This direct-heat delivery has been demonstrated to increase single-pass water recovery and enhance the overall thermal efficiency. We developed a bench-scale direct-heated vacuum MD (DHVMD) process to desalinate pre-treated oil and gas "produced water" with an initial total dissolved solids of 115,500 ppm at a feed temperature ranging between 24 and 32 degrees C. We evaluated both water flux and specific energy consumption (SEC) as a function of water recovery. The system achieved a 50% water recovery without significant scaling, with an average flux >6 kg m-2 hr-1 and a SEC as low as 2,530 kJ kg-1. The major species of mineral scales (i.e., NaCl, CaSO4, and SrSO4) that limited the water recovery to 68% were modeled in terms of thermodynamics and identified by scanning electron microscopy and energy-dispersive X-ray spectroscopy. In addition, we further developed and employed a physics-based process model to estimate temperature, salinity, water transport and energy flows for full-scale vacuum MD and DHVMD modules. Model results show that a direct-heat input rate of 3,600 W can increase single-pass water recovery from 2.1% to 3.1% while lowering the thermal SEC from 7,800 kJ kg-1 to 6,517 kJ kg-1 in an unoptimized module. Finally, the scaling up potential of DHVMD process is briefly discussed.

brine↗
DOE OSTI2024

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective

Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ranking Biological Features in Soil-Based Microbial Multi-Omics Data with Integration Modeling

Distinguishing the most important features (e.g. proteins, metabolites, etc.) per group (e.g. control and treatment) is a critical challenge in feature-rich multi-omics experiments, especially in soil data. Traditional feature identification and ranking approaches, such as differential expression, are based on single omics and thus not directly translatable to multi-omics experiments. Here, 5 multi-omics integration models (DIABLO, JACA, MOFA, MultiMLP, and SLIDE) that were not explicitly built for soil data applications were tested using a soil-based multi-omics experiment. The data were obtained from an experimental setup of an autoclaved soil system inoculated with 8 bacteria and using chitin as the carbon source and including samples collected at 0- (control), 4-, 8-, and 12-weeks post-inoculation. The omics data included metaproteomics, 16S rRNA sequencing, and LC-MS/MS metabolomics (in positive and negative mode). Each multi-omics integration model was implemented, and top features were compared to differential univariate statistics per omic type, demonstrating that integration approaches cut the potential number of top features from 2957 identified by differential statistics to 13-224 (a 99.6% to 92.4% reduction). Interestingly, most top features across integration models were not shared; though, scaling and averaging ranks across models shared similar patterns. This work highlights the usefulness of multi-omics integration models in soil-based microbial studies and the power of using multiple integration models together to interpret results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A new approach to the evaluation and solution of the relativistic kinetic dispersion relation and verification with continuum kinetic simulation

Here, the present work describes a new approach to evaluation and root finding for the kinetic dispersion relation of Langmuir waves, which is central to the analytical understanding of collisionless damping in plasmas. The plasma dispersion function is solved to machine precision using direct integration in the complex plane in combination with an analytic evaluation of the residue to account for the deformation along the Landau contour. To efficiently attain machine precision, the contour is displaced in the complex plane prior to integration, and numerical subtleties related to the placement of the contour are discussed. The approach is generic in that it applies to arbitrary distribution functions, with the present manuscript focused on relativistic cases. Detailed verification of results via direct kinetic simulation in a variety of configuration space dimensions is also presented. Finally, the technique is applied to the challenging case of highly relativistic (i.e. extremely hot) plasmas. Here we show both qualitative agreement with prior work, as well as the disappearance of the Landau root which would have significant implication for real-life observation or experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Accurate determination of uranium isotope abundances by wavelength modulation spectroscopy in atomic beams

The design and demonstration of an optical analysis system based on wavelength modulation spectroscopy in an atomic beam for uranium isotope abundance determinations is presented. This system probes the uranium 5f 3 6d7s 2 ( 5 L 6 ) → 5f 2 6d 2 7s 2 ( 5 K 5 ) transition at 861.031 nm, which is considered to be the most suitable transition for uranium isotopic analysis. A new laser characterization strategy was developed for the conditions where optimum laser wavelength modulation depth was small compared to the free spectral range (FSR) of etalons. Two capabilities enabled the higher-precision determination of isotope abundances of atomic beams: (1) reduction of low-frequency additive noise, especially the noise caused by black-body radiation and (2) suppression of non-absorption transmission losses. The performance of this system was validated with uranium samples of various isotopic compositions. Further, by comparing the measurements using natural uranium samples between the direct absorption and the wavelength modulation approaches, a 21-fold decrease in uncertainty of the integrated absorbance and a 6.8-fold improvement in the 1-σ precision of the number density were achieved. In addition, by comparing the results using uranium oxide samples, a 6.1-fold decrease in the uncertainty of inferred isotope abundance was obtained. These results demonstrate that the 1f-normalized 2f wavelength modulation spectroscopy (WMS-2f/1f) technique enables higher-precision analysis of atomic beams.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗