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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Expected sensitivity of the Light Dark Matter eXperiment to long-lived dark photons and axion-like particles

The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment primarily designed to achieve world-leading, model-independent sensitivity to sub-GeV dark matter particles. LDMX aims to identify dark sector particle production through the detection of events with substantial missing energy and momentum, a signature of invisible particles escaping detection. Beyond this primary objective, LDMX offers a complementary search strategy for long-lived, visibly decaying particles, such as dark photons and axion-like particles. We present the first detailed evaluation of the ability of LDMX to identify visibly decaying, long-lived particles that couple to electrons using a detailed simulation, based on the Geant 4-toolkit, that incorporates realistic detection efficiencies and background levels. We demonstrate that LDMX can achieve a sensitivity that is competitive with other experiments that are currently running. The models explored in this paper are distinct and complementary to those probed in the LDMX flagship missing-momentum analysis. Through searching for both invisible dark matter and visibly decaying long-lived signatures, LDMX will significantly advance the search for light dark matter and provide a broad exploration of the sub-GeV dark sector.[graphic not available: see fulltext]

Akesson, Torsten [Lund U.] (ORCID:0000000341415408↗
DOE OSTI2025

Defect modeling in semiconductors: the role of first principles simulations and machine learning

Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2024

A Systematic Comparison for Consistent Scenario Development Using Microscopic Simulation Software

This study aims to explore a methodology that enables the development of consistent traffic micro simulation for emerging traffic and vehicle control technologies for improved mobility and energy efficiency across different modeling platforms. Researchers might study the same application on different platforms and have the need to benchmark across platforms. However, there lacks a systematic study on simulation software comparison, especially for emerging mobility and energy efficiency applications. For this, a systematic scenario development and evaluation approach is presented and demonstrated to compare scenarios generated in different traffic microsimulation platforms. Network-level and vehicle-level trip performance results of the traffic scenario are evaluated in three microscopic simulation platforms - VISSIM, AIMSUN, and SUMO. The results indicate that the network-level performance is consistent among the three software suites except when the demand is high, where the energy consumption performance varies.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2025

Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis

Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities a significant computational challenge for complex real-world problems, particularly when failure is a rare event. To implement multifidelity modeling, estimators that efficiently combine information from multiple models/sources are necessary. In past works, the variance reduction techniques of control variates (CV) and importance sampling (IS) have been leveraged for this task. In this paper, we present the CVIS framework—a creative take on a coupled CV and IS estimator for bifidelity reliability analysis. The framework addresses some of the practical challenges of the CV method by using an estimator for the control variate mean and sidestepping the need to estimate the covariance between the original estimator and the control variate through a clever choice for the tuning constant. Furthermore, the task of selecting an efficient IS distribution is also considered, with a view towards maximally leveraging the bifidelity structure and maintaining expressivity. Additionally, a diagnostic is provided that indicates both the efficiency of the algorithm as well as the relative predictive quality of the models utilized. Finally, the behavior and performance of the framework is explored through analytical and numerical examples.

Markov chain Monte Carlo↗
DOE OSTI2025

Structural features of xylan dictate reactivity and functionalization potential for bio-based materials

Plant-based materials have the potential to replace some petroleum-based products, offering compostability and biodegradability as critical advantages. Xylan-rich biomass sources are gaining recognition due to their abundance and underutilization in current industrial applications. Research of potential xylan applications has been complicated by the complex and heterogeneous structure that varies for different xylan feedstocks. Acylation is a broadly used reaction in functionalization of polysaccharides at an industrial scale. However, the efficiency of this reaction varies with the xylan source. To optimize xylan valorization, a systematic understanding of structure–reactivity relationships is essential. This study explores, characterizes, and compares various xylan feedstocks in the acylation process. Xylan feedstocks were analyzed for their chemical composition, degree of polymerization, branching, solubility, and presence of impurities. These features were correlated with xylan glycotypes’ reactivity toward functionalization with succinic anhydride in an optimized DMSO/KOH condition, achieving carboxyl contents of up to 1.46. We used principal component analysis and hierarchical clustering to identify key structural features of xylan that promote its reactivity. Our findings reveal that xylans with higher xylose content and lower degrees of branching exhibit enhanced reactivity, achieving higher carboxyl content and yields. Structural analyses confirmed successful modification, and light scattering analyses showed dramatic changes in the solution properties. Succinylation improves the solubility and film-forming properties of native xylans. This study shows key structure–reactivity relationships in xylan succinylation, establishing that low branching, high xylose content, and reduced lignin impurity enhance chemical functionalization. The results offer a framework for selecting optimal biomass feedstocks and support future efforts in genetic and synthetic biology to design plants with tunable xylan architectures. These findings advance the hemicellulose valorization for applications in coatings and packaging.

Acylation↗
DOE OSTI2024

Georgia Tech Accelerated, Compressed, and Regularized Compute of Kinetic-based PDEs (Final Report)

This report summarizes the collaborative effort between Lawrence Livermore National Laboratory and Georgia Tech to enhance the BoBa library for tensor train computation in PDE solvers, with a target on kinetic equations and their continuum limits. We aimed to reduce computational cost and memory usage by replacing traditional array-based computations with tensor trains. We examined the compressibility of time-evolving solutions to the Euler equations with discontinuities. We also explored using the first invsicid and linear regularization of the compressible flow equations via the information geometric regularization (IGR). We explored this in a tensor train formulation. To identify that inverse terms in the IGR equations pose problems for tensor train formulations and investigate efficient methods for batched inversion of tensor trains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Technoeconomic Analysis of Kraft Pulp Mill Integration with an Advanced Nuclear Reactor

This study focuses on post-combustion capture and oxy-fuel combustion for the boilers at the mill, as well as steam integration with the nuclear power plant. The primary goal of the research outlined in this report is to design, analyze, and document the integration of industrial-scale HTGR with a reference Kraft Pulp Mill. The purpose is to deliver reliable, cost-effective, and sustainable clean energy alternatives while reducing CO2 emissions. Specifically, this study focuses on 6 different scenarios that include carbon capture equipment and some of them use nuclear power to meet the heat and electricity needs of the reference plant. Also, 2 of these scenarios are created while also producing clean hydrogen through integrated High-Temperature Steam Electrolysis (HTSE). This report offers a detailed techno-economic assessment of different scenarios for a Kraft Pulp Mill, including an analysis of tax credits (section 45V, 45Q, and 48E) provided by the Inflation Reduction Act (IRA) of 2022. The evaluation explores the potential economic benefits and challenges of incorporating different configurations, including nuclear energy, into Kraft Pulp Mill operations, with particular attention to energy efficiency, economic implications, and environmental impact. By assessing both the technical feasibility and economic viability, this analysis aims to identify existing gaps and propose solutions for the successful implementation of nuclear integration. The findings are intended to provide valuable insights for stakeholders considering the adoption of advanced nuclear reactors in the pulp and paper industries.

08 HYDROGEN↗
DOE OSTI2025

Bridging the Gap for Powering Data Centers

The rapid expansion of data centers, primarily driven by artificial intelligence, is outpacing the adaptability of the U.S. electric grid. This report, developed by Idaho National Laboratory (INL) , presents a gap analysis of some of the infrastructure challenges associated with large-scale data center deployment. Drawing from a national workshop hosted by INL in October of 2025, the report synthesizes stakeholder insights, survey data, and technical discussions to identify critical barriers and research needs. Key findings highlight the growing preference for behind-the-meter generation, the perceived inadequacy of legacy interconnection processes, and the urgent need for improved coordination between utilities, regulators, and data center developers. Environmental concerns such as water use and noise pollution, as well as economic constraints like equipment lead times and cost allocation, are also explored. The report outlines national lab capabilities in modeling, simulation, and technical assistance, and proposes targeted R&D priorities to support resilient, scalable, and efficient integration of data centers into the grid.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Impact of Temperature and Optical Error on the Combined Optical and Thermal Efficiency of Solar Tower Systems for Industrial Process Heat: Preprint

Concentrating solar thermal (CST) power towers can provide high flux concentrations at commercial scale. As a result, CST towers exhibit potential for high-temperature solar industrial process heat (SIPH) applications. However, at higher operating temperatures, thermal radiation losses can be significant. This study explores the trade-off between thermal and optical losses for SIPH applications using a collection of three case studies at operating temperatures that range from 900-1,550 degrees C. We assume blackbody radiation to represent the thermal losses at the receiver and we use ray tracing to estimate the optical losses. The results show the impact of process temperature on the maximum attainable system efficiency, as well as the higher flux concentration requirements as the temperature increases.

concentrating solar power↗
DOE OSTI2026

Laboratory Testing of Portable Air Cleaner Products for Energy Efficiency and Clean Air Performance at Various Fan Settings [Slides]

This document contains the results of a product review and laboratory testing experiment of portable air cleaning devices (PACs). Thirty-four commercially available PACs were reviewed, and from those, 7 products were purchased and underwent AHAM AC-7 clean air delivery rate (CADR) testing. The following research questions were explored: 1) What is the relationship between clean air delivery rate (CADR) and energy across a range of products and what is the general decrement in CADR as a result of operating at lower fan speeds? 2) How might the multiple units at lower power compare to fewer, larger units at full power in terms of energy efficiency and total cost? 3) How do the measured CADR, power, and efficiency compare to the manufacturer specs? 4) Are there product attributes that can be identified that might contribute to better CADR/W performance?

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Energy-Efficient Capacitive Deionization through Electrode Modification and Process Development

Electrochemical separation technologies, such as capacitive deionization (CDI), are promising for addressing global energy and water challenges. However, there is a need to improve the performance, better understand property-performance relationships, and evaluate the longevity of CDI electrodes. This study explores the chemical modification of electrodes and the adjustment of CDI operating parameters. Results indicate that nitric acid (HNO3) conditioning of activated carbon cloth (ACC) electrodes removes metal oxides, introduces oxygen and nitrogen functionalities, and increases the specific capacitance (16% at 1 mV/s). Moreover, these changes in electrode properties positively impact device-level CDI performance. Through HNO3-conditioning of the ACC and tuning of the operational parameters, this work demonstrates higher electrosorption capacity (4.0x), greater charge efficiency (90% vs 24%), and lower energy consumption (3.8x). Despite these enhancements, limitations of the HNO 3 -conditioned ACC include decreased desorption kinetics and a 32% loss in electrosorption capacity after 200 cycles. Overall, this work provides guidance on using oxidative pretreatment via HNO 3 to modify ACC electrodes for CDI and evaluates the trade-offs associated with varying operational parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators

We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microwave-Driven Nonoxidative and Selective Conversion of Methane to Ethylene over Mn-Based Catalysts

Recent advancements in microwave-driven nonoxidative catalytic synthesis of C 2 H 4 from CH 4 coupling offer a promising, energy-efficient, and eco-friendly alternative to conventional methods, where selective heating under microwave irradiation enables comparable conversions at substantially lower bulk temperatures and shorter reaction times. This study explores the performance of an MnO X -based catalyst supported on CeO 2 and HY zeolite (silica-toalumina ratio = 5.1) for the nonoxidative coupling of CH 4 (NOCM) under microwave irradiation. Inspired by the well-established efficacy of MnO X catalyst in oxidative CH 4 coupling (OCM), their application in NOCM has also shown significant performance. The catalytic system achieved 15% CH 4 conversion and 99% selectivity toward C 2 hydrocarbons and maintained 64% selectivity toward C 2 H 4 surpassing the yields reported in the literature even at higher temperatures (700−1000 °C). Catalyst performance was correlated with measurements by in situ Raman spectroscopy, and additional characterizations were performed using H 2 −temperature programmed reduction , NH 3 −temperature programmed desorption, and BET surface area analysis to understand structural changes during the reactions. These findings suggested that Mn functions as active sites for CH 4 activation in nonoxidative environments while also promoting efficient C−C coupling under microwave irradiation.

Catalysts↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Investigating resource-efficient neutron/gamma classification ML models targeting eFPGAs

There has been considerable interest and resulting progress in implementing machine learning (ML) models in hardware over the last several years from the particle and nuclear physics communities. A big driver has been the release of the Python package, hls4ml, which has enabled porting models specified and trained using Python ML libraries to register transfer level (RTL) code. So far, the primary end targets have been commercial field-programmable gate arrays (FPGAs) or synthesized custom blocks on application specific integrated circuits (ASICs). However, recent developments in open-source embedded FPGA (eFPGA) frameworks now provide an alternate, more flexible pathway for implementing ML models in hardware. These customized eFPGA fabrics can be integrated as part of an overall chip design. In general, the decision between a fully custom, eFPGA, or commercial FPGA ML implementation will depend on the details of the end-use application. In this work, we explored the parameter space for eFPGA implementations of fully-connected neural network (fcNN) and boosted decision tree (BDT) models using the task of neutron/gamma classification with a specific focus on resource efficiency. We used data collected using an AmBe sealed source incident on Stilbene, which was optically coupled to an OnSemi J-series silicon photomultiplier (SiPM) to generate training and test data for this study. We investigated relevant input features and the effects of bit-resolution and sampling rate as well as trade-offs in hyperparameters for both ML architectures while tracking total resource usage. The performance metric used to track model performance was the calculated neutron efficiency at a gamma leakage of 10 -3 . The results of the study will be used to aid the specification of an eFPGA fabric, which will be integrated as part of a test chip.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Advances in ArborX to support exascale applications

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Hythane production from brewery wastewater‐generated biogas using a membrane electrochemical cell

Converting organic wastes into hythane, a blend of hydrogen (5% to 25%) and methane (75% to 95%), will not only reduce waste discharge but also maximize energy recovery. Herein, a membrane electrochemical cell was investigated to produce hythane from biogas generated in anaerobic digestion of brewery wastewater (BW). The key parameters including current densities, electrolyte concentrations, and biogas flow rates were examined in batch tests. Under an optimal condition (210 mA, 100 mM electrolyte, 1 mL min −1 of biogas flow), the system achieved the production of hythane containing 70.6% ± 1.1% CH 4 , 27.3% ± 0.5% H 2 , and 2.1% ± 1.6% CO 2 (corresponding to 91.1% ± 6.4% CO 2 removal). Meanwhile, the H 2 S concentration was decreased from 513 to 2 ppm, 99.9% ± 0.2% removal. Energy efficiency of this system was estimated 61.8% ± 10.7%, and energy output increased by 54.4% ± 10.6% with biogas upgrading to hythane. Furthermore, these results encourage further exploration of electrochemical approach for simultaneous biogas upgrading and hythane production.

Rao, Yue [Washington University in St. Louis, MO (↗
DOE OSTI2025

Evaluating the carbon capture potential of industrial waste as a feedstock for enhanced weathering

Abstract Limiting anthropogenic global climate warming since the start of the industrial period to less than 2 °C will very likely require both deep and rapid reductions in anthropogenic greenhouse gas emissions and a range of approaches toward carbon dioxide removal (CDR). One prominent CDR approach is enhanced weathering (EW), in which crushed silicate rock is applied on land or in the open ocean to accelerate natural weathering processes that absorb carbon dioxide from Earth’s ocean–atmosphere system. However, in addition to a range of potential environmental, socioeconomic, and ethical issues associated with this pathway, bottlenecks in feedstock sourcing represent a key barrier for deployment of EW at scale. Here, we evaluate the potential of silicate wastes produced from industrial processes—such as steel slag and cement waste—as feedstocks for the EW process. An empirical model that links industrial alkaline waste production to gross domestic product at purchase power parity is developed to forecast waste production in the alternative futures described by the shared socioeconomic pathway (SSP) framework. By incorporating these results into an intermediate-complexity Earth system model, we also explore the impacts of EW using industrial waste on changes to global temperature, ocean pH, and ocean aragonite saturation state, while also quantifying overall CDR efficiency through the end of the century. We estimate a maximum cumulative end-of-century capture potential of ∼400 GtCO 2 for industrial waste, which could represent a significant fraction of the projected CDR requirement of many mitigation scenarios in the SSP framework. However, feedstock-dependent environmental impacts and the technoeconomics of feedstock redistribution may ultimately limit deployment scope.

Xu, Pengxiao (ORCID:0009000633724293)↗