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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
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 OSTI2026

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗
DOE OSTI2020

Tunable exciton binding energy in 2D hybrid layered perovskites through donor–acceptor interactions within the organic layer

The strength of electrostatic interactions within semiconductors strongly affects their performance in optoelectronic devices. An important target is the tuning of a material’s exciton binding energy—the energy binding an electron–hole pair through the electrostatic Coulomb force—independent of its electronic band gap. Here, we report on the doping of a family of two-dimensional hybrid perovskites, in which inorganic lead halide sheets alternate with naphthalene-based organic layers, with tetrachloro-1,2-benzoquinone (TCBQ). For four out of seven n = 1 perovskites, the incorporation of the electron-accepting TCBQ dopant into the organic sublattice containing the electron-donating naphthalene species enabled the tuning of the materials’ 1s exciton binding energy. The naphthalene–TCBQ electron donor–acceptor interactions increased the electrostatic screening of the exciton, in turn lowering its binding energy relative to the undoped perovskite—by almost 50% in one system. Structural and optical characterization showed that the inorganic lattice is not significantly perturbed even though the layer-to-layer spacing increases upon molecular dopant incorporation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of surface functional groups on MXene conductivity

In this work, we report the in-plane electron transport in the MXenes (i.e., within the MXene layers) as a function of composition using the density-functional tight-binding method, in conjunction with the non-equilibrium Green’s functions technique. Our study reveals that all MXene compositions have a linear relationship between current and voltage at lower potentials, indicating their metallic character. However, the magnitude of the current at a given voltage (conductivity) has different trends among different compositions. For example, MXenes without any surface terminations (Ti 3 C 2 ) exhibit higher conductivity compared to MXenes with surface functionalization. Among the MXenes with –O and –OH termination, those with –O surface termination have lower conductivity than the ones with –OH surface terminations. Interestingly, conductivity changes with the ratio of –O and –OH on the MXene surface. Our calculated I–V curves and their conductivities correlate well with transmission functions and the electronic density of states around the Fermi level. The surface composition-dependent conductivity of the MXenes provides a path to tune the in-plane conductivity for enhanced pseudocapacitive performance.

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

An open-source framework for simulation-based testing of buildings control strategies

Here this paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Domain-specific text embedding model for accelerator physics

Accelerator physics presents unique challenges for natural language processing (NLP) due to its specialized terminology and complex concepts. A key component in overcoming these challenges is the development of robust text embedding models that transform textual data into dense vector representations, facilitating efficient information retrieval and semantic understanding. In this work, we introduce AccPhysBERT, a sentence embedding model fine-tuned specifically for accelerator physics. Our model demonstrates superior performance across a range of downstream NLP tasks, surpassing existing models in capturing the domain-specific nuances of the field. We further showcase its practical applications, including semantic paper-reviewer matching and integration into retrieval-augmented generation systems, highlighting its potential to enhance information retrieval and knowledge discovery in accelerator physics. Published by the American Physical Society 2025

Hellert, Thorsten (ORCID:0000000227970926)↗
DOE OSTI2026

Strain mapping of three-dimensionally structured two-dimensional materials

Strain plays a crucial role in tuning materials’ properties, influencing their optical, electrical, and chemical performances. In two-dimensional (2D) materials, applied stress often induces out-of-plane deformation, resulting in a more intricate three-dimensional (3D) topography, where mapping the strain remains a challenge due to the limitations of conventional characterization techniques. In this work, we introduce BRIGHT (Bragg-Rod Informed, Gradient-based Height-mapping Technique), an integrated method for reconstructing both the topography and planar strain profile of 3D-structured 2D materials using nanobeam four-dimensional scanning transmission electron microscopy (4D-STEM). We apply BRIGHT to a MoS2-MoSe2 transition metal dichalcogenide (TMD) lateral heterojunctions exhibiting built-in strain and out-of-plane ripples and show that varying heterojunction widths lead to distinct surface morphologies and corresponding changes in the planar strain distribution. These results establish a foundation for more effective strain engineering in 2D materials by accounting for out-of-plane structural features, thereby enabling more precise control of strain-dependent properties.

Mireles, Adan [Rice Univ., Houston, TX (United Sta↗
DOE OSTI2022

Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work (Luu et al., 2021) temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI’s GPT-2.

Foundation Models, Chemistry↗
DOE OSTI2020

Performance Assessment of AFQMC Implementation (ECP Milestone 5.2 Report)

The auxiliary field quantum Monte Carlo algorithm in QMCPACK was ported to run on AMD GPUs using HIP. Initial performance measurements on early access hardware indicate a 4x slowdown compared to NVIDIA V100s. The slowdown was determined to be a result of suboptimal ROCM libraries for small matrix sizes coupled with some poorly performing hipified CUDA kernels. Through a preliminary analysis of HIP kernel performance we determined a strategy for pro ling and tuning kernels. We request that ROCM libraries are optimized for smaller problem sizes particularly for batched operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Recipe for a Great Meal: A Benchtop Route from Elemental Se to Superior Thermoelectric β-Ag 2 Se

The low-temperature modification of β-Ag 2 Se has proven to be useful as a near-room-temperature thermoelectric material. Over the past years, research has been devoted to interstitial, vacancy, and substitutional doping into the parent β-Ag 2 Se structure, aiming at tuning the material’s charge and heat transport properties to enhance thermoelectric performance. The transformation of β-Ag 2 Se into α-Ag 2 Se at ~134 °C and the low solubility of dopants are the main obstacles for the doping approach. Herein, we report a facile, safe, scalable, and cost-effective benchtop approach to successfully produce metal-doped β-Ag 2 Se. The doped materials display a remarkable enhancement of thermoelectric performance with a record-high peak zT of 1.30 at 120 °C and an average zT of ~1.15 in the 25–120 °C range for 0.2 at. % Zn-doped Ag 2 Se. The enhancement in zT is attributed to point defects created by Zn doping into Ag vacancies/interstitials, which enhances the scattering of phonons and tunes the charge carrier properties, leading to the significant suppression of thermal conductivity. In conclusion, the simplicity of the synthetic method developed herein and the high performance of the final products provide an avenue to produce high-quality Ag 2 Se-based thermoelectric materials.

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

Healing Gradient Degradation in Nb3Sn SRF Cavities Using a Recoating Method

Despite having advantageous superconducting properties, Nb3Sn superconducting radiofrequency (SRF) cavities still have practical challenges compared to Nb SRF cavities due to the brittle nature of Nb3Sn. Performance degradation can occur when a Nb3Sn SRF cavity experiences mechanical stresses such as during handling and tuning of the cavity. In this study, we present a potential treatment for SRF cavities that have experienced stress induced performance degradation that involves a recoating procedure. The degraded cavity is coated with a small amount of Sn using a single step vapor-diffusion methodology. Using this approach we can recover a significant portion of the lost performance of a Nb3Sn SRF cavity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution

Neuroevolution has had significant success over recent years, but there has been relatively little work applying neuroevolution approaches to spiking neural networks (SNNs). SNNs are a type of neural network that includes temporal processing component, are not easily trained using other methods, and can be deployed into energy-efficient neuromorphic hardware. In this work, we investigate two evolutionary approaches for training SNNs. We explore the impact of the hyperparameters of the evolutionary approaches, including tournament size, population size, and representation type, on the performance of the algorithms. We present a multi-objective Bayesian-based hyperparameter optimization approach to tune the hyperparameters to produce the most accurate and smallest SNNs. We show that the hyperparameters can significantly affect the performance of these algorithms. We also perform sensitivity analysis and demonstrate that every hyperparameter value has the potential to perform well, assuming other hyperparameter values are set correctly.

Parsa, Maryam↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Operational control with set-points tuning - application to mobile robots

This paper proposes a novel method for the optimal tuning of set points for multiple-layered control system structure widely seen in robotics and other complex industrial processes composed of a number of subsystems. The terminal sliding mode control (SMC) is used as the low-level control strategy to ensure the stability of subsystems. When uncertainties exist, it can be shown that the deteriorated system performance will be improved by the outer loop with set points tuning. For this purpose, the learning of the new set point is designed to compensate for the effects caused by uncertainties during the system operation. At the same time, the system is proven to stay with the original set point when the compensation is introduced. A practical application to a holonomic mobile robot system is given to illustrate the presented method. Desired results have been obtained.

42 ENGINEERING↗
DOE OSTI2020

Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

17 WIND ENERGY↗
DOE OSTI2021

High-Performance Catalytic Four-Channel Hollow Fibers with Highly Dispersed Nickel Nanoparticles Prepared by Atomic Layer Deposition for Dry Reforming of Methane

In this study, highly dispersed nickel (Ni) nanoparticles (NPs) with an average particle size of 4.3 nm were uniformly deposited on the outer surface, the inner channel surface, and inside the pores of 20-cm long 4-channel α-Al 2 O 3 hollow fibers (HFs) by atomic layer deposition (ALD) for dry reforming of methane (DRM). Cerium oxide (CeO 2 ) was added to promote the catalytic performance of Ni/Al 2 O 3 -HF catalysts. Rationally designed filling methods, by tuning the reactor size and inert fillings, can reduce the catalyst bed voidage in a fixed bed reactor for better reactant gas distribution, effectively utilize the Ni reactive sites, and achieve excellent catalytic performance. It was found that the CeO 2 -promoted Ni/Al 2 O 3 -HF catalyst was highly active and highly stable without deactivation during an overall 400-hr DRM test at 850 ºC. CeO 2 with reversible valence states could participate in surface reactions; especially, the formation of CeAlO 3 provided sufficient surface Ce 3+ for CO 2 activation and enhanced the stability and reusability of the HF catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗