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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

On-Surface Reactions of Electronically Active Self-Assembled Monolayers for Electrode Work Function Tuning

Self-assembled monolayers (SAMs) help improve the performance of organic electronic devices through interface passivation and enhanced carrier transport. Yet, there is limited information regarding the chemical structure of the SAMs upon functionalization and subsequent thermal treatment. Here, we studied the on-surface reaction of carbazole-derived SAMs on model gold electrodes, focusing on the chemical structure changes induced by thermal treatments. Furthermore, we correlate the microscopic changes with their impact on the electrode’s work function. The carbazole-based SAMs first transform into organometallic complexes. At higher annealing temperatures, SAMs convert to oligomeric complexes. The observed chemical reactions significantly reduce the electrode work function and facilitate electron injection in n-type organic thin-film transistors. Our results highlight the on-surface synthesis of electronically active SAMs as an alternative approach for modifying the work function of electrodes for organic electronics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Strong parametric dispersive shifts in a statically decoupled two-qubit cavity QED system

Qubits in cavity quantum electrodynamic (QED) architectures are often operated in the dispersive regime, in which the operating frequency of the cavity depends on the energy state of the qubit, and vice versa. The ability to tune these dispersive shifts provides additional options for performing either quantum measurements or logical manipulations. Here we couple two transmon qubits to a lumped-element cavity through a shared superconducting quantum interference device (SQUID). In this work, our design balances the mutual capacitive and inductive circuit components so that both qubits are statically decoupled from the cavity with low flux sensitivity, offering protection from decoherence processes. Parametric driving of the SQUID flux enables independent, dynamical tuning of each qubit’s interaction with the cavity. As a practical demonstration, we perform pulsed parametric dispersive readout of both qubits. The dispersive frequency shifts of the cavity mode follow the theoretically expected magnitude and sign. This parametric approach creates an extensible, tunable cavity QED framework with various future applications, such as entanglement and error correction via multi-qubit parity readout, state and entanglement stabilization, and parametric logical gates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains

Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen amazing improvements over the last few years. This is due to models such as BERT that achieve deep knowledge transfer by using a large pre-trained model, then fine-tuning the model on specific tasks. The BERT architecture has shown even better performance on domain-specific tasks when the model is pre-trained using domain-relevant texts. Here, inspired by these recent advancements, we have developed NukeLM, a nuclear-domain BERT model pre-trained on 1.5 million abstracts from the DOE Office of Scientific and Technical Information (OSTI) database. This NukeLM model is then fine-tuned for the classification of research articles into either binary classes (related to the nuclear fuel cycle (NFC) or not) or multiple categories related to the subject of the article. We show that continued pre-training of a BERT-style architecture prior to fine-tuning results in greater performance in both article classification tasks. This information is critical for properly triaging manuscripts, a necessary task for better understanding citation networks that publish in the nuclear space and uncovering new areas of research in the nuclear (or nuclear relevant) domain.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Multiphase layered transition metal oxide positive electrodes for sodium ion batteries

Abstract Multiphase layered transition metal oxides (LTMOs) for sodium ion battery (SIB) positive electrodes with phase interfaces across multiple length scales are a promising avenue toward practical, high‐performance SIBs. Combinations of phases can complement each other's strengths and mitigate their weaknesses if their interfaces are carefully controlled. Intra‐ and interparticle phase interactions from nanoscale to macroscale must be carefully tuned to generate distinct effects on properties and performance. An informed design strategy must be paired with relevant synthesis techniques and complemented by spatially resolved characterization tools to manipulate different length scales and interfaces. This review examines the design, synthesis, and characterization strategies that have been demonstrated for the preparation of heterogeneous, multiphasic LTMOs with phase interfaces across varied length scales.

25 ENERGY STORAGE↗
DOE OSTI2020

Evaporation Sub-model Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions (Final Report)

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation sub-model, which has been implemented in the Converge software.

42 ENGINEERING↗
DOE OSTI2020

Evaporation Submodel Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD (Computational Fluid Dynamics) spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF (Volume of Fluid) modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation submodel, which has been implemented in the flow solver.

42 ENGINEERING↗
DOE OSTI2025

Deepening the LUMO: Brominated Naphthalene Diimide Electron Transport Layers for Low-Hysteresis Perovskite Solar Cells

Precise energy level alignment at the interfaces between the charge transport layers, active layer, and electrodes plays a key role in maximizing photovoltaic performance and operational stability in perovskite solar cells (PSCs). Organic electron transport layers (ETLs) have received little attention compared to their inorganic counterparts but offer the unique advantage of facile functionalization for fine-tuning of electronic properties. Here, we report the design, synthesis, and characterization of two benzyl-phosphonic acid (BnPA)-functionalized naphthalene diimide (NDI) derivatives, NDI-(BnPA) 2 and Br 2 -NDI-(BnPA) 2 as organic ETLs for PSCs in an n-i-p device configuration. Bromination of the NDI core at the 4,9-positions deepens the lowest unoccupied molecular orbital (LUMO) in Br 2 -NDI-(BnPA) 2 by 0.29 eV, enabling improved energy alignment with the conduction band minimum of the perovskite. Devices incorporating Br 2 -NDI-(BnPA) 2 demonstrated enhanced short-circuit current density (JSC), reduced hysteresis, and a maximum power conversion efficiency of 13.67%, compared to 13.20% for the unsubstituted NDI-(BnPA) 2 analog. The deeper LUMO of Br 2 -NDI-(BnPA) 2 is hypothesized to facilitate more efficient electron extraction, suppress interfacial charge accumulation, and reduce field-driven ion migration, collectively contributing to the observed reduction in hysteresis. These results highlight the effectiveness of combining molecular-level LUMO tuning with robust interfacial anchoring to advance the performance and durability of organic ETLs in PSCs.

Deposition↗
DOE OSTI2020

Modulating Energy Level on an A‐D‐A′‐D‐A‐Type Unfused Acceptor by a Benzothiadiazole Core Enables Organic Solar Cells with Simple Procedure and High Performance

Unfused‐ring acceptors (UFAs) have gained considerable research attention as they offer simple chemical structures through simplified synthesis methods, which would boost the commercialization of organic solar cells (OSCs). Recently, a new small molecule acceptor (SMA) named Y6 was reported, yielding high‐performance OSCs. Herein, the Y6‐like A‐DA′D‐A framework is developed to A‐D‐A′‐D‐A‐type backbone adopted in constructing UFAs. Two new Y6‐like UFAs are synthesized within four steps and the effect of noncovalent atoms at the central electron‐deficient core on material properties and device performances is studied. It is found that the introduction of fluorine atoms can bring larger red‐shift in the absorption spectra and better aggregation of the resulting UFA film states compared with those of oxygen atoms. Interestingly, the variations in the noncovalent interaction atoms induce different intermolecular charge transfer between donors and UFAs. When blended with another economical donor, PTQ10, F substitution at the benzothiadiazole ring is more effective than O substitution, leading to the increased short‐circuit current density ( J SC ) and higher efficiency of over 12%, among the best performances of UFA‐based OSCs. This contribution demonstrates the appropriate introduction of noncovalent interaction is a promising method for tuning energy levels, absorption, and aggregation of UFAs for high‐performance OSCs.

Yu, Han↗
DOE OSTI2025

Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.

biological databases↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2026

A Chemocatalytic Route to Stereoregular Poly(3-hydroxyhexanoate) and Its Statistical and Tri-Block Copolymers

Poly(3-hydroxyhexanoate) (P3HHx)-based poly(3- hydroxyalkanoate)s (PHAs) are biologically produced, commercially implemented PHAs, but little is known in the open literature about the structure and property characterizations of discrete, authentic homopolymer P3HHx, and its copolymers with poly(3- hydroxybutyrate) (P3HB). Here, we introduce a chemocatalytic route to stereoregular (both isotactic and syndiotactic) P3HHx and PHA copolymers with P3HB, including statistical copolymer P3HBHx with various levels of 3HHx incorporation and discrete hard−soft−hard triblock copolymer P3HB-b-P3HHx-b-P3HB, and present extensive characterizations of their structures, thermal properties, and mechanical performance. The synthesis is efficiently achieved by one-pot polymerization of eight-membered di(n-propyl) and dimethyl (for copolymerization) diolides catalyzed by chiral molecular catalysts. Notably, P3HBHx can be rapidly produced in quantitative yield and exhibits high molar mass (M n up to 551 kg/mol) as well as both high modulus (up to 1.39 GPa) and ductility (up to 445%), while P3HB-b-P3HHx-b-P3HB further extends application temperature windows by possessing a unique combination of low T g (−18 °C) and high T m (154 °C) values. These findings highlight the stereomicrostructural and architectural versatility of chemocatalytic routes to PHAs, which, in turn, can be utilized to largely tune the PHA thermal properties and mechanical performance.

Biopolymers↗
DOE OSTI2023

Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.

deep learning↗
DOE OSTI2023

Structural mapping and tuning of mixed halide ions in amorphous sulfides for fast Li-ion conduction and high deformability

Amorphous sulfides are among the most promising candidates for solid electrolytes (SEs) owing to their excellent deformability and acceptable Li-ion conductivity (σion) at room temperature. However, the complex atomic structure of these amorphous materials without long-range ordering results in a lack of structural understanding and difficulties in tuning material properties. Here, in this study, we performed structural mapping of glassy sulfide SEs composed of various anion clusters using a combinatorial atomic level analysis of synchrotron X-ray-based pair distribution function (PDF) and reverse Monte-Carlo (RMC) methods, demonstrating the potential to tune σ ion in glass SEs. First, we prepared a new glassy sulfide with a mixed anion framework of two halogens (Br and I) as atomic anions, with PS 4 3- molecular anions in a Li 2 S–P 2 S 5 -based glass SE. At a specific Br content, [(Li 2 S) 0.658 (LiI 0.9 LiBr 0.1 ) 0.342 ] 0.825 [P 2 S 5 ] 0.175 recorded a σ ion of 2.27 mS cm -1 , the highest value for any glass sulfides reported to date; however, its elastic modulus was still suppressed to 14.48 GPa. PDF and RMC calculations successfully provided structural mapping of anion clusters, including two halogens. Molecular dynamics simulations of each composition confirmed that flexible coordination caused by the rattling of small polarizable Br ions in the mixed halogens of glassy SEs contributed to the superior σ ion . Our results may provide new insights into the design of superior glassy SEs that play key roles in all-solid-state batteries requiring fast Li-ion conduction and high deformability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design, Processing, and Integration of Pouch-Format Cell for High-Energy Lithium-Sulfur Batteries

This project objective was to develop and demonstrate a lithium-sulfur (Li-S) battery in a pouch-format cell capable of achieving an energy density ≥ 500 Wh/kg while achieving a 1,000 cycle life. The research focused on cell optimization and fabrication addressing different technical barriers and challenges including: 1) thin lithium anode optimization; 2) current collector and tab attachment design; 3) cathode porosity control; 4) electrolyte to sulfur ratio control; 5) cell design; and 6) cell fabrication. The project developed and demonstrated various technologies to address these technical barriers and challenges. The project demonstrated a thin lithium anode by vapor deposition, a collector design and validation of laser welding of tab attachment method, a cathode porosity control strategy by binder optimization, a calendaring process control and surface/interface treatment, and an electrolyte to sulfur ratio control with influence on cell energy density. The sulfur cathode was optimized by tuning the formulation, optimizing the calendaring process, and introducing a novel electrode fabrication process. To address the electrolyte performance issue, the electrolyte optimization was achieved with additive and formulation tuning and the introduction of a dual-phase electrolyte system. In addition to the demonstration of the optimized electrode with a novel fast-curing coating process and dual-phase electrolyte, a coating separator was further developed to address the polysulfide shuttling issue. The resulting new cell design with these optimized cell components was demonstrated in the 1 Ah pouch cell with medium sulfur loading and moderate porosity (~ 4.5 mAh cm-2, 65% porosity) and showed an energy density of > 400 Wh/kg (with E/S ratio of 2.8). The Li-SPAN cell configuration was also evaluated in combination with a new dual-phase electrolyte system. Initial coin cell performance demonstrated cycle stability of >300 cycles with an estimated energy density of 300 Wh/kg at the pouch format level. The corresponding 1 Ah pouch format SPAN cells with the new polymer electrolyte were developed which has shown a stable capacity at 800-900 mAh for ~40 cycles so far.

25 ENERGY STORAGE↗
DOE OSTI2024

Review of data-driven models for quantifying load shed by non-residential buildings in the United States

Shifting and shedding power demand in buildings can be cost-effective techniques for grids to function reliably and for end users to earn compensation. Grid operators reimburse customers in proportion to the quantity of load shed. Simple data-driven methods are used to quantify this shed, which is the difference between a measured load during the event and modeled "baseline" that would have occurred in absence of the event. These methods have evolved over the years and in many cases have been integrated with building physics, to make them a hybrid between physics based and empirical models. However, there is no comprehensive analysis that provides guidance to building operators, grid operators and researchers in selecting appropriate models based on their specific needs and available data. Here, this work aims to fill this gap by critically assessing the performance of baseline models put forward from the year 2000 through 2023. The literature reviewed includes reports generated by grid operators, reports from national laboratories and academic journal articles. The work outlines modeling features like the inputs, training period, estimation method, adjustments to fine tune the predictions and metrics to evaluate the performance. A comprehensive list of 50 models has been provided. For each model, the study explores the applicability of the model to weather sensitive buildings, variability in the building profile, timing of the event, and whether the building reduces energy consumption before an event. The work identifies the situations in which a particular model works and draws lessons based on evidence of performance. Finally, recommendations to aid in model selection are given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.

Parpart, Gavin G.↗
DOE OSTI2023

CMS PYTHIA 8 colour reconnection tunes based on underlying-event data

New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the PYTHIA 8 event generator, are obtained based on the default CMS PYTHIA 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies $\sqrt{s}=$7 and 13 TeV, and by the CDF experiment at 1.96 TeV are used to constrain the parameters of colour reconnection models and multiple-parton interactions simultaneously. The new colour reconnection tunes are compared with various measurements at 1.96, 7, 8, and 13 TeV including measurements of the underlying-event, strange-particle multiplicities, jet substructure observables, jet shapes, and colour flow in top quark pair ($t\bar{t}$) events. The new tunes are also used to estimate the uncertainty related to colour reconnection modelling in the top quark mass measurement using the decay products of $t\bar{t}$ events in the semileptonic channel at 13 TeV .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗