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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Chu, Yufei [Stony Brook Univ., NY (United States)]↗
DOE OSTI2026

Validating Simulated Models of Energy Consumption by a Battery Electric Motorcoach: A real-world deployment in a harsh climate.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2021

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Fermi Level Engineering of Passivation and Electron Transport Materials for p-Type CuBi 2 O 4 Employing a High-Throughput Methodology

Metal oxide semiconductors are promising for solar photochemistry if the issues of excessive charge carrier recombination and material degradation can be resolved, which are both influenced by surface quality and interface chemistry. Coating the semiconductor with an overlayer to passivate surface states is a common remedial strategy but is less desirable than application of a functional coating that can improve carrier extraction and reduce recombination while mitigating corrosion. Here, a data-driven materials science approach utilizing high-throughput methodologies, including inkjet printing and scanning droplet electrochemical cell measurements, is used to create and evaluate multi-element coating libraries to discover new classes of candidate passivation and electron-selective contact materials for p-type CuBi 2 O 4 . The optimized overlayer (Cu 1.5 TiO z ) improves the onset potential by 110 mV, the photocurrent by 2.8×, and the absorbed photon-to-current efficiency by 15.5% compared to non-coated photoelectrodes. It is shown that these enhancements are related to reduced surface recombination through passivation of surface defect states as well as improved carrier extraction efficiency through Fermi level engineering. This work presents a generalizable, high-throughput method to design and optimize passivation materials for a variety of semiconductors, providing a powerful platform for development of high-performance photoelectrodes for incorporation into solar-fuel generation systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Twisted Crystalline Organic Semiconductor Photodetectors

Optoelectronic properties of anisotropic crystals vary with direction requiring that the orientation of molecular organic semiconductor crystals is controlled in optoelectronic device active layers to achieve optimal performance. Here, in this work, a generalizable strategy to introduce periodic variations in the out-of-plane orientations of 5,11-bis(triisopropylsilylethynyl)anthradithiophene (TIPS ADT) crystals is presented. TIPS ADT crystallized from the melt in the presence of 16 wt.% polyethylene (PE) forms banded spherulites of crystalline fibrils that twist in concert about the radial growth direction. These spherulites exhibit band-dependent light absorption, photoluminescence, and Raman scattering depending on the local orientation of crystals. Mueller matrix imaging reveals strong circular extinction (CE), with TIPS ADT banded spherulites exhibiting domains of positive or negative CE signal depending on the crystal twisting sense. Furthermore, orientation-dependent enhancement in charge injection and extraction in films of twisted TIPS ADT crystals compared to films of straight crystals is visualized in local conductive atomic force microscopy maps. This enhancement leads to 3.3- and 6.2-times larger photocurrents and external quantum efficiencies, respectively, in photodetectors comprising twisted crystals than those comprising straight crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polymorphs of the n–Type Polymer P(NDI2OD–T2): A Comprehensive Description of the Impact of Processing on Crystalline Morphology and Charge Transport

A systematic study of the polymorphs emerging in P(NDI2OD-T2) (also commercially known as N2200), a prototypical organic semiconducting n-type polymer, is presented. Using a tightly integrated experimental and computational approach, detailed atomistic-level descriptions are provided investigating the three known P(NDI2OD-T2) polymorphs observed at room temperature as a function of thin-film processing. Importantly, over the course of the work, a missing link is uncovered, a fourth polymorph referred to here as Form I-β; this new form is a morphological intermediary observed upon thermal annealing, which evolves from Form I but tends to disappear upon full polymer chain melting. The computationally derived polymorph structures show excellent agreement with experimental X-ray scattering characterization. The relative stabilities of each polymorph are calculated in terms of both the bulk material and the polymorph-air interface. An energy landscape is then constructed to qualitatively compare the thermodynamic versus kinetic origins of each polymorph, and the factors driving (supra)assembly and associated transformations among polymorphs using an approach generalizable to other organic semiconducting polymers. Lastly, the relationships among preferential polymorphic crystallinity, relative chain orientations, and directional charge transport properties in P(NDI2OD-T2) are explored. Altogether, this work provides unprecedented insights into complex structure-processing-transport relationships in a representative semiconducting organic polymer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unique Role of GeO 2 as a Noninvasive Promoter of Nano‐Sized Zeolite Crystals

Abstract The synthesis of zeolites with nano‐sized dimensions is often limited to a narrow design space that conventionally relies upon the design of organics to direct hierarchical materials. Here, it is demonstrated that the addition of an inorganic modifier, germanium oxide (GeO 2 ), to a zeolite growth mixture directs the formation of crystals with ultrasmall dimensions. This effect is observed for zeolites ZSM‐11 and ZSM‐5 over a range of synthesis conditions wherein the role of GeO 2 in zeolite crystallization deviates from its typical function as a heteroatom. Notably, the final products contain trace amounts of Ge, which indicates the inorganic modifier does not compete for sites in the zeolite framework based on its formation of a discrete phase that enables GeO 2 recovery. Catalytic tests using the methanol‐to‐hydrocarbons reaction reveal significant enhancement in the performance of zeolite catalysts prepared with GeO 2 compared to reported examples of nano‐sized zeolites. These findings highlight a potentially generalizable and commercially viable synthesis method to reduce mass‐transport limitations in zeolites for diverse applications.

Chemistry↗
DOE OSTI2023

Synthesis of Zwitterionic CsPbBr 3 Nanocrystals with Controlled Anisotropy using Surface‐Selective Ligand Pairs

Abstract Mechanistic studies of the morphology of lead halide perovskite nanocrystals (LHP‐NCs) are hampered by a lack of generalizable suitable synthetic strategies and ligand systems. Here, the synthesis of zwitterionic CsPbBr 3 NCs is presented with controlled anisotropy using a proposed “surface‐selective ligand pairs” strategy. Such a strategy provides a platform to systematically study the binding affinity of capping ligand pairs and the resulting LHP morphologies. By using zwitterionic ligands (ZwL) with varying structures, majority ZwL‐capped LHP NCs with controlled morphology are obtained, including anisotropic nanoplatelets and nanorods, for the first time. Combining experiments with density functional theory calculations, factors that govern the ligand binding on the different surface facets of LHP‐NCs are revealed, including the steric bulkiness of the ligand, the number of binding sites, and the charge distance between binding moieties. This study provides guidance for the further exploration of anisotropic LHP‐NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High‐Concentration Antibody Formulation via Solvent‐Based Dehydration

Abstract Although subcutaneous (SC) delivery is the preferred administration route for immunotherapies and other biologics for improved patient compliance and lower healthcare costs, it necessitates high‐concentration antibody formulations. However, high‐concentration antibody solutions face significant instabilities and prohibitively high viscosities. Other approaches for high‐concentration formulations have been developed, including non‐aqueous solutions, which can be irritating or painful, and antibody‐laden hydrogel microparticles, which require centrifugation and are limited to concentrations <300 mg mL −1 . This work presents a new formulation process wherein the antibody is concentrated and encapsulated into hydrogel microparticles via solvent‐based dehydration. The final dosage form is an aqueous particle suspension with a formulation concentration of 360 mg mL −1 . In this process, microparticles are synthesized continuously, and antibody precipitation is realized simultaneously to dehydration, which allows for higher antibody concentrations. Antibody phase behavior and precipitation–dehydration kinetics are analyzed. The antibody is structurally and functionally stable in the microparticle post‐processing and after 4 months. Injectability of the suspension meets clinical standards with glide force <20 N. For the first time, an aqueous antibody formulation at high concentrations comparable to non‐aqueous formulations is presented, ideal for subcutaneous administration. The process is envisioned to be generalizable as a platform for SC delivery in multiple clinical applications.

Zheng, Talia [Department of Chemical Engineering M↗
DOE OSTI2026

Powder‐to‐Film Conversion of Nickel Single‐Atom Catalysts into Binder‐Free and Resistant Electrodes

Although a few binder-free and self-supported single-atom electrodes have been reported, achieving mechanically robust, defect-engineered, and reproducible films that preserve atomic dispersion under electrochemical operation remains challenging. This work addresses this limitation by presenting a versatile and generalizable strategy to transform powders into standalone, defect-engineered thin films hosting atomically dispersed Ni centers within conductive 2D frameworks. The physicochemical and electronic properties of these materials are thoroughly characterized using a comprehensive set of spectroscopic and microscopic techniques and confirmed the homogeneous dispersion and monoatomic nature of the Ni centers (0.94 wt.%) on the electrode films. Electrochemical testing via cyclic voltammetry and electrochemical impedance spectroscopy under a range of experimental conditions revealed that integration of Ni single atoms markedly enhanced performance and stability compared to carbon nanotube-only electrodes, maintaining integrity after 15 h of continuous operation. This improvement is accompanied by a notable reduction in charge transfer resistance (30.50 Ω) and an increase in double-layer capacitance (295.45 µF). Post-electrochemical analyses corroborated the structural integrity and robustness of the electrodes. Overall, this work bridges atomically precise catalysis and device-level electrochemistry, opening a route toward reproducible and scalable single-atom electrodes for sensing and energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

End–to–End Metasurface Design for Temperature Imaging via Broadband Planck‐Radiation Regression

A theoretical framework is presented for temperature imaging from long-wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end-to-end approach that optimizes manufacturable, single-layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. Here, it is also shown that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A General Strategy for Bandgap Engineering Via Anion‐Lattice Doping in High‐Entropy Oxides

Bandgap engineering is a critical tool for tailoring the electronic properties of functional materials, traditionally achieved by modifying the cation sublattice. Here, a generalizable strategy is introduced that leverages facile anion-lattice doping in high entropy materials to modulate the bandgap in high-entropy metal oxides (HEMOs). By incorporating nitrogen into a single-phase high-entropy metal oxide/nitride (HEMO:HEMN) solid solution, a substantial bandgap reduction is achieved from 3.55 eV (HEMO) to ≈2.46 eV (HEMO:HEMN), significantly enhancing electronic conductivity. Unlike conventional bandgap tuning approaches that rely on cation substitution or heterojunction formation, this method exploits anion-mediated entropy stabilization, enabling uniform bandgap narrowing across the entire solid solution. This anion-lattice engineering strategy is broadly applicable to high-entropy systems, providing a new pathway for designing energy materials with tailored electronic properties. The resulting HEMO:HEMN solid solution exhibits a tenfold increase in capacitance and capacity compared to HEMO in supercapacitor and lithium-ion battery tests, demonstrating the transformative potential of anion-driven bandgap modulation for next-generation energy storage and conversion technologies.

energy storage↗
DOE OSTI2020

Quantifying Losses and Assessing the Photovoltage Limits in Metal–Insulator–Semiconductor Water Splitting Systems

Metal–insulator–semiconductor (MIS) photo-electrocatalysts offer a pathway to stable and efficient solar water splitting. Initially motivated as a strategy to protect the underlying semiconductor photoabsorber from harsh operating conditions, the thickness of the insulator layer in MIS systems has recently been shown to be a critical design parameter which can be tuned to optimize the photovoltage. Here we analyze the underlying mechanism by which the thickness of the insulator layer impacts the performance of MIS photo-electrocatalysts. A concrete example of an Ir/HfO 2 /n-Si MIS system is investigated for the oxygen evolution reaction. The results of combined experiments and modeling suggest that the insulator thickness affects the photovoltage i) favorably by controlling the flux of charge carriers from the semiconductor to the metal electrocatalyst and ii) adversely by introducing nonidealities such as surface defect states which limit the generated photovoltage. It is important to quantify these different mechanisms and suggest avenues for addressing these nonidealities to enable the rational design of MIS systems that can approach the fundamental photovoltage limits. The analysis described in this contribution as well as the strategy toward optimizing the photovoltage are generalizable to other MIS systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning

An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.

42 ENGINEERING↗
DOE OSTI2025

Calibration of multisite raters for prospective visual reads of amyloid PET scans

Abstract INTRODUCTION In multicenter Alzheimer's disease studies, amyloid positron emission tomography (PET) visual reads are typically performed centrally by a few experts. Incorporating a broader reader network enhances scalability and generalizability. METHODS Ten neuroimaging experts from eight Alzheimer's Disease Research Centers (ADRCs) visually read 180 amyloid PET scans (30 scans and 15 duplicate scans for each of four tracers, imaged across a wide variety of scanners), using preferred reading software without anatomical imaging or quantitation. Scans were classified as elevated or non‐elevated per tracer‐specific criteria. Inter‐ and intra‐rater agreement was assessed. RESULTS Inter‐rater agreement was substantial (Fleiss’κ = 0.78), with full consensus on 69% of scans. Inter‐rater reliability was substantial to perfect across tracers (Fleiss’κ = 0.70–0.87). Intra‐rater agreement was substantial to perfect (Cohen'sκ = 0.79‐1). Scans with intermediate (10–40 Centiloid) quantitation had lower reader agreement. DISCUSSION A multicenter expert network achieved substantial agreement classifying amyloid PET scans. These scans provide a standard for reader training and reliability assurance in future studies. Highlights Calibration methods ensure reliable amyloid positron emission tomography (PET) visual reads across multiple raters. Substantial agreement is possible across readers using their preferred tools. Agreement is also substantial regardless of the amyloid PET tracer used. Scans with intermediate (10–40 Centiloid) quantitation have lower reader agreement. The calibration set will become a training tool for amyloid PET visual read studies.

Neurosciences & Neurology↗
DOE OSTI2021

Leveraging Free Volume Manipulation to Improve the Membrane Separation Performance of Amine‐Functionalized PIM‐1

Abstract Gas‐separation polymer membranes display a characteristic permeability–selectivity trade‐off that has limited their industrial use. The most comprehensive approach to improving performance is to devise strategies that simultaneously increase fractional free volume, narrow free volume distribution, and enhance sorption selectivity, but generalizable methods for such approaches are exceedingly rare. Here, we present an in situ crosslinking and solid‐state deprotection method to access previously inaccessible sorption and diffusion characteristics in amine‐functionalized polymers of intrinsic microporosity. Free volume element (FVE) size can be increased while preserving a narrow FVE distribution, enabling below‐upper bound polymers to surpass the H 2 /N 2 , H 2 /CH 4 , and O 2 /N 2 upper bounds and improving CO 2 ‐based selectivities by 200 %. This approach can transform polymers into chemical analogues with improved performance, thereby overcoming traditional permeability–selectivity trade‐offs.

Mizrahi Rodriguez, Katherine↗
DOE OSTI2022

Semiconducting Copolymers with Naphthalene Imide/Amide π‐Conjugated Units: Synthesis, Crystallography, and Systematic Structure‐Property‐Mobility Correlations

Abstract In a series of n‐type semiconducting naphthalene tetracarboxydiimide ( NDI )‐dithiophene ( T2 ) copolymers, structural and electronic properties trends are systematically evaluated as the number of NDI carbonyl groups is reduced from 4 in NDI to 3 in NBL (1‐amino‐4,5‐8‐naphthalene‐tricarboxylic acid‐1,8‐lactam‐4,5‐imide) to 2 in NBA (naphthalene‐bis(4,8‐diamino‐1,5‐dicarboxyl)‐amide). As the NDI ‐ T2 backbone torsional angle falls the LUMO energy rises. However, the thienyl attachment regiochemistry also plays an important role in less symmetric NBL and NBA . Electron mobility is greatest for N2200 (0.17 cm 2 V −1 s −1 ) followed by PNBL‐3,8‐T2 and PNBA‐2,6‐T2 (0.11 cm 2 V −1 s −1 ), 0.02 cm 2 V −1 s −1 in PNBL‐4,8‐T2 , and negligible in PNBA‐3,7‐T2 . Charge transport reflects a delicate balance between electronic backbone communication (optimum for N2200 and PNBL‐4,8‐T2 ), backbone planarity (optimum for PNBA‐2,6‐T2 and PNBL‐3,8‐T2 ), LUMO energy (optimum for N2200 ), π–π stacking distance (optimum for PNBA‐2,6‐T2 ), and film crystallinity (optimum for PNBA‐2,6‐T2 and N2200 ). These results offer generalizable insight into semiconducting copolymer design.

Chen, Yao↗