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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comprehensive evaluation of commercially scalable atomic-layer-deposited alumina coating impact on full cell battery performance across varied test conditions

Atomic Layer Deposition (ALD) has emerged as a strategic enhancement method for lithium-ion battery (LIB) materials offering potential benefits and durability benefits for industrial battery production. However, the translation from laboratory achievements to commercial-scale applications has been limited. Here, this study aims to bridge this gap by comprehensively evaluating the effects of commercially scalable Al 2 O 3 ALD coatings using full pouch cell performance as a means to assess the ALD impact. We utilized large-scale slot-die coating techniques to ensure consistent electrode quality and tested four configurations of pouch cells to analyze the individual effects of ALD coating on anode and cathode electroactive materials. Our extensive testing matrix included long-term cycling, fast discharge, fast charge, leakage current, and high voltage tests. While at lower C-rates (<~1C), the influence of Al 2 O 3 coatings on cell performance is not significant. Fast charging conditions reveal that the anode ALD coating significantly enhances performance via a passivating effect, while on the cathode, it is detrimental, potentially due to increased resistance of the thin interfacial layer formed during the ALD processing. Leakage current and high-voltage tests show that the application of ALD coatings on either anode or cathode effectively minimizes side reactions at the electrode-electrolyte interface. Additionally, ALD coatings significantly mitigate concentrated and localized lithium plating on the anodes. These insights provide a valuable understanding of the potential of ALD technologies in LIB manufacturing to tailor cell performance, paving the way for safer, more efficient, and cost-effective battery solutions.

25 ENERGY STORAGE↗
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2026

Scalable slot-die coating of phyllosilicate membranes for selective ion separations

Abstract Two-dimensional (2D) materials have recently drawn attention as candidate materials for molecular-scale membrane separations due to their tunable nanoscale interlayer properties. Phyllosilicates, a broad class of naturally abundant 2D clay minerals, offer significant advantages over synthetic 2D materials, including low cost, stability, and environmental compatibility. While these properties make phyllosilicates attractive for industrial-scale nanofiltration applications, phyllosilicate-based membranes have, to date, only been fabricated at the lab scale via vacuum filtration. Scalable fabrication methods are essential to advance the technical maturity of phyllosilicate membranes and bring these promising materials closer to large-scale adoption. Herein, we have successfully scaled up phyllosilicate (vermiculite) membrane fabrication via slot-die coating and roll-to-roll coating of an ethylenediamine–vermiculite (EDAVM) mixture onto nylon. The coated membranes are 1–2 μm $ \unicode{x03BC} \mathrm{m} $ mu m in thickness and exhibit similar structure and performance to vacuum-filtered EDAVM membranes. The membranes also show selectivity for monovalent ions over multivalent ions in binary salt mixtures. This work represents a major step toward scaling up phyllosilicate membranes for industrial ion separation applications such as resource recovery from water.

Booth, Austin [Princeton University]↗
DOE OSTI2025

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗
DOE OSTI2026

Scalable Hot-Water-Repellent Superhydrophobicity via Thermal Insulation

Superhydrophobic surfaces, which rely on a combination of surface texture and chemistry, often lose their repellent behavior when contacted by hot water (≳40 °C) because the impinging hot water replaces the requisite air layer within the surface texture via evaporation and recondensation. In contrast to previous approaches targeting this condensation-induced failure mode that rely on intricately tailored surface structures or complex chemical treatments, we present a scalable approach based on thermal design: the multilayered insulated superhydrophobic (MISH) coating mitigates condensation-induced failure by preventing heat transfer. Superhydrophobicity is retained at impinging water temperatures up to 90 °C, with durability demonstrated via long-term (>1 million impacts) droplet impingement experiments. We explain the mechanism for this approach with a detailed thermal model; the model reveals that the underlying physical behavior is self-similar across coating parameters and impinging fluid temperatures. Finally, the MISH coating accommodates curved geometries and large surfaces, and it is over 4 orders of magnitude less expensive than cleanroomnanofabricated alternatives, indicating promise for practical use in the energy sector, chemical processing, and the food and medical industries.

coating materials↗
DOE OSTI2024

Scalable Surface Micro-Texturing of LLZO Solid Electrolytes for Battery Applications

A challenge for lithium lanthanum zirconate (LLZO)-based solid-state batteries is to increase the critical current density (CCD) to enable high current cycling. A promising strategy is to modify the LLZO surface morphology to provide a larger contact area with the Li metal. Here, a surface-textured thin LLZO electrolyte was prepared through an easily scalable process. The texturing process is a simple pressing of green LLZO tapes between micro-textured substrates. A variety of textures can be produced, depending on the type of substrate, and texturing can be on either one side or both sides. For this work, after pressing and sintering, several micro-patterns are formed on thin LLZO (~118 μm thick). The properties of the various samples were characterized to investigate the impact of surface texturing, and the most promising ones were selected for electrochemical testing in symmetrical lithium cells and full cells. Li symmetric cells using a coarse ridge-textured LLZO exhibit ~2.5 times increased CCD compared to planar non-textured LLZO, and a solid-state full cell shows stable cycling and improved rate performance. Finally, we believe this process offers a favorable trade-off of processing complexity vs structural optimization to maximize CCD.

25 ENERGY STORAGE↗
DOE OSTI2024

4 V Na Solid State Batteries Enabled by a Scalable Sodium Metal Oxyhalide Solid Electrolyte

All-solid-state sodium batteries (ASSSBs) are viable candidates for large scale energy storage that could vie with lithium. Ductile solid catholytes for such cells that can be prepared without extensive ball milling and directly paired with high voltage sodium cathodes are lacking, however. We report a new amorphous fast Na-ion conducting metal oxychloride that meets these criteria, synthesized through a scalable and low-cost route based on a spontaneous solid-state reaction with simple short mixing and 100 °C annealing. It has an ionic conductivity of 1.2 mS·cm–1 and low activation energy of 0.31 eV. Due to its dual O2–/Cl– framework, it exhibits a high anodic potential of 4 V vs Na+/Na and good chemical/electrochemical compatibility with high voltage sodium cathode materials. ASSSBs consisting of the oxychloride solid electrolyte paired with a high voltage P2–Na2/3Ni1/3Mn2/3O2 cathode showed stable long-term cycling with a 4.0 V vs Na3Sn cutoff potential and even to 4.3 V.

solid-state electrolyte, solid-state batteries, Nu↗
DOE OSTI2024

Toward Mass-Production of Transition Metal Dichalcogenide Solar Cells: Scalable Growth of Photovoltaic-Grade Multilayer WSe2 by Tungsten Selenization

Semiconducting transition metal dichalcogenides (TMDs) are promising for high-specific-power photovoltaics due to their desirable band gaps, high absorption coefficients, and ideally dangling-bond-free surfaces. Despite their potential, the majority of TMD solar cells to date are fabricated in a nonscalable fashion, with exfoliated materials, due to the lack of high-quality, large-area, multilayer TMDs. Here, we present the scalable, thickness-tunable synthesis of multilayer WSe2 films by selenizing prepatterned tungsten with either solid-source selenium at 900 degrees C or H2Se precursors at 650 degrees C. Both methods yield photovoltaic-grade, wafer-scale WSe2 films with a layered van der Waals structure and superior characteristics, including charge carrier lifetimes up to 144 ns, over 14x higher than those of any other large-area TMD films previously demonstrated. Simulations show that such carrier lifetimes correspond to ~22% power conversion efficiency and ~64 W g-1 specific power in a packaged solar cell, or ~3 W g-1 in a fully packaged solar module. The results of this study could facilitate the mass production of high-efficiency multilayer WSe2 solar cells at low cost.

carrier lifetime↗
DOE OSTI2025

Scalable freeform optimization of wide-aperture 3D metalenses by zoned discrete axisymmetry

We introduce a novel framework for design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter, by breaking the lens into a sequence of radial “zones” with 𝑛-fold discrete axisymmetry, where 𝑛 increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry, while avoiding the compromises of the locally periodic approximation (LPA) or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain (FDTD) solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter and centimeter-scale, poly-achromatic, 3D freeform metalenses which outperform the state of the art. By demonstrating the scalability and resulting optical performance enabled by our “zoned discrete axisymmetry” (ZDA) and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale meta-optics and next-generation photonic technologies.

Sun, Mengdi [Wesleyan University]↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2024

Scalable electrified cementitious materials production and recycling

The production of Portland cement, the industry-standard cement, contributes ~8% of global CO 2 emissions through fossil-fuel heating and decomposition of limestone (the primary cement raw material). Decarbonization, e.g., via direct electrification, of this 200-year-old liming routine is extremely challenging at the industry scale. We propose a scalable electrochemical decarbonization approach to circumvent the limestone use by switching to carbon-free calcium silicates from abundant minerals and recycled concrete. Water electrolysis produces protons and hydroxides to drive a pH gradient that accelerates Ca 2+ ion leaching from calcium silicates and captures atmospheric CO 2 to form carbon-negative CaCO 3 , which serves as the feedstock for cement manufacturing or as the carbon-mineralized product for cement substitution with permanent carbon storage. Value-added co-products amorphous silica and green H 2 further enhance cement performance and supplant fossil fuels for net-zero transition, respectively. The products readily meet present-day regulatory standards and demands, and the approach readily synergizes with business-as-usual cement manufacturing and concrete construction, which are important for upscaling and structural safety, promising ready reception by the public and industries. Blended Portland cement produced through our approach with carbon-negative CaCO 3 and silica demonstrates enhanced resilience and achieves carbon neutrality or negativity when incorporating storage or circulation of CO 2 from cement plant flue gas, respectively. This low-cost, electrochemical cement production approach using abundant ubiquitous raw materials enables electrification, transition to clean fuel, and decarbonization at a gigaton scale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Levelized cost and carbon intensity of solar hydrogen production via water splitting using a scalable and intrinsically safe photocatalytic Z-scheme raceway system

Generating hydrogen from local energy resources such as solar or wind would unlock a low-carbon energy carrier that could be used to reduce greenhouse gas emissions in sectors such as industry and transportation. Yet, the allocation of new or existing power generation solely to hydrogen production remains contentious due to disputes regarding emissions accounting. Photocatalytic (PC) hydrogen production technologies offer a unique solution, as hydrogen is produced directly from solar energy and water, without the need for electricity generation. However, cost projections for all photocatalytic designs to date have suggested that they are not cost competitive compared to conventional electrolysis systems manufactured at scale. Herein, we offer the first illustrative benchmark of cost and carbon intensity of hydrogen produced in a type 2 “Z-scheme” photocatalytic reactor design, which employs suspended semiconducting nanoconductor particles organized into two stacked volumes in a raceway design. The “Z-scheme” system utilizes two separate photoabsorber particles, tuned to drive either the hydrogen evolution reaction or the oxygen evolution reaction individually, connected via a reversible, charge transfer redox couple in solution. Furthermore, the results suggest a highly competitive and scalable technology, that justifies further experimental validation and prototyping in the field.

Carbon↗
DOE OSTI2025

Designs for scalable construction of hybrid quantum photonic cavities

Nanophotonic resonators are central to numerous applications, from efficient spin–photon interfaces to laser oscillators and precision sensing. A leading approach consists of photonic crystal (PhC) cavities, which have been realized in a wide range of dielectric materials. However, translating proof-of-concept devices into a functional system entails a number of additional challenges, inspiring new approaches that combine resonators with wavelength-scale confinement and high quality factors; scalable integration with integrated circuits and photonic circuits; electrical or mechanical cavity tuning; and, in many cases, a need for heterogeneous integration with functional materials such as III–V semiconductors or diamond color centers for spin–photon interfaces. Here we introduce a concept that generates a finely tunable PhC cavity at a selected wavelength between two heterogeneous optical materials whose properties satisfy the above requirements. The cavity is formed by stamping a hard-to-process material with simple waveguide geometries on top of an easy-to-process material consisting of dielectric grating mirrors and active tuning capability. We simulate our concept for the particularly challenging design problem of multiplexed quantum repeaters based on arrays of cavity-coupled diamond color centers, achieving theoretically calculated unloaded quality factors of 106, mode volumes as small as 1.2(λ/neff)3, and maintaining >60% total on-chip collection efficiency of fluorescent photons. We further introduce a method of low-power piezoelectric tuning of these hybrid diamond cavities, simulating optical resonance shifts up to ∼760 GHz and color center fluorescence tuning of 5 GHz independent of cavity tuning. These results will motivate integrated photonic cavities toward larger scale systems-compatible designs.

Greenspon, Andrew S. (ORCID:0000000296317568)↗
DOE OSTI2025

Implementation of scalable suspended superinductors

Superinductors have become a crucial component in the superconducting circuit toolbox, playing a key role in the development of more robust qubits. Enhancing the performance of these devices can be achieved by suspending the superinductors from the substrate, thereby reducing stray capacitance. Here, we present a fabrication framework for constructing superconducting circuits with suspended superinductors in planar architectures. To validate the effectiveness of this process, we systematically characterize both resonators and qubits with suspended arrays of Josephson junctions, ultimately confirming the high quality of the superinductive elements. In addition, this process is broadly compatible with other types of superinductors and circuit designs. Furthermore, our results not only pave the way for scalable superconducting architectures utilizing superinductors but also provide the primitive for future investigation of loss mechanisms associated with the device substrate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2026

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Nextflow↗