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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Recent Advances in Scalable, High‐Mass Loaded Electrodes for Grid‐Scale Energy Storage

Abstract The increasing electrification of daily life as well as the intermittent characteristic of renewable energy sources require viable solutions for grid‐scale energy storage. Critical considerations for grid storage applications are electrode mass loading and electrode thickness as these features govern battery pack energy density, an important factor in determining manufacturing costs. For this reason, there is increased interest in finding new ways of creating electrodes with high mass loading. In this review, various high‐mass loading fabrication approaches are considered for positive electrode materials used in batteries. The benchmark used for high mass loading is above 20 mg cm −2 , which is higher than the practical limit of conventional tape‐cast electrodes. Several different electrode approaches are described including templating, laser patterning, direct ink writing, and electrodeposition. A variety of materials are covered with the most prominent being LiFe(PO 4 ) (LFP), LiCoO 2 (LCO), and MnO 2 . In research to date, scalable electrochemical performance has been achieved with mass loadings over 100 mg cm −2 . Areal capacities as high as 14.7 mAh cm −2 at 1.82 mA cm −2 have been achieved in non‐aqueous electrolytes and 9.8 mAh cm −2 at 10 mA cm −2 in aqueous electrolytes. These results establish that the mass loading of electrodes can be scaled up without compromising their electrochemical properties.

White, Makena [Department of Materials Science and↗
NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Trends for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss- estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗
NASA NTRSDate not supplied

Battery Cell-to-Pack Scaling Laws for Electric Aircraft

Battery pack gravimetric energy density is one of the most important, yet often miss-estimated design parameters for sizing all-electric aircraft. Proper accounting for thermal, structural, and operational safety margins are frequently lost when extrapolating performance from the cell level to the aircraft level. This paper summarizes the relevant engineering and certification details needed to better account for the penalties associated when assembling battery packs. The relationship between the cell and pack energy density is not linear, as is often assumed. Furthermore, the relationship varies depending on pack requirements, cell chemistry, and architecture. Parametric, high-fidelity models are used to determine optimal battery pack sizes over a range of conditions to better quantify technology scaling effects.

Battery Electric Aircraft↗
DOE OSTI2026

Structure of Layers Formed by [2-(3,6-Disubstituted-9 H -carbazol-9-yl)ethyl]phosphonic Acids on Metal Oxides

[2-(9 H -Carbazol-9-yl)ethyl]phosphonic acid (2PACz) and its derivatives are being used extensively as hole-transport layers in organic and perovskite solar cells due to their ability to modify electrode work function, surface wettability, and in some cases, to improve active-layer adhesion, while minimizing interfacial energy losses. The orientation and coverage of surface modifiers significantly impact these functional properties; however, the detailed structure and packing in these overlayers are challenging to investigate, leading to a lack of understanding of how this structure and packing influence performance and limiting the ability to rationally design molecular modifiers. Here, we investigate monolayers of 2-(9 H -carbazol-9-yl)ethyl phosphonic acid derivatives (from here on referred to as X-2PACz) on indium tin oxide (ITO) and alpha phase aluminum oxide (α-Al 2 O 3 ) using a combination of X-ray photoelectron spectroscopy (XPS), near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, and X-ray reflectivity (XRR). By correlating elemental ratios, molecular orientation, and electron density profiles, we directly quantify surface coverage, layer thickness, and molecular tilt across a series of chemically related monolayers. We find that 2PACz and the X-2PACz derivatives form dense monolayers on α-Al 2 O 3 and ITO, with similar surface coverages on either substrate that are inversely proportional to molecular steric bulk. These surface coverage results indicate that X-2PACz is sterically limited in its monolayer surface packing density, as opposed to site limited. Despite surface packing density differences between molecules, the NEXAFS data show a constant average molecular orientation of 61° to 65° between the plane of the carbazole and the substrate for all the molecules on both substrates. These results increase our general understanding of 2PACz derivatives as they become increasingly useful in high performance solar cells.

NEXAFS↗
NASA NTRSDate not supplied

Design and Analysis of Battery Thermal Management Systems

Thermal management of battery cell packs is a critically needed technology. The purpose of this work is to design new and improved Battery Thermal Management Systems (BTMS) for use in electric airplanes. The BTMS should be 3D printable and hold twelve to sixteen 18650 batteries. To minimize aircraft’s weight, an actively air-cooled battery pack was chosen to avoid the excess weight of water-cooled and phase change material (PCM) cooled packs. As a result, significant changes were made to remove all metal in the pack and replace it with lower density polymer matrix composites (PMCs) which can be additively manufactured. Two air cooled battery pack configurations (traditional propeller fans & bladeless fans) were designed, modeled, and compared. These packs were first modeled in SolidWorks 2021 3D CAD, then imported into COMSOL MultiPhysics to be studied using the “Heat Transfer in Solids and Fluids” module. The design with the bladeless fans eliminated the need to use high conductivity heavy metal to remove unwanted thermal energy. These bladeless fans were designed entirely out of PMCs. This thermal pack design weighs 0.04 kg less than that of the traditional propeller fans design and has increased the battery pack energy density by 8.25 Wh/kg.

Thermal Management System for Battery Packs↗
DOE OSTI2025

Interstitial Atoms and the Frustrated and Allowed Structural Transitions Principle: Tunability in the Electronic Structure of AuCu 3 ‐type Frameworks in Dy 4 T 1− x Ga 12 (T = Ag, Ir)

In this Article, we explore how the chemical pressure (CP) features of an intermetallic phase may provide opportunities to couple perturbations in electron count with the stabilization of the underlying geometrical structure. AuCu 3 ‐type LnGa 3 (Ln = lanthanide or group 3 metal) phases contain octahedral cavities of negative CP held open by overly compressed Ln–Ga interactions, leading to a series of transition metal‐stuffed derivatives. We present new additions to this family with the synthesis and crystal structures of Dy 4 T 1−x Ga 12 with (T, x) = (Ag, 0.29) and (Ir, 0.15), adopting Y 4 PdGa 12 ‐type superstructures of the AuCu 3 ‐type. density functional Ttheory (DFT)‐CP calculations, when adjusted to avoid dipolar CP features, affirm that T atom incorporation provides a mechanism for the relief of packing tensions, while electronic density of states distributions illustrate that the T atoms serve largely as electron or hole donors to the band structure, as needed for them to attain d 10 configurations. The maximum obtainable value for x may be limited by a mismatch between the Fermi energy and pseudogap, in line with the balance of factors envisioned by the frustrated and allowed structural transitions principle. Furthermore, trends in resistivity measurements on T = Ir, Pd, and Ag compounds are interpretable in terms of the varying degrees of disorder arising from x < 1.0.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Superstructural phase transitions in polymer-grafted nanooctahedra

Superlattices of polyhedral nanocrystals exhibit emergent properties defined by their structural arrangements, but native nanocrystal ligands often limit their programmability. Polymeric ligands address this limitation by enabling tunable nanocrystal softness through modifications of polymer molecular weight and grafting density. Here, we investigate phase transitions in polymer-grafted nanooctahedra by varying polymer length, nanocrystal size, truncation, and ligand density. In two-dimensional superlattices, longer polymers or smaller nanooctahedra induce a transition from orientationally ordered to hexagonal rotator lattices. In three-dimensional superlattices, increasing polymer length drives transitions from Minkowski to body-centered cubic and plastic hexagonal close-packed phases, while higher grafting densities further enable transitions to simple hexagonal phases. Polymer brush and thermodynamic perturbation theories, supported by Monte Carlo simulations, uncover the entropic and enthalpic forces that govern these transitions. This work highlights the versatility of polymer-grafted anisotropic nanocrystals as building blocks for designing hierarchical superstructures and metamaterials with customizable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Iodine Close Packing in Hybrid Halide Bismuth(III) and Antimony(III) Semiconductors: (NH 3 (CH 2 ) 7 NH 3 ) 2 Bi 2 I 10 and (NH 3 (CH 2 ) 7 NH 3 ) 2 Sb 2 I 10

Simple features in complex hybrid inorganic–organic crystalline materials provide opportunities for targeted discovery of materials with desired optoelectronic properties. In this study, we report the structure and optoelectronic properties of isostructural (NH 3 (CH 2 ) 7 NH 3 ) 2 Bi 2 I 10 and (NH 3 (CH 2 ) 7 NH 3 ) 2 Sb 2 I 10 . The crystal structures are characterized by corner-connected metal-iodide octahedral chains that form a cubic close-packed iodine inorganic framework. Variable temperature UV–visible diffuse reflectance spectroscopy reveals stark contrasts in the onset of absorption and color changes between the [MX6]3– based structures, due to differences in the interaction of the (NH 3 (CH 2 ) 7 NH 3 ) 2+ organic ammonium cation and the iodine packing of the inorganic framework. Density functional theory (DFT) calculations reveal flat bands reflective of the pseudo-1D crystal structure. Dark microwave conductivity (DMC) and time-resolved microwave conductivity (TRMC) reveal an excitonic character with long carrier lifetimes, consistent with the electronically confined octahedral chains. Comparison of the structural features with those of other diammonium-containing crystals reveals that diammoniumheptane can substitute into structures, displacing inorganic octahedra while retaining a close-packed anion framework. This provides a means for targeting new hybrid materials in which “vacancy-ordering” provides a crystal chemical approach for targeting desirable optoelectronic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2025

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗
DOE OSTI2026

Multiphysics simulation of TRISO fuel compacts and the effects of homogenization on silver release predictions

This work studies the impact of explicit and homogenized modeling approaches on the multiphysics simulation of TRistructural ISOtropic (TRISO) fuel compacts in prismatic High Temperature Gas Reactors (HTGRs) and the silver release predictions. TRISO fuel compacts exhibit complex double heterogeneity that significantly affects heat conduction, neutron transport, and fission products release. In this work, we use Cardinal, a multiphysics tool based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to couple neutron transport and heat conduction. OpenMC is used for neutron transport and the MOOSE heat transfer module is used for the heat conduction. Then, we use BISON for the silver release predictions. Two different modeling approaches—explicit modeling of individual TRISO particles and homogenized representation using effective thermal properties—are compared at high TRISO packing fractions (20% and 40%) across varying power densities. Results demonstrate that homogenization significantly under-predicts peak temperatures, for the case of high power/TRISO, there is a difference of 83.73 K in the maximum temperature. Additionally, homogenization underestimates the silver release predictions compared to explicit modeling. Specifically, the temperature and power density differences lead to significant differences in silver release predictions. This work demonstrates the importance of accurately modeling heterogeneity of the TRISO particles to reliably predict fission product release and assess reactor safety margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Amphiphilic Baskets for Supramolecular Nanoarchitectures at Interfaces: Inverted Monolayer Formation on Water

Interfacial chemistry of molecular baskets remains poorly understood despite their promise for supramolecular applications of detection and sequestration of toxic molecules including those of illicit drugs, organophosphorus compounds, and anticancer agents. We present a fundamental investigation of the interfacial behavior of three amphiphilic supramolecular baskets (ASB 4, 8, and 12), having increasingly longer yet linear alkyl chains at the top of their bowl-shaped cavity. The studies were completed at the air−water interface to elucidate surface activity, interfacial stability, self-assembly, and monolayer organization that drive inverted monolayer formation, in which the molecular arms orient toward the aqueous phase in a configuration opposite to that typically observed for lipids. Herein, surface pressure−area isotherms of ASB 4, 8, 12, deposited on a water surface, were performed in tandem with nonequilibrium relaxation experiments to quantify surface activity, thermodynamic stability, and monolayer compressibility of the baskets’ monolayer assembly. Brewster angle microscopy enabled direct visualization of morphological evolution, aggregation, and packing at the interface. We show that systematic extension of the hydrocarbon arms, from four to 12 methylene groups, progressively modifies intermolecular packing, drives distinct two-dimensional aggregation pathways, and increases number densities at the air−water interface. Atomistic molecular dynamics simulations corroborate many of these experimentally observed trends and provide mechanistic detail on the cooperative roles of basket topology and interfacial concentration in regulating the hydration structure and dynamics within the cavities generated by surface-adsorbing baskets, consistent with observed variations in surface activity and packing. Our results establish how the topology of these unique supramolecules and their concentration govern interfacial organization and offer a rational framework for designing amphiphiles with predictable behavior at soft interfaces.

Hydrocarbons↗
DOE OSTI2025

Defect-Engineered High-Entropy Spinel Oxide@Onion-Like Carbon Catalysts for High-Areal-Energy Rechargeable Zinc–Air Batteries

Rechargeable zinc-air batteries (ReZAB) have emerged as the next-generation batteries with several advantages over the conventional lithium-ion battery. In this work, single nanocrystals of inverse-type high-entropy spinel oxides (HESOx, particle size of 10−12 nm) confined in highly curved defective onion-like carbons (HESOx/OLC AT ) as efficient electrocatalysts for oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and ReZAB, have been synthesized. The HESOx materials were thoroughly characterized using several analytical techniques, including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy (STEM), Raman, and electron paramagnetic resonance (EPR). HESOx/OLC AT catalyst was tested for ReZAB using literature-recommended parameters that would allow for real technological application. These parameters include a current loading of 10 mA cm −2 and a discharge areal energy density of 35 mWh cm geometric −2 , which maps a Li-ion battery pack-level specific energy of 120 Wh kg pack −1 . HESOx/OLC AT electrocatalysts allowed for continuous discharging and charging at a current loading of 10 mA cm −2 with discharge areal energy densities between 37 and 74 mWh cm geometric −2 , thus outperforming the recommended threshold of 35 mWh cm geometric −2 . Considering that most studies (>90%) hardly meet the recommended threshold for technological application of ReZAB, the present work represents one of the top-performing electrocatalysts for ReZAB. The excellent electrocatalytic properties of defect-rich HESOx/OLC AT toward ORR/OER and ReZAB are governed by the strong electronic modulation arising from d-π hybridization, the availability of multiple catalytic sites for intermediates, and weakened d-band centers of the rate-determining intermediates (i.e., *O adsorption for ORR and *OOH formation for OER) compared to the pristine HESOx. This work introduces an effective approach for the design and synthesis of single nanocrystals of high-entropy electrocatalysts for the development of low-cost, robust, and technologically relevant rechargeable zinc−air batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Packing fraction control during additive manufacturing of powder green bodies

The demand for high performance ceramic and metal components with complex geometry necessitates developments in powder handling and green body shaping. Here, in this study, vibrational powder deposition is adapted to three-dimensional printing with the ability to modulate packing fraction during printing. Boron carbide powder with sub-micron primary particle size is printed over a packing fraction range of 25.0 % to 46.5 % (below the poured density to above the tapped density). Solid metal powders are printed at 67.5 %, which enables novel freestanding, vertical-walled features without binder. These capabilities introduce opportunities for more complex geometries in high-performance materials, e.g. large-scale uniaxial hot-pressing of ceramic ballistic armor components with graded thickness and three-dimensional curved or stepped features (by compensating for displacement differences during compaction). This technology also enables multi-material patterning of additive manufacturing powder beds with reduced feedstock quantity requirements and wastage.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interfacial Cation Arrangement Controls Electrocatalytic Kinetics in CO 2 Reduction

The identity of electrolyte cations is known to strongly influence electrocatalytic activity, but the relationship between their interfacial arrangement and observed performance remains poorly understood. Organic cations, with their molecular tunability, provide a powerful platform for systematically probing these effects. Here, we leverage phosphonium-based geminal dications to control interfacial cation arrangement and identify the variables that most strongly influence catalytic rates. As a case study, we examine CO 2 reduction to CO over polycrystalline silver electrodes in dry aprotic acetonitrile. Through a combination of rotating disk electrode measurements, electrochemical impedance spectroscopy, and molecular dynamics simulations, we decouple the effects of cation–electrode distance and interfacial cation density on catalytic rates. We find that smaller, more densely packed cations induce stronger interfacial electric fields, which lower the activation barrier for CO 2 adsorption and increase reaction rates. Using geminal phosphonium dications [C n (P mmm ) 2 ][ClO 4 ] 2 , we demonstrate that both the vertical and lateral positioning of organic cations within the electrical double layer independently affect reactivity. These results demonstrate that electrolyte cation identity primarily influences catalytic kinetics by determining how efficiently charge can be arranged at electrochemical interfaces. Altogether, our findings support an electrostatic view of cation effects in catalysis and provide design principles for next-generation electrolytes.

Cations↗
DOE OSTI2026

Extreme Nanoconfinement Dramatically Enhances Small Molecule Solubility in Nonpolar Polymers

Elucidating gas solubility in confined polymer systems addresses a fundamental gap in polymer physics and has important implications for gas barrier and separation technologies and polymer upcycling reactions. In this study, we examine the solubility of methanol and n-hexane in polystyrene and low-density polyethylene confined within the interstitial pores of disordered silica nanoparticle packings. Using capillary rise infiltration, these polymers are infiltrated into the nanoparticle packings. Gas solubility in these confined polymers is measured using a quartz crystal microbalance. Remarkably, confinement leads to a 10- to ∼100-fold increase in gas solubility. Systematic experiments reveal that pore size plays a dominant role in increasing solubility. In contrast, the molecular weight of the polymer and the surface wetting characteristics of the nanoparticles, achieved by hydrophobically modifying the nanoparticles, have minimal effects. By systematically varying penetrant polarity, polymer crystallinity, pore size, and nanopore surface chemistry, this study isolates geometric nanoconfinement as the dominant factor governing solubility enhancement in confined polymers. Atomistic simulations revealed that confinement and surface−polymer interactions both contribute to enhanced solubility, with polymer packing playing a significant role in modulating gas uptake. Here, these results suggest that the changes in the molecular arrangements of polymer segments underlie the observed trends. This study highlights the potential of confined polymers in engineering the separation performance of membranes and heterogeneously catalyzed polymer upcycling reactions.

alcohols↗
DOE OSTI2025

Statistical Analysis of Intertube Tunneling Contacts in the Macroscopic Electrical Conductivity of Carbon Nanotube Fibers

Here, this study investigates the influence of tunneling contact resistances between carbon nanotubes (CNTs) on electron transport and electrical conductivity of macroscopic carbon nanofibers (CNFs), which profoundly impacts the performance of CNT thin film electronics, CNF electron emitters and cathodes, and energy conversion and storage devices. Utilizing a self-consistent electrical contact model coupling a transmission line model with tunneling current, we calculate the contact resistances of a plethora of CNT-CNT contacts within a CNF fiber, which consists of aligned, densely packed CNTs. A statistical analysis is conducted, using Gaussian distributions to account for variations in contact lengths, tunneling gap distances, and single CNT aspect ratios, to calculate the CNT-CNT contact resistance and the overall resistance of CNT fiber. By scaling our model to a macroscopic level, our results are in good agreement with experimental measurements. Our calculation suggests that while increasing the contact overlap length diminishes individual CNT-CNT contact resistance, it could paradoxically increase macroscopic CNT fiber resistance for a given constant CNF mass density, which is due to that fact that a larger overlap length allows more CNTs to pack along an electrical conduction path per unit length, leading to more tunneling contact junctions connected in series and thus less number of parallel conduction paths within the fiber cross section. Increasing tunneling gap distance increases both individual contact and overall fiber resistance. This research provides a simple design tool for tailoring CNT fiber electrical properties to promote real-world applications using CNTs or similar low-dimensional materials.

36 MATERIALS SCIENCE↗