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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials.

high-entropy materials↗
DOE OSTI2025

Direct ink writing of shear exfoliated two-dimensional nanomaterial- elastomeric multifunctional nanocomposite

Direct ink writing (DIW) of polymer nanocomposites with high loadings of two-dimensional (2D) nanofillers (graphene and hexagonal boron nitride (hBN)) is challenging because of potential clogging, use of hazardous solvents, and agglomeration. Here, in this work, a shear exfoliation and sieving method to prepare DIW ink with high loading of nanofillers produced from low-cost bulk layered materials such as graphite and bulk hBN powder for successful DIW printing without the use of any solvents, binders, or plasticizers. The single-step exfoliation technique resulted in a composite with substantial layer reduction along the c-axis, as confirmed by SEM, TEM, XRD, and Raman analysis. Incorporating exfoliated graphene (40 wt%) increased viscosity by ∼6 orders of magnitude due to enhanced particle–matrix interactions, leading to pronounced yield stress behavior and a yield stress of approximately 1598 Pa, which enabled excellent shape retention during extrusion. Using the DIW technique, porous structures such as desalination membranes, self-sensing bone scaffolds, thermal management coating, and serpentine strain sensors were fabricated. When tested in a direct contact membrane distillation setup, the fabricated membrane demonstrated a promising permeate flux of 21.85 Lm −2 h −1 and a salt rejection of 74.3 %. The fabricated serpentine sensor exhibited stable signal variations under cyclic tensile loading, with a working range of 0–200 % strain and a maximum gauge factor of 43,735. A cell culture test using the printed bone scaffold demonstrated promising cell attachment and proliferation. The DIW printed hBN nanocomposite exhibited reversible shape change under heat, demonstrating potential 4D printing capability and efficient thermal management when exposed to high heat or flame.

Desalination↗
DOE OSTI2024

An Impedance Humidity Sensor Based on CVD Grown WSe 2 2D Films

2D materials-based planar devices have been sought after for gas and humidity sensor applications owing to their high sensitivity due to their ultrathin nature and increased surface area. Herein, 2D WSe 2 films-based planner devices were fabricated to evaluate their performance for potential application in humidity sensors. The structure and morphology of the WSe 2 sensing films were studied by Raman spectroscopy, photoluminescence, optical microscopy, and scanning electron microscopy (SEM) techniques. The relative humidity-dependent sensing performances of the 2D WSe 2 films were evaluated using current–voltage (I–V), relative impedance, and complex total impedance (electrochemical impedance spectroscopy, EIS) techniques. This sensor showed a very stable and repeatable performance investigated over the period of 18 months. The response and recovery times of the WSe 2 -based impedance sensors were found to be 40 and 30 s, respectively. The WSe 2 sensor exhibits a 2 ± 1% minimum hysteresis at a lower humidity level (50% RH) and around 8 ± 2% maximum hysteresis at relative humidity level (70% RH). This value of hysteresis is in the acceptable range during the initial investigation of any material to explore its potential for application in humidity sensors. The density functional theory (DFT) analysis was performed with the interaction from water molecules of the WSe 2 sample for a better understanding of the sensing mechanism of devices.

2D- WSe2↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Peering into cloud physics using ultra-fine resolution radar and lidar systems

Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process-level arising from limited systematic observations. Most surface-based active remote sensors, including today’s operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and sub-meter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single photon counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Molecular Scale Tuning of Covalent Organic Frameworks for Enhanced Properties

Covalent organic frameworks (COFs) are a type of porous, extended structure material which has demonstrated utility in numerous applications, including sensing and separations. Judicious choice of the linker and node not only fine tune the size of the framework, but also the functionality. We seek to tailor COF materials for applications in gas capture and electrochemical sensing. Herein, we demonstrate the material properties of COFs can be fine-tuned via post-synthetic metal cation doping of the parent structures. We have synthesized several COF structures with varying linkers which allow for the incorporation of metal cations. Structures incorporating Zn 2+ , Mn 2+ , and Cu 2+ have demonstrated utility in improving the CO 2 uptake properties of the COFs, while Ag + doped structures show potential as electrochemical sensors for chloride anions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An engineered lactate oxidase based electrochemical sensor for continuous detection of biomarker lactic acid in human sweat and serum

Lactate levels in humans reveal intensity and duration of exertion and provide a critical readout for the severity of life-threatening illnesses such as pediatric sepsis. Using the lactate oxidase enzyme (Lox) from Aerococcus viridians, we demonstrated its functionality for lactate electrochemical sensing in physiological fluids in a lab setting. The structure and dynamics of LOx were validated by crystallography, X-ray scattering, and hydroxyl radical protein footprinting. This provided a validated protein template for understanding and designing an enzyme-based electrochemical sensing elements. Using this template, LOx enzyme variants were generated and compared. Comparison of the variants demonstrates that one exhibits effective lactate sensing at significantly reduced operating voltages. Additionally, we demonstrate that the four hexahistidine-tags on each enzyme tetramer are sufficient for immobilization to create a durable, functional sensor, with no need for a covalent attachment, enabling self-immobilization and eliminating the need for additional immobilization steps. The functionality of the LOx enzyme variants was verified at physiological lactate concentrations in both human serum (0–4 mM) and artificial sweat (0–100 mM) using 3-electrode setups for analysis of the three variants in parallel. Accuracy of measurement in both artificial sweat and human serum were high. Employing a microfluidic flow cell, we successfully monitored varying lactate levels in physiological fluids continuously over a 2h period. Overall, this optimized LOx enzyme, which self-immobilizes onto gold sensing electrodes, facilitates efficient and reliable lactate detection and continuous monitoring at reduced operating voltages suitable for further development towards commercial use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Mechanical Toughening of Two-Dimensional Covalent Organic Frameworks Enabled by Graphene-Based Sandwich-Structured Nanocomposites

Low resistance to fracture in two-dimensional covalent organic frameworks (2D COFs) limits their practical applications, especially in mechanically demanding fields, such as flexible electronics and sensing devices. We address this critical limitation by fabricating a sandwich-structured nanocomposite consisting of graphene layers sandwiched between 2D COF layers (2D COFs/graphene/2D COFs) via chemical vapor deposition. Here, our sandwich-structured nanocomposites exhibit a remarkable improvement in modulus E, fracture toughness K IC , and critical energy release rate G C compared to pure 2D COFs. This enhancement is likely due to the graphene layers as the backbone of the sandwich structure effectively carrying and redistributing mechanical stress within the nanocomposite. Our findings demonstrate that a sandwich structure can improve the mechanical robustness of a 2D COF so that it can preserve the functionality and mechanical integrity for applications in stretchable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A baseline structure inventory with critical attribution for the US and its territories

Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure inventory covering the US and its territories to support disaster preparedness, response, and recovery. The dataset contains more than 125 million structures with critical attribution, and is ready to be used by federal agencies, local government and first responders to accelerate on-the-ground response to disasters, further identify vulnerable areas, and develop strategies to enhance the resilience of critical structures and communities. Data can be freely and openly accessed through Figshare data repository, ESRI’s Living Atlas or FEMA’s Geodata platform.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

The influence of kinematics of deformation on polycrystalline halite dynamic recrystallization: Full-field simulation of simple shear versus pure shear

Rock salt, composed mainly of halite, flows viscoplastically over a wide range of geological conditions, strongly impacting the dynamic evolution of sedimentary basins and orogens. Understanding how dislocation creep, which involves dislocation glide, intracrystalline recovery and dynamic recrystallization, influences the microstructure and rheology of halite under various deformation kinematics and temperatures is crucial for enhancing knowledge of salt flow dynamics. Here, this study employs a full-field numerical simulation method to compare the viscoplastic deformation of polycrystalline halite under simple shear and pure shear conditions up to a natural strain of ε = 1.5 at temperatures ranging from 100 °C to 300 °C. The results are presented in terms of crystallographic preferred orientation (CPO), grain shape preferred orientation (SPO), subgrain boundary direction, grain size and strain rate distribution. The results indicate that the crystallographic anisotropy of individual halite crystals is transferred to the polycrystalline scale, resulting in strain localization, particularly in simple shear simulations. The kinematics of deformation affect the evolution and distribution of high strain-rate bands, determining the direction of intragranular substructures and the morphology of strain-induced grain boundaries, with minimal impact on grain size. The intensity of grain boundary migration increases with temperature, significantly influencing grain morphology and size, thereby obscuring strain localization, while having little effect on CPOs. At low strain (ε < 1.0), CPOs relative to both the maximum shortening direction and the grain SPO are similar regardless of the deformation kinematics. At high strain (ε > 1.0), simple shear CPOs exhibit three stronger {100} maxima with a monoclinic symmetry relative to the grain SPO compared to the six {100} maxima with an orthotropic symmetry relative to the grain SPO generated under pure shear. Therefore, microstructures and CPOs can serve as indicators of the strain path in polycrystalline halite under various conditions, aiding in determining the shear sense and elucidating the deformation kinematics of salt structures.

58 GEOSCIENCES↗
DOE OSTI2026

Unraveling plant phenotype to genotype associations with daily hyperspectral traits in Populus trichocarpa

Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (spearman rho = 0.3, p < 1x10 −8 ) between individual wavebands or vegetative indices and growth rate, assessed as the relative change of tree height over the growing season. The growth rate prediction was substantially improved by a regularization multivariate model (spearman rho>0.5, p < 1x10 −16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.

09 BIOMASS FUELS↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Flavor as an Incomplete Structure: Conceptual Questions and the Role of DUNE

Flavor remains one of the most successful yet least understood structures of the Standard Model. The discovery of the Higgs boson completed the electroweak account of mass generation, but did not explain the origin of fermion families, mass hierarchies, or mixing patterns. In this sense, flavor can be regarded as an empirically successful but conceptually incomplete structure. Neutrinos occupy a particularly sensitive place within this problem: their masses are tiny, their mixing is large, and their mass-generation mechanism may differ from that of charged fermions. In this article, we discuss flavor as an open conceptual problem and argue that DUNE, as a phased program spanning precision oscillation measurements and sensitivity to BSM and dark-sector phenomena, provides a powerful framework for testing the self-consistency and possible limits of the present three-flavor description. In particular, the complementarity between the long-baseline program and the Phase I near-detector complex, together with the DUNE-PRISM strategy for controlling interaction-model systematics and enabling data-driven near-to-far predictions, makes DUNE especially well-suited to search for small, correlated departures from the minimal flavor framework.

Montanari, Claudio S. [Fermilab; INFN, Pavia] (ORC↗
DOE OSTI2026

Structure and Dynamics of Water Confined in Transition Metal Carbide MXenes: Implications for Electrochemical Applications

Two-dimensional transition metal carbides and nitrides (MXenes) are an important family of electrochemically active 2D materials. MXenes combine high conductivity with hydrophilicity, making them attractive materials for many applications, including electrochemical energy storage, sensing, desalination, and others. In order to better understand the role of structure on MXene properties, here, we investigated the vibrational properties and diffusion of water in MXenes with differing layer thicknesses and transition metal compositions using inelastic, quasi-elastic, and small-angle neutron scattering. We found that all of the Mo-containing MXenes studied here exhibited comparable vibrational dynamics and diffusion coefficients to each other and to previously studied Ti 3 C 2 T x . However, Ti 2 CT x was distinguished by its faster diffusion and more hydroxyl groups compared to the other MXenes studied. These results can help guide the selection of appropriate MXenes for energy storage and electrochemical water purification applications.

MXenes↗
DOE OSTI2024

Electric field sensitivity of molecular color centers

Molecular color centers with S=1 ground states are promising candidates for quantum sensing of electric fields. These molecules have an electronic structure similar to solid state color centers, but they allow for processing modalities that permit direct interfacing with an analyte. Currently, it is unknown how sensitive these molecules are to electric fields and what molecular properties affect their sensitivity. We perform density functional theory calculations to understand the impact of electric fields on the electronic structure of five nominally tetrahedral molecular color centers exhibiting variable transition metal chemistry and ligand densities. We then extract the Stark parameters from each of these molecules and compare them to molecular properties such as the dipole moment and inner shell stiffness and find that the dipole moment of the molecule largely governs sensitivity. We predict that polar heteroleptic molecules may have electric field sensitivities comparable to solid state color centers such as nitrogen-vacancy centers in diamond.

Physics↗
DOE OSTI2024

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure–function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation–emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. Finally, the results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

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

Structural dynamics at cytosolic interprotomer interfaces control gating of a mammalian TRPM5 channel

The transient receptor potential melastatin (TRPM) tetrameric cation channels are involved in a wide range of biological functions, from temperature sensing and taste transduction to regulation of cardiac function, inflammatory pain, and insulin secretion. The structurally conserved TRPM cytoplasmic domains make up >70 % of the total protein. To investigate the mechanism by which the TRPM cytoplasmic domains contribute to gating, we employed electrophysiology and cryo-EM to study TRPM5—a channel that primarily relies on activation via intracellular Ca 2+ . Here, we show that activation of mammalian TRPM5 channels is strongly altered by Ca 2+ -dependent desensitization. Structures of rat TRPM5 identify a series of conformational transitions triggered by Ca 2+ binding, whereby formation and dissolution of cytoplasmic interprotomer interfaces appear to control activation and desensitization of the channel. This study shows the importance of the cytoplasmic assembly in TRPM5 channel function and sets the stage for future investigations of other members of the TRPM family.

Science & Technology - Other Topics↗