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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sub-wavelength optical lattice in 2D materials

Recently, light-matter interaction has been vastly expanded as a control tool for inducing and enhancing many emergent nonequilibrium phenomena. However, conventional schemes for exploring such light-induced phenomena rely on uniform and diffraction-limited free-space optics, which limits the spatial resolution and the efficiency of light-matter interaction. Here, we overcome these challenges using metasurface plasmon polaritons (MPPs) to form a sub-wavelength optical lattice. Specifically, we report a “nonlocal” pump-probe scheme where MPPs are excited to induce a spatially modulated AC Stark shift for excitons in a monolayer of MoSe 2 , several microns away from the illumination spot. We identify nearly two orders of magnitude reduction for the required modulation power compared to the free-space optical illumination counterpart. Moreover, we demonstrate a broadening of the excitons’ linewidth as a robust signature of MPP-induced periodic sub-diffraction modulation. Our results will allow exploring power-efficient light-induced lattice phenomena below the diffraction limit in active chip-compatible MPP architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A cohesive zone treatment for the material point method involving problems of large deformation and damage

A new algorithm is described that permits the use of cohesive zones in the material point method for problems involving large deformation and fracture. In contrast to previous cohesive zone implementations, this method does not utilize massless surface-element particles. Instead, cohesive tractions are computed using the shape function mappings from a reference grid configuration in combination with explicitly defined particle surface normals and surface positions. These normals and relative surface positions are updated each time step according to particle deformation. The tractions are converted to cohesive forces using the nodal areas and mapped back to particles using the same reference shape function mappings. These forces are then remapped by conventional particle-to-grid interpolation as external forces using the current-configuration shape-function mappings. This allows highly compliant cohesive zones to function over jump displacements larger than a grid cell. Upon damage, these interfaces can revert to conventional multi-field contact surfaces. This approach is general and readily applies to two and three dimensions as well as being compatible with damage-field gradient partitioning offering exceptional computational flexibility. The framework for this method enables other capabilities, such as improved contact precision using explicitly defined surface normals and positions, and a method to mitigate spurious material damage at weak discontinuities between stiff brittle materials and soft or compliant materials.

Cohesive zone↗
DOE OSTI2025

Unlocking enhanced gas capture via core scrambling of porous-organic cages

The demand for low-cost, low-energy, and highly selective gas capture and separations is an ongoing driver of porous material development. Porous liquids have been identified as a promising gas separation material by creating permanent porosity in inorganic solvents through inclusion of nanoporous materials that sterically exclude solvent from their internal porosity. Among the nanoporous materials that can be used to form porous liquids, porous-organic cages (POCs) have been one of the most popular due to the inherent tunability of POCs. “Scrambled” POCs with varying functionalities on the POC vertices have been developed and incorporated into porous liquid compositions, increasing their gas adsorption capacity. An unexplored avenue to tailor the properties of porous liquids is through scrambling the functionality of the core of the POC. Here, therefore, we have synthesized a new POC, a CC3-OH derivative with scrambled hydroxides on the core and evaluated the impact on the CO 2 uptake capacity in silicon oil-based porous liquids. Core scrambling of the POC resulted in a twofold increase CO 2 adsorption capacity in the porous liquid, an emergent property that is a dramatic increase beyond a linear combination of the gas adsorption capacity of the neat solvent and the POC. Density functional theory modeling of the CC3 POC and its hydroxide-based derivatives identified that free rotation of the linker hydroxide allowed for forced interaction between the CO 2 molecule and the hydroxide in the pore window. Solvation of the POC may release scrambled core hydroxides from intramolecular bonding with a neighboring imine, allowing for increased gas uptake in the porous liquid over the neat POC. These results identify a key structural relationship of POCs that enables emergent properties in porous liquids and can guide future development of liquid phase gas capture and separation materials for environmental and industrial applications.

Gas capture↗
DOE OSTI2025

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y 2 O 3 interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (𝐸 𝑆𝑒𝑔 ) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining 𝐸 𝑆𝑒𝑔 . Moreover, the segregation behavior at a specific Fe/Y 2 O 3 interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y 2 O 3 interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Multiscale study of helium diffusion in Ni-Cr alloys: Short-range trapping versus long-range channeling

Ni-Cr alloys are widely employed as structural materials in fast nuclear reactors but are vulnerable to high-temperature helium (He) embrittlement (HTHE) under fast neutron irradiation. A comprehensive understanding of He diffusion in Ni-Cr alloys, which governs the kinetics of HTHE, is therefore essential for developing resilient materials and preventing failure. In this work, we reveal the underlying mechanisms of He diffusion in pure Ni and Ni-Cr alloys by integrating density functional theory (DFT) with atomic kinetic Monte Carlo (AKMC) simulations. Our findings uncover a non-monotonic dependence of He diffusivity on Cr concentration, contradicting the monotonic trends predicted by DFT-parameterized theories. At low Cr concentrations, He diffusion is dominated by short-range trapping, characterized by multiple trapping sites and a distinct mechanism within the first nearest neighbor of Cr, differing from that in pure Ni. At high Cr concentrations, these local traps become interconnected, forming long-range fast diffusion channels that enhance He mobility. The competition between localized trapping and extended channeling results in a diffusivity that first decreases, then increases with rising Cr content. These atomic-scale insights offer critical guidance for the design of radiation-tolerant Ni-based alloys. Moreover, the combined DFT-AKMC methodology and the concept of random walker diffusion through interconnected energy basins present a broadly applicable framework for studying transport phenomena in disordered systems.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Temperature Dependent Spin Dynamics in La 0.67 Sr 0.33 MnO 3 /Pt Bilayers

Complex ferromagnetic oxides such as La 0.67 Sr 0.33 MnO 3 (LSMO) offer pathways for creating energy‐efficient spintronic devices with new functionalities. LSMO exhibits high‐temperature ferromagnetism, half metallicity, sharp resonance linewidth, low damping, and a large anisotropic magnetoresistance response. Combined with Pt, a proven material with high spin‐charge conversion efficiency, LSMO can be used to create robust nano‐oscillators for neuromorphic computing. Ferromagnetic resonance (FMR) and device‐level spin‐pumping FMR measurements are performed to investigate the magnetization dynamics and spin transport in NdGaO 3 (110)/LSMO(15 nm)/Pt(0 and 5 nm) thin films ranging from 300 K to 90 K and compare the device performance with Py(7 nm)/Pt(5 nm) sample. The spin current pumped into Pt is quantified to determine the temperature‐dependent influence of interfacial interactions. The generated spin current in the micro‐device is maximum at 170 K for the optimally grown LSMO/Pt films. Additionally, this bilayer system exhibits low magnetic Gilbert damping (0.002), small linewidth (12 Oe), and a large spin Hall angle (≈3.2%) at 170 K. Ex situ deposited LSMO/Pt bilayers demonstrate excellent dynamic response, exhibiting fourfold enhancement in signal output, eightfold reduction in damping, and a threefold reduction in linewidth as compared to the Pt/Py system. Such robust device‐level performance can pave way for energy‐efficient spintronic‐based devices.

complex perovskite thin films↗
DOE OSTI2026

Dual Chemical Looping/Catalytic Process for Alkylation of Benzene With Ethane and Propane Yielding Ethylbenzene and Cumene Over Copper‐Containing Mordenite

Given the sustained demand for alkylated aromatics and the strained olefin market, there is an urgent need to develop efficient one‐step processes for the direct alkylation of aromatics using alkanes instead of olefins. Such technologies offer greater energy efficiency and sustainability by eliminating the need for separate, energy‐intensive alkane dehydrogenation steps. In this work, we report a dual chemical looping / catalytic process that couples alkane dehydrogenation with aromatic alkylation over a copper‐containing mordenite yielding up to 25% of alkylated aromatics with >97% selectivity per cycle. In situ MAS NMR and FTIR spectroscopies combined with DFT calculations showed that the alkylation of benzene with alkanes proceeds via a π‐bounded Cu(I)‐olefin intermediate, which subsequently interacts with benzene, catalyzed by Brønsted acid sites, leading to alkylated products that readily desorb from the active material into the gas phase. DFT calculations show that alkylation mediated solely by Cu(I) has prohibitively high barriers (>1.8 eV), whereas a bi‐functional pathway involving both Cu(I) and Brønsted acid sites can proceed with significantly lower barrier (0.8 eV) through a concerted C–C bond formation and proton transfer step.

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

Nano zero valent iron electrodeposition at boron doped diamond electrodes

Here, in this study, we present an ecofriendly and simple electrochemical method for synthesizing nano zero-valent iron particles (nZVIs) directly on boron-doped diamond (BDD) electrode surfaces. A BDD electrode served as the substrate for electrodeposition using a 5 mM FeCl 3 /0.1 M KCl solution and chronoamperometry at an applied potential of −1.3 V versus Ag/AgCl (1 M KCl), as determined by cyclic voltammetry (CV) and supported by Pourbaix diagram analysis. The electrochemical behavior and surface modification were characterized using CV, electrochemical impedance spectroscopy (EIS), and surface analysis and microscopy techniques. The results confirm that BDD electrodes can serve as effective platforms for a controlled deposition of 56 nm nZVIs, offering a promising strategy for the development of advanced materials for environmental remediation, catalysis, and sensing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechanism of Vapor-Phase Infiltration of Organometallic Hf in Poly(Methyl Methacrylate) for Hybrid Resist Applications

Inorganic–organic hybrid thin films synthesized by vapor-phase infiltration (VPI) of metal oxides into organic photoresists, such as poly(methyl methacrylate) (PMMA), have recently demonstrated their utility in extreme ultraviolet lithography, critical for angstrom-era semiconductor device miniaturization. Hafnium oxide infiltration has been reported recently for this purpose, but its detailed VPI mechanism has remained largely unexplored. In this study, we investigated the VPI characteristics and mechanisms of tetrakis(dimethylamido)hafnium (TDMAHf)─the hafnium precursor predominantly used for VPI in the field─into PMMA and examined its impact on electron-beam lithography (EBL) exposure behavior. VPI was performed at temperatures ranging from 85 to 150 °C, with chemical interactions characterized using infrared reflection-absorption spectroscopy, and resist patterning performance was evaluated through EBL dose-sensitivity assessments. The results indicate that TDMAHf forms a reversible adduct with PMMA at temperatures up to 120 °C, whereas at 150 °C, covalent bond formation occurs, most likely via dealkylation that leads to acetate formation. EBL studies reveal that resist sensitivity is influenced by both infiltration temperature and developer selection, with aqueous isopropyl alcohol development demonstrating enhanced sensitivity compared to organic solvent-based development. The optimized infiltration protocol at 120 °C ensures a uniform inorganic distribution without compromising resist dissolution. These findings not only help refine hybrid resist patterning performance but also offer insights potentially applicable to the VPI of other homoleptic metal-amide organometallic VPI precursors that include TDMA ligands.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Unified Workflow for Sensitivity-Based Kinetic Analysis in Microkinetic Models

Degrees of rate control (DRC), apparent activation energies, and apparent reaction orders are established local sensitivity diagnostics for interpreting microkinetic models, but applying them routinely to large mechanisms often requires substantial reaction-specific bookkeeping, perturbation design, and postprocessing. Here, in this study, we present a unified derivative-based workflow that evaluates these quantities from a single compiled reaction-network model and target-rate definition. For any user-provided microkinetic model, the workflow compiles the mechanism into stoichiometrically consistent mass-action rate equations, solves the surface dynamics, and uses automatic differentiation to compute sensitivities with respect to rate constants, temperature, and gas partial pressures. By combining their calculations in the same framework, the workflow clearly demonstrates the relationships between different DRCs and the apparent activation energy. Using existing examples of propylene partial oxidation and methane oxidation on Pd(100), we verify expected transient redistribution of rate control, distinguish net Campbell DRCs from one-sided directional sensitivities, and show how apparent activation energy can be reconstructed either from one-sided DRCs or from state-based DRCs while critical mechanistic insights are obtained consistently. In the methane oxidation case, a pathway-subset test further illustrates how a simplified mechanism preserves key kinetic signatures of a full model, showing the potential of our user-friendly tool for model construction beyond kinetic analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Discovering CO Adsorption and Desorption Pathways from Chemical Reaction Neural Network Modeling of Transient Kinetics Spectroscopy

Here, we demonstrate a data-driven approach to interpreting surface reactions by combining time-resolved gas pulsing infrared spectroscopy with chemical reaction neural networks (CRNNs). Using CO adsorption and desorption on Pd(111) at 460–490 K as a model system, we show how transient kinetic data can reveal detailed reaction mechanisms. Starting with a simple one-species model, we systematically evaluate increasingly complex mechanisms involving hollow and bridge site adsorption. Despite the similar goodness of fit to the same experimental absorbance data, our models predict distinct coverage dynamics for different adsorption sites. Through analysis of spectral peak stability and predicted dynamics, we identify a mechanism in which CO primarily adsorbs on bridge sites followed by rapid conversion to hollow sites as being the most physically consistent with experimental observations. This work provides a framework for extracting mechanistic insights from limited experimental data, demonstrating how machine learning can bridge the gap between transient kinetic measurements and a molecular-level understanding of surface reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessment of Blocking Contacts in Suppressing Polarization Effects in CsPbBr 3 Perovskite Detectors

The CsPbBr 3 perovskite has garnered significant attention as a room-temperature semiconductor for hard radiation detection due to straightforward synthesis, scalable crystal growth, low cost, and excellent energy resolution. However, despite these advantages, at ambient temperature, CsPbBr 3 devices may experience performance deterioration and irreversible failure due to “polarization” induced by electromigration of ions to electrical connections on the device. In this study, we tested several contact materials and their optimization deposition techniques to assess the stability of high-performance CsPbBr 3 γ-ray detectors. Metals, with low work functions (Ti, In, Sn, Sb, Pb, Bi, Al, Au, and TiC) and high-work-function (Au or Pt) contacts, were used to form different Schottky junctions using vacuum thermal evaporation, electron-beam evaporation, and sputtering methods. These detectors were tested in intermittent and continuous modes to assess their stability. Among the tested detectors, the Bi/CLB/Pt electrode configuration demonstrated superior stability, operating effectively for 11 months under periodic testing and 16 days under continuous testing. In contrast, other configurations functioned only for a few months under intermittent conditions. Upon incorporating a ~40-nm-thick TiC passivation layer on the anode side (Bi/TiC/CLB/Pt), the CLB device operated continuously for 36 days without degradation. In many cases, the failure mode of the devices was due to the degradation of the anode. Here, the chemical changes in the fresh and deteriorated anodes were characterized using scanning electron microscopy and energydispersive X-ray spectroscopy

CsPbBr3 perovskite↗
DOE OSTI2025

Benchmarking Density Functional Theory Methods for Efficient Calculations of a Strongly Correlated Li 1– x Ni 1– y O 2−δ System

Transition metal oxides (TMOs), such as LiNiO 2 , are promising candidates for energy storage and electronic devices due to their unique electronic properties, exceptional physical and chemical characteristics, and ability to adopt multiple oxidation states. However, accurately predicting their properties using mean-field density functional theory (DFT) is challenging due to the presence of strongly correlated d-electrons and the complex interplay between their structural, electronic, and magnetic responses. These challenges are further exacerbated by the need to model defects, surfaces, and interfaces, which require computationally efficient, large-scale simulations. To address these issues, we carry out a benchmark study on the Li 1–x NiO 2 system, evaluating the performance of several popular functionals. Our findings demonstrate that combining SCAN functional relaxation with single-step HSE calculations provides a practical and scalable computational strategy. This approach balances accuracy and efficiency, enabling high-throughput simulations of strongly correlated TMOs and improved predictive modeling capability of TMOs for practical applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Deciphering electrocatalysts with multimodal operando approaches

Here, the electrocatalytic processes of a copper catalyst during nitrate electroreduction are unveiled by correlated operando microscopy and spectroscopy. Catalysts are vital to the modern chemical industry, yet their development has largely relied on trial-and-error approaches. Optimizing catalysts requires a fundamental understanding of their structure–chemical property behaviour under operational conditions. However, operando characterization remains challenging, especially for reactions occurring in the complex and dynamic liquid environments of electrochemical systems. Over the past decade, the rapid development of in situ and operando environmental transmission electron microscopy (ETEM) and microelectromechanical system (MEMS)-based closed-cell holders, enabling environmental studies within the vacuum environment of transmission electron microscopy (TEM), has substantively advanced understanding of heterogeneous catalysis, notably for gas-phase reactions. In situ ETEM enables the direct observation of the catalyst evolution in terms of structure, morphology and chemical state at the nano-to-atomic scale, providing insights into their correlation with catalytic performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

Artificial neural networks↗
DOE OSTI2025

Diffuse scattering from correlated electron systems

The role of inhomegeneity in determining the properties of correlated electron systems is poorly understood because of the dearth of structural probes of disorder at the nanoscale. Advances in both neutron and x-ray scattering instrumentation now allow comprehensive measurements of diffuse scattering in single crystals over large volumes of reciprocal space, enabling structural correlations to be characterized over a range of length scales from 5 to 200 angstroms or more. When combined with new analysis tools, such as three-dimensional difference pair-distribution functions, these advanced capabilities have produced fresh insights into the interplay of structural fluctuations and electronic properties in a broad range of correlated electron materials. This review describes recent investigations that have demonstrated the importance of understanding structural inhomogeneity pertaining to phenomena as diverse as superconductivity, charge density wave modulations, metal-insulator transitions, and multipolar interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Utah FORGE: Triaxial Direct Shear Results - February 2025

This dataset contains results from nine triaxial direct shear tests conducted by Los Alamos National Laboratory on samples from FORGE Well 16A(78)-32. The primary objectives of this work were to determine the shear strength in both intact and residual states, evaluate dilation against displacement, assess permeability in relation to displacement, time, and normal stress, understand the relationship between aperture and normal stress, and monitor the effluent chemistry as a function of time. The data includes time-series measurements of stress, displacement, permeability, and effluent chemistry, with and without experimental dilution corrections. Additional materials include profilometry data, photographic documentation of the experimental setups and apparatus, and test notes. The dataset is organized into folders corresponding to each test, containing hydromechanical data, effluent chemistry measurements, and images. The hydromechanical data consists of detailed time-series records capturing parameters such as shear force, confining pressure, permeability, and temperature. Effluent chemistry data tracks fluid composition changes over time. Also included are conference papers, presentation slides, and a summary document outlining the experiments.

15 GEOTHERMAL ENERGY↗