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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Synergistic Enhancement of Electrocatalytic CO 2 Reduction with Gold Nanoparticles Embedded in Functional Graphene Nanoribbon Composite Electrodes

Regulating the complex environment accounting for the stability, selectivity, and activity of catalytic metal nanoparticle interfaces represents a challenge to heterogeneous catalyst design. Here in this paper, we demonstrate the intrinsic performance enhancement of a composite material composed of gold nanoparticles (AuNPs) embedded in a bottom-up synthesized graphene nanoribbon (GNR) matrix for the electrocatalytic reduction of CO 2 . Electrochemical studies reveal that the structural and electronic properties of the GNR composite matrix increase the AuNP electrochemically active surface area (ECSA), lower the requisite CO 2 reduction overpotential by hundreds of millivolts (catalytic onset > -0.2 V versus reversible hydrogen electrode (RHE)), increase the Faraday efficiency (>90%), markedly improve stability (catalytic performance sustained over >24 h), and increase the total catalytic output (>100-fold improvement over traditional amorphous carbon AuNP supports). The inherent structural and electronic tunability of bottom-up synthesized GNR-AuNP composites affords an unrivaled degree of control over the catalytic environment, providing a means for such profound effects as shifting the rate-determining step in the electrocatalytic reduction of CO 2 to CO, and thereby altering the electrocatalytic mechanism at the nanoparticle surface.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2022

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Abstract Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

All-silicon quantum light source by embedding an atomic emissive center in a nanophotonic cavity

Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stabilization of high-performance rock-salt LiMnSbTe3 thermoelectrics with embedded van der Waals-like gaps

Rock-salt-structured compounds like lead chalcogenides are promising thermoelectrics, as their high symmetry, strong anharmonicity, and favorable phase behavior collectively lead to high performance by enabling large power factors and ultralow thermal conductivity. Here, we report LiMnSbTe 3 , a new rock-salt semiconductor stabilized through targeted chemical design by combining hexagonal MnTe with cubic LiSbTe 2 . Embedded in the highsymmetry matrix, van der Waals-like gaps form due to Sb 2 Te 3 nanoscale segregation, which acts as effective phonon-scattering centers, leading to a low lattice thermal conductivity of 0.37Wm- 1 K -1 at 873 K with alloy scattering from disordered cations. The ordered local structure of Sb2Te 3 -type vdW-like gaps and the cross-gap interaction facilitate the carrier transport. Aided by energyconverged valence bands and a paramagnon drag effect, high Seebeck coefficients and enhanced power factor can be achieved, leading to a high ZT of 1.2 at 873 K. Furthermore, introducing Mn deficiency increases ZT to 1.5, highlighting the potential for higher performance through optimized doping or alloying. A segmented single-leg thermoelectric module achieves an output power density of 0.52 Wcm -2 and an efficiency of 8.7% under ΔT of 478 K, further demonstrating its promising thermoelectric applications.

42 ENGINEERING↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Electronic excitations and spin interactions in chromium trihalides from embedded many-body wavefunctions

Although chromium trihalides are widely regarded as a promising class of two-dimensional magnets for next-generation devices, an accurate description of their electronic structure and magnetic interactions has proven challenging to achieve. Here, we quantify electronic excitations and spin interactions in CrX 3 (X = Cl, Br, I) using embedded many-body wavefunction calculations and fully generalized spin Hamiltonians. We find that the three trihalides feature comparable d-shell excitations, consisting of a high-spin 4 A 2 ($t^3_{2g}$$e^0_g$) ground state lying 1.5–1.7 eV below the first excited state 4 T 2 ($t^2_{2g}$$e^1_g$). CrCl 3 exhibits a single-ion anisotropy A sia = - 0.02 meV, while the Cr spin-3/2 moments are ferromagnetically coupled through bilinear and biquadratic exchange interactions of J 1 = - 0.97 meV and J 2 = - 0.05 meV, respectively. The corresponding values for CrBr 3 and CrI 3 increase to A sia = -0.08 meV and A sia = - 0.12 meV for the single-ion anisotropy, J 1 = -1.21 meV, J 2 = -0.05 meV and J 1 = -1.38 meV, J 2 = -0.06 meV for the exchange couplings, respectively. We find that the overall magnetic anisotropy is defined by the interplay between A sia and A dip due to magnetic dipole–dipole interaction that favors in-plane orientation of magnetic moments in ferromagnetic monolayers and bulk layered magnets. The competition between the two contributions sets CrCl 3 and CrI 3 as the easy-plane (A sia + A dip >0) and easy-axis (A sia + A dip <0) ferromagnets, respectively. The differences between the magnets trace back to the atomic radii of the halogen ligands and the magnitude of spin–orbit coupling. Our findings are in excellent agreement with recent experiments, thus providing reference values for the fundamental interactions in chromium trihalides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum embedding theories to simulate condensed systems on quantum computers.

Quantum computers hold promise to improve the efficiency of quantum simulations of materials and to enable the investigation of systems and properties that are more complex than tractable at present on classical architectures. Here, we discuss computational frameworks to carry out electronic structure calculations of solids on noisy intermediate-scale quantum computers using embedding theories, and we give examples for a specific class of materials, that is, solid materials hosting spin defects. These are promising systems to build future quantum technologies, such as quantum computers, quantum sensors and quantum communication devices. Although quantum simulations on quantum architectures are in their infancy, promising results for realistic systems appear to be within reach.

Vorwerk, Christian↗
DOE OSTI2024

Energy efficient photonic memory based on electrically programmable embedded III-V/Si memristors: switches and filters

Abstract Over the past few years, extensive work on optical neural networks has been investigated in hopes of achieving orders of magnitude improvement in energy efficiency and compute density via all-optical matrix-vector multiplication. However, these solutions are limited by a lack of high-speed power power-efficient phase tuners, on-chip non-volatile memory, and a proper material platform that can heterogeneously integrate all the necessary components needed onto a single chip. We address these issues by demonstrating embedded multi-layer HfO 2 /Al 2 O 3 memristors with III-V/Si photonics which facilitate non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers, and (de-)interleaver filters. The Mach-Zehnder optical memristor exhibits non-volatile optical phase shifts > π with ~33 dB signal extinction while consuming 0 electrical power consumption. We demonstrate 6 non-volatile states each capable of 4 Gbps modulation. (De-) interleaver filters were demonstrated to exhibit memristive non-volatile passband transformation with full set/reset states. Time duration tests were performed on all devices and indicated non-volatility up to 24 hours and beyond. We demonstrate non-volatile III-V/Si optical memristors with large electric-field driven phase shifts and reconfigurable filters with true 0 static power consumption. As a result, co-integrated photonic memristors offer a pathway for in-memory optical computing and large-scale non-volatile photonic circuits.

Cheung, Stanley (ORCID:0000000248860013)↗
DOE OSTI2013

Regenerable Photovoltaic Devices with a Hydrogel-Embedded Microvascular Network

Light-driven degradation of photoactive molecules could be one of the major obstacles to stable long term operation of organic dye-based solar light harvesting devices. One solution to this problem may be mimicking the regeneration functionality of a plant leaf. We report an organic dye photovoltaic system that has been endowed with such microfluidic regeneration functionality. A hydrogel medium with embedded channels allows rapid and uniform supply of photoactive reagents by a convection-diffusion mechanism. A washing-activation cycle enables reliable replacement of the organic component in a dye-sensitized photovoltaic system. Repetitive restoration of photovoltaic performance after intensive device degradation is demonstrated.

14 SOLAR ENERGY↗
DOE OSTI2016

Atom probe tomographic mapping directly reveals the atomic distribution of phosphorus in resin embedded ferritin

In this study, we report the atomic-scale analysis of biological interfaces using atom probe tomography. Embedding the protein ferritin in an organic polymer resin lacking nitrogen provided chemical contrast to visualize atomic distributions and distinguish organic-organic and organic-inorganic interfaces. The sample preparation method can be directly extended to further enhance the study of biological, organic and inorganic nanomaterials relevant to health, energy or the environment.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Enhanced magneto-optical effects in composite coaxial nanowires embedded with Ag nanoparticles

Nanostructures decorated with noble metal nanoparticles (NPs) exhibit potential for use in highly sensitive optoelectronic devices through the localized surface plasmon resonance (LSPR) effect. In this study, Faraday rotation was significantly enhanced through the structural optimization of ferromagnetic (FM)/semiconductor composite nanostructures. Experimental and theoretical results revealed that the position of noble metal NPs significantly influenced the coupling of the LSPR-enhanced electromagnetic field with FM materials. Furthermore, nanostructures embedded with noble metals demonstrated an improved capability to efficiently use the electromagnetic field compared to other structures. The Faraday rotation of ZnO/Ag(NPs)/Fe was enhanced 58 fold compared to that of the ZnO(film)/Fe. This work provides a basis for the design of nanoarchitectures for miniaturized high-performance magneto-optical devices

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Hierarchical nanostructured core-shell Sn@C nanoparticles embedded in graphene nanosheets: spectroscopic view and their application in lithium ion batteries

Hierarchical carbon encapsulated tin (Sn@C) embedded graphene nanosheet (GN) composites (Sn@C–GNs) have been successfully fabricated via a simple and scalable one-step chemical vapor deposition (CVD) procedure. The GN supported Sn@C core–shell structures consist of a crystalline tin core, which is thoroughly covered by a carbon shell and more interestingly, extra voids are present between the carbon shell and the tin core. Synchrotron spectroscopy confirms that the metallic tin core is free of oxidation and the existence of charge redistribution transfer from tin to the carbonaceous materials of the shell, facilitating their intimate contact by chemical bonding and resultant lattice variation. The hybrid electrodes of this material exhibit a highly stable and reversible capacity together with an excellent rate capability, which benefits from the improved electrochemical properties of tin provided by the protective carbon matrix, voids and the flexible GN matrices.

Wang, Dongniu↗
DOE OSTI2015

Unassisted solar-driven photoelectrosynthetic HI splitting using membrane-embedded Si microwire arrays

Free-standing, membrane-embedded, Si microwire arrays have been used to affect the solar-driven, unassisted splitting of HI into H 2 and I 3 -. The Si microwire arrays were grown by a chemical-vapor-deposition vapor–liquid–solid growth process using Cu growth catalysts, with a radial n + p junction then formed on each microwire. A Nafion proton-exchange membrane was introduced between the microwires and Pt electrocatalysts were then photoelectrochemically deposited on the microwires. The composite Si/Pt–Nafion membrane was mechanically removed from the growth substrate, and Pt electrocatalysts were then also deposited on the back side of the structure. The resulting membrane-bound Si microwire arrays spontaneously split concentrated HI into H 2 (g) and I 3 - under 1 Sun of simulated solar illumination. The reaction products (i.e. H 2 and I 3 -) were confirmed by mass spectrometry and ultraviolet–visible electronic absorption spectroscopy.

14 SOLAR ENERGY↗
DOE OSTI2016

Hydrodynamic coupling of particle inclusions embedded in curved lipid bilayer membranes

Here, we develop theory and computational methods to investigate particle inclusions embedded within curved lipid bilayer membranes. We consider the case of spherical lipid vesicles where inclusion particles are coupled through (i) intramembrane hydrodynamics, (ii) traction stresses with the external and trapped solvent fluid, and (iii) intermonolayer slip between the two leaflets of the bilayer. We investigate relative to flat membranes how the membrane curvature and topology augment hydrodynamic responses. We show how both the translational and rotational mobility of protein inclusions are effected by the membrane curvature, ratio of intramembrane viscosity to solvent viscosity, and intermonolayer slip. For general investigations of many-particle dynamics, we also discuss how our approaches can be used to treat the collective diffusion and hydrodynamic coupling within spherical bilayers.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Transition metal-embedded two-dimensional C 3 N as a highly active electrocatalyst for oxygen evolution and reduction reactions

Searching for highly efficient, stable and cost-effective catalysts for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is vital to resolve energy security and environmental problems. Herein, by means of computational screening based on the density functional theory (DFT), we studied a wide range of transition metal (TM) atoms embedded into the double carbon vacancy of C 3 N monolayers (V CC ), denoted as TM-V CC (TM = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ir and Pt), as efficient single-atom catalysts (SACs) for OER and ORR. The calculated results showed that all the considered TM-V CC composites exhibited a metallic feature that ensured efficient charge transfer during reactions. The interaction strength between intermediates and TM-V CC has direct correlation with the d-band center of TM, which can be tuned by changing the TM atoms with different numbers of d electrons. The best catalyst for OER was Rh-V CC with an overpotential (η OER ) of 0.35 V, followed by Co-V CC (0.43 V). For the ORR process, Rh-V CC exhibited the lowest ORR overpotential (η ORR ) of 0.27 V, followed by Co-V CC (0.42 V). The results suggested that the performances of the newly predicted Rh-V CC and Co-V CC SACs are comparable to those of the noble-metal benchmark catalysts for OER and ORR. Ab initio molecular dynamic simulations indicated that Rh-V CC and Co-V CC SACs could remain stable under 300 K and possessed high energy barriers to prevent the isolated Rh and Co atoms from clustering. Here our results highlight a new family of efficient and stable catalysts with a single atom anchored on carbon nitride-based materials, which provides a useful guideline for catalyst design and practical applications.

36 MATERIALS SCIENCE↗