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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Solvation governs cation transference in glyme-based lithium battery electrolytes

The efficacy of electrochemical systems is governed by the cation transference number, which represents the fraction of current carried by the working ion. Energy is wasted when field-induced motion also drives anions and solvent molecules, decreasing the transference number to near-zero. We present a systematic study of cation transference in a series of electrolytes: tetraglyme (TG), octaglyme (OG), and poly(ethylene oxide) (PEO) mixed with lithium bis(trifluoromethanesulfonyl)imide. In all three electrolytes, starting from the dilute salt concentration limit, the experimentally measured cation transference number decreases with increasing concentration, reaching a minimum between -0.1 and -0.2, before rising back to positive values. Explicit measurements of field-induced species' velocities by electrophoretic nuclear magnetic resonance indicate that negative cation transference numbers in TG and OG electrolytes are dictated by solvation interactions with minimal contribution from anion-cation interactions. Simulation-based solvation structures indicate that OG serves as a bridge between TG and PEO. Multi-charge positive clusters, which are negligible in TG, become increasingly important at higher chain lengths (OG and PEO). As migrating cations drag their solvation shells, this solvation-induced motion is amplified in glyme electrolytes because of covalent interactions between solvating glyme molecules and free glyme molecules.

Im, Julia↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2025

Energy transfer between localized emitters in photonic cavities from first principles

Radiative and nonradiative resonant couplings between defects are ubiquitous phenomena in photonic devices used in classical and quantum information technology applications. In this work, we present a first-principles approach to enable quantitative predictions of the energy transfer between defects in photonic cavities, beyond the dipole-dipole approximation and including the many-body nature of the electronic states. As an example, we discuss the energy transfer from a dipolelike emitter to an 𝐹 center in MgO in a spherical cavity. We show that the cavity can be used to controllably enhance or suppress specific spin-flip and spin-conserving transitions. Specifically, we predict that an ∼10–100 enhancement in the resonant energy transfer rate can be gained in the case of the 𝐹 center in MgO at ∼10 nm distances from a dipolar source, using rather moderate cavity with quality factor 𝑄 ∼ 400. We also show that a similar suppression in the transfer rate can be achieved by off-tuning the cavity resonance relative to the emitter transition energy. The framework presented here is general and readily applicable to a wide range of devices where localized emitters are embedded in microspheres, core-shell nanoparticles, and dielectric Mie resonators. Hence, our approach paves the way to predict how to control energy transfer in quantum memories and in ultrahigh-density optical memories, and in a variety of quantum information platforms.

First-principles calculations↗
DOE OSTI2024

Coherent Transfer of Lattice Entropy via Extreme Nonlinear Phononics in Metal Halide Perovskites

Entropy transfer in metal halide perovskites, characterized by significant lattice anharmonicity and low stiffness, underlies the remarkable properties observed in their optoelectronic applications, ranging from solar cells to lasers. The conventional view of this transfer involves stochastic processes occurring within a thermal bath of phonons, where the lattice arrangement and energy flow from higher- to lower-frequency modes. Here, we unveil a comprehensive chronological sequence detailing a conceptually distinct coherent transfer of entropy in a prototypical perovskite CH 3 NH 3 Pbl 3 . The terahertz periodic modulation imposes vibrational coherence into electronic states, leading to the emergence of mixed (vibronic) quantum beat between approximately 3 and 0.3 THz. We highlight a well-structured bidirectional time-frequency transfer of these diverse phonon modes, each developing at different times and transitioning from high to low frequencies from 3 to 0.3 THz, before reversing direction and ascending to around 0.8 THz. First-principles molecular dynamics simulations disentangle a complex web of coherent-phononic coupling pathways and identify the salient roles of the initial modes in shaping entropy evolution at later stages. Capitalizing on coherent entropy transfer and dynamic anharmonicity presents a compelling opportunity to exceed the fundamental thermodynamic (Shockley-Queisser) limit of photoconversion efficiency and to pioneer novel optoelectronic functionalities. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Toward Establishing Uniqueness of Experimentally Determined Transference Numbers

The passage of current through a battery results in the development of concentration gradients in the electrolytic phase. For a fully characterized binary electrolyte, where the conductivity, salt diffusion coefficient, cation transference number, and the thermodynamic factor are known, concentration and potential gradients in the electrolytic phase can be modeled using Newman’s concentrated solution theory. We report two methods for measuring the transference number: the standard method based on electrochemical measurements ( t + , echem 0 ) and electrophoretic NMR ( t + , eNMR 0 ). The electrochemical approach requires combining measurements from multiple experiments; the equations used to determine the cation transference number and the thermodynamic factor are coupled, nonlinear algebraic equations. In the electrophoretic-NMR-based approach, however, the equations used to determine the cation transference number and the thermodynamic factor are decoupled. We find for a liquid electrolyte comprised of a lithium salt dissolved in tetraglyme, the values of the transference numbers obtained by these two methods are distinct. For example, at 30 °C, t + , echem 0 = −1.02 ± 1.11 and t + , eNMR 0 = 0.25 ± 0.04. The corresponding thermodynamic factors are also different. While the magnitude of the predicted concentration gradients based on the two sets of parameters are different, the predicted current-voltage relationships are similar.

Hickson, Darby T. (ORCID:0000000251339755)↗
DOE OSTI2025

Impact of the LiPF6 Concentration on the Interfacial Charge Transfer and Fast-charging Capabilities of Lithium-Ion Batteries

Fast-charging lithium-ion batteries (LIB) demand optimized electrolyte formulations to balance ionic conductivity, viscosity, and interfacial charge transfer kinetics. This study examines how LiPF 6 concentration shapes solvation structure, desolvation energy, charge transfer activation energy, and solid electrolyte interphase (SEI) properties, which are critical for fast-charging performance. Using Raman spectroscopy, electrochemical cycling, X-ray photoelectron spectroscopy, and atomistic modeling, we analyze how varying LiPF 6 concentrations impact interfacial and bulk transport properties. Our findings show that increasing LiPF 6 concentration alters lithium solvation structures, reduces desolvation energy, and accelerates charge transfer at the electrode interface. Higher concentrations lower the activation energy for charge transfer and suppress excessive SEI growth, improving interfacial kinetics. However, concentrations above a certain threshold increase viscosity and reduce ionic conductivity, limiting transport efficiency. These results offer insights into electrolyte solvation and interfacial charge transfer mechanisms, providing guidelines for designing next-generation fast-charging LIB electrolytes with enhanced efficiency, stability, and longevity.

Son, Seoung-Bum [Argonne National Laboratory (ANL)↗
DOE OSTI2024

Arbitrary Low-Dimensional Film Transfer Enabled by GeO 2 Release Layer

Low-dimensional materials show great promise for enhanced computing and sensing performance in mission-relevant environments. However, integrating low-dimensional materials into conventional electronics remains a challenge. Here, we demonstrate a novel transfer method by which low-dimensional materials and their heterostructures can be transferred onto any arbitrary substrate. Our method relies on a water soluble GeO 2 substrate from which lowdimensional materials are transferred without significant perturbation. We apply the method to transfer a working electronic device based on a low-dimensional material. Process developments are achieved to enable the fabrication and transfer of a working electronic device, including the growth of high-k dielectric on GeO 2 by atomic layer deposition and inserting an indium diffusion barrier into the device gate stack. This work supports Sandia’s heterogeneous integration strategy to broaden the implementation of low-dimensional films and their devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterization of throughput on the AXI DMA bus for burst data transfer over Ethernet

cThe Xilinx AXI Direct Memory Access (AXI DMA) module is an efficient solution for medium-speed data transfer in Xilinx SoC FPGAs, supporting data rates greater than 1000 Gbps even in very suboptimal operating modes. It facilitates direct transfer of AXI stream data into processor memory without constant software intervention, which reduces overhead and ensures consistent data logging. By utilizing the FPGA's available memory, large circular buffers (1-5 GiB) are used to buffer data and accommodate network limitations, enabling high-rate data bursts. In this study, we measured the performance of AXI DMA under conditions simulating its lowest practical data transfer speeds. The Arbitrary Length Data Sender was used to transmit AXI stream packets at 32-bit width and 100 MHz frequency, a narrow width and slow speed. Results show that the AXI DMA can transfer up to 3192.76 Mbps with large packet sizes but experiences reduced performance for smaller packets, as low as 2.6 Mbps for 4-byte packets. For Ethernet-limited applications, packet sizes between 8,000 and 16,000 bytes provided optimal transfer speeds of 874 to 1600 Mbps. These findings suggest that the AXI DMA is not the limiting factor in systems where packet sizes exceed 8,000 bytes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Contrastive Machine Learning with Gamma Spectroscopy Data Augmentations for Detecting Shielded Radiological Material Transfers

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Chirped Laser Pulse Control of Vibronic Wavepackets and Energy Transfer in Phycocyanin 645

Photosynthetic organisms use light-harvesting complexes to increase the spectrum of light that they absorb from solar photons. Recent ultrafast spectroscopic studies have revealed that efficient (sub-ps) energy transfer is mediated by vibronic coherence in the phycobiliprotein phycocyanin 645 (PC645). Here, we report studies that employ broadband pump–probe spectroscopy with linearly chirped excitation pulses to further investigate the relationship between vibronic state preparation and energy transfer dynamics in PC645. Negatively chirped pulse excitation is found to enhance wavepackets of a high-frequency mode (1580 cm –1 ) and increase the rate of downhill energy transfer, while on the other hand, positively chirped pulses suppress these oscillatory features and decrease this rate. Model calculations incorporating the influence of the chirped pump pulse are used to understand its effect on initial state preparation. Furthermore, these results provide mechanistic insight into how the overall nonequilibrium rate of energy transfer is influenced by initial state preparation.

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

Base-Free Catalytic Transfer Hydrogenation of Alkyl Formates and Organic Esters at Mild Temperature

The pincer-ligated ruthenium complex ( iPr PN H P)Ru(CO)H 2 ( iPr PN H P = ( i Pr 2 PC 2 H 4 ) 2 NH) is an active catalyst for the transfer hydrogenation of alkyl formates (HCO 2 R) and organic esters (RCO 2 R′) to the corresponding alcohols under base-free reaction conditions at mild temperatures. Specifically, a range of alkyl formate esters were reduced to MeOH and the corresponding alcohols in high yields using ( iPr PN H P)Ru(CO)H 2 as the catalyst and isopropanol ( i PrOH) as the hydrogen donor at 30 °C. The first step in the process is the metal-catalyzed transesterification of the alkyl formate with i PrOH to generate isopropyl formate, which is then reduced. The use of i PrOH as the hydrogen donor is crucial. ( iPr PN H P)Ru(CO)H 2 can also catalyze the transfer hydrogenation of a broad range of organic esters, including cyclic, acyclic, heteroatom-substituted, and long-chain bio-derived esters, to the corresponding alcohols in good yields using ethanol (EtOH) as the hydrogen donor at 55 °C. Computational studies were used to elucidate the proposed pathway for alkyl formate reduction and the underlying reasons why i PrOH is the most effective hydrogen donor for alkyl formate reduction, while EtOH is optimal for organic ester reduction. Overall, this work describes a highly active catalyst for alkyl formate and organic ester transfer hydrogenation and provides mechanistic insight into the factors responsible for the strong catalytic performance. Finally, these findings will be valuable for designing catalysts for both transfer hydrogenation and related reactions.

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

Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery

Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (R 2 = 0.81, RMSE = 0.64 m 2 /m 2 , rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (R 2 = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. In conclusion, by bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.

Leaf area index (LAI)↗
DOE OSTI2025

High-heat transfer lithium-ion batteries: A new era in battery thermal management

Despite advances in lithium-ion battery technology, critical challenges remain that must be addressed to accelerate electric vehicle (EV) adoption and global energy transformation. Significantly improved battery thermal management (BTM) is key to overcoming these challenges. BTM approaches focus on increasing heat transfer coefficients via air, liquid, or refrigerant cooling, but less attention is given to reducing the battery's thermal resistance, a major bottleneck for heat transfer. This work introduces a novel approach to reduce battery thermal resistance by integrating in-plane heat transfer with optimized cell geometry, minimized thermal resistances, and reduced interfacial resistances, representing a departure from previous methods. The standard prismatic can cell incorporating this technology is referred to as the high heat transfer (HHT) battery. An equivalent resistance battery thermal model is developed for speed and accuracy, validated against experimental data in the literature, demonstrating strong correlation and ensuring reliable predictions for real-world performance. Thermal performance metrics of the conventional and HHT batteries are compared using a parametric study with air, liquid, and refrigerant boundary conditions across a range of aspect ratios. The HHT battery shows a heat removal rate up to 20 times higher than a conventional battery. These findings suggest that HHT technology could be transformative for EV battery performance, enabling fast charging, mitigating thermal runaway, extending battery life, reducing cold-weather power loss, increasing reliability, lowering costs, and enabling higher energy density, all critical for EV adoption and energy transformation. Future work will focus on prototyping and real-world testing to refine these findings for commercial-scale applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Quenching of single-particle strength inferred from nucleon-removal transfer reactions on 15 C

The difference in the proton and neutron separation energies (ΔS) of the weakly bound 15 C ground state is -19.86 MeV, an extreme value. Data from intermediate-energy heavy-ion induced (HI-induced) knockout reactions on nuclei spanning -20 ≲ ΔS ≲ +20 MeV, suggest that the degree to which single-particle strength is quenched, R s , has a negative correlation with ΔS, decreasing from unity around -20 MeV to around 0.2 at +20 MeV. For the 15 C ground state (R s = 0.96 (4) in HI-induced knockout), contrasting results have recently been obtained via the neutron-adding transfer reaction, which reveal a value of R s = 0.64 (15), similar to the value observed at modest and more extreme values of ΔS with reaction probes other than HI knockout. In order to explore the any potential differences between adding and removing processes in transfer reactions at extreme ΔS, single-neutron removal transfer reactions on 15 C were performed at 7.1 MeV/u in inverse kinematics. The removal of a valence neutron in 2s 1/2 orbit using both (p, d) and (d, t) reactions shows consistent quenching factors and agrees with those from the neutron-adding reaction. The present results, which can be compared with neutron knockout reaction, suggest that correlations, represented by the quenching factor, show limited dependence on neutron-proton asymmetry under the most extreme asymmetry conditions so far achieved in transfer reactions.

quenching factor↗
DOE OSTI2025

Direct Evidence for Buffer-Enhanced Proton-Coupled Electron Transfer Generation of a High-Valent Metal-Oxo Complex

Here, the oxidation of metal-aquo and -hydroxo complexes to generate the high-valent metal-oxo species used in oxidative catalysis is often kinetically slow due to sluggish proton transfer between ligated −H 2 O/–OH in the proton-coupled electron transfer (PCET) chemistry. In this research, a ruthenium water oxidation catalyst anchored to a conductive tin-doped indium oxide (ITO) thin film, abbreviated ITO|Ru II –OH 2 , was characterized by spectroscopic and electrochemical methods in acetate or phosphate buffers. The deprotonated intermediate, Ru II –OH, was observed spectroscopically in the PCET half-reaction ITO(e – )|Ru III –OH + H + → ITO|Ru II –OH 2 indicating an underlying stepwise ET-PT mechanism. In contrast, at elevated buffer concentrations, this intermediate was absent, and a 2–4 order of magnitude increase in the proton transfer rate constant was observed. Kinetic data for this PCET reaction measured as a function of the driving force provided the reorganization energy λ = 1.05 eV and was assigned to a concerted electron–proton transfer (EPT) mechanism. In addition, the standard heterogeneous rate constants for two PCET equilibria, Ru III –OH + H + + e – ⇌ Ru II –OH 2 and Ru IV = O + H + + e – ⇌ Ru III –OH were enhanced by these same buffers. Collectively, the data show that the added buffers can enhance the kinetics and thermodynamics for PCET reactions relevant to oxidative catalysis.

Catalysts↗
DOE OSTI2024

Exciton–Phonon Coupling Induces a New Pathway for Ultrafast Intralayer-to-Interlayer Exciton Transition and Interlayer Charge Transfer in WS 2 –MoS 2 Heterostructure: A First-Principles Study

Despite the weak, van der Waals interlayer coupling, photoinduced charge transfer vertically across atomically thin interfaces can occur within surprisingly fast, sub-50 fs time scales. An early theoretical understanding of charge transfer is based on a noninteracting picture, neglecting excitonic effects that dominate optical properties of such materials. We employ an ab initio many-body perturbation theory approach, which explicitly accounts for the excitons and phonons in the heterostructure. Our large-scale first-principles calculations directly probe the role of exciton-phonon coupling in the charge dynamics of the WS 2 /MoS 2 heterobilayer. We find that the exciton-phonon interaction induced relaxation time of photoexcited excitons at the K valley of MoS 2 and WS 2 is 67 and 15 fs at 300 K, respectively, which sets a lower bound to the intralayer-to-interlayer exciton transfer time and is consistent with experiment reports. We further show that electron-hole correlations facilitate novel transfer pathways that are otherwise inaccessible to noninteracting electrons and holes.

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

Microscopic Origin of Twist-Dependent Electron Transfer Rate in Bilayer Graphene

Using molecular simulation and continuum dielectric theory, we consider how electrochemical kinetics are modulated by the twist angle in bilayer graphene electrodes. By establishing a connection between the twist angle and the screening length of charge carriers within the electrode, we investigate how tunable metallicity modifies the statistics of the electron transfer energy gap. Constant potential molecular simulations show that the activation free energy for electron transfer increases with screening length, leading to a non-monotonic dependence on the twist angle. Here, the twist angle alters the density of states, tuning the number of thermally accessible channels for electron transfer and the reorganization energy by affecting the stability of the vertically excited state through attenuated image charge interactions. Understanding these effects allows us to express the Marcus rate of interfacial electron transfer as a function of the twist angle in a manner consistent with experimental observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗