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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Extreme Temperature Cryptography Based On Nitrogen-Incorporated Ultrananocrystalline Diamond

Physical entropy sources that remain stable under extreme temperatures are essential for cryptography in emerging technological frontiers in deep space exploration, geothermal energy harvesting, and nuclear energy. However, conventional semiconductor platforms fail to generate stable and reliable cryptographic keys above 200 degrees C due to performance degradation. Here, we report a diamond-based cryptographic primitive that exploits the defect-rich sp 2 -bonded grain boundary network in nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) film as a robust entropy source to generate cryptographic keys that remain operationally stable even after enduring extreme temperatures of 700 degrees C for 54 h while also surviving thermal cycling between room temperature and 700 degrees C for 48 h. The strength of the generated keys is assessed through several cryptographic metrics such as bit uniformity, entropy, hamming distances, and correlation coefficients, all of which are found to be near their respective ideal values. Moreover, the generated keys pass the NIST SP 800 and SP 800-90B tests and are also resilient to supply bias variations and a regression-based machine learning attack model based on the Fourier series. The robustness of the keys is attributed to the better thermal stability and chemical inertness of the n-UNCD film. This is supported by high-resolution energy-dispersive X-ray spectroscopy (EDS), which shows no significant lateral diffusion of metal atoms into the n-UNCD layer, and by Raman spectroscopy, which reveals no significant changes in the bonding configuration of the n-UNCD structure. Our findings highlight the remarkable potential of n-UNCD film for extreme environment cryptography by expanding the operational limits of conventional hardware security platforms.

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

SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline). Here, we present a model in which learnable grids of B-Spline activation functions are replaced by grids of re-weighted sine functions (SineKAN). We evaluate numerical performance of our model on a benchmark vision task. We show that our model can perform better than or comparable to B-Spline KAN models and an alternative KAN implementation based on periodic cosine and sine functions representing a Fourier Series. Further, we show that SineKAN has numerical accuracy that could scale comparably to dense neural networks (DNNs). Compared to the two baseline KAN models, SineKAN achieves a substantial speed increase at all hidden layer sizes, batch sizes, and depths. Current advantage of DNNs due to hardware and software optimizations are discussed along with theoretical scaling. Additionally, properties of SineKAN compared to other KAN implementations and current limitations are also discussed.

Reinhardt, Eric↗
DOE OSTI2024

Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering defenses. We demonstrate the effectiveness of the attack on two different classification tasks through both digital and real world experiments, and show that the attack is robust against common transform-and-compare defense pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Gluon to $\textrm{q}\overline{\textrm{q}}$ antenna in anisotropic QCD matter: spin-polarized and azimuthal jet observables

We study the production of a quark-antiquark antenna in the presence of a dense and anisotropic QCD medium. We assume the antenna to originate from an unpolarized gluon state, and consider both massless and massive final states. The medium anisotropy is captured by allowing the jet quenching coefficient to take different magnitudes in orthogonal directions with respect to the jet axis. We find that the final particle distribution is sensitive to the medium anisotropy, and more importantly, that this effect couples directly to the helicity/spin of the final states. We propose to look into these effects by performing a Fourier decomposition of the particle distribution inside the jet. In our medium model, we find that the spin independent terms contribute to the even harmonics of the cosine series. The helicity/spin dependence enters only through the sine Fourier series. We further explore the spin dependence by examining the degree of polarization of the final states in different directions. Our results indicate that the anisotropies present in the QCD matter produced in heavy ion collisions can be probed by studying azimuthal and spin observables inside jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Enhancing Short-Range Weather Forecasts through Temporal Variation Encoding: A Multiperiod Embedding Approach

Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.

Artificial intelligence↗
DOE OSTI2025

Amplified Mesoscale and Submesoscale Variability and Increased Concentration of Precipitation under Global Warming over Western North America

Abstract Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. Significance Statement The precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041–80 period. As compared to present-day climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future.

Climate change↗
DOE OSTI2026

SPRUCE FT-ICR MS, Bulk Chemistry, and Mass Loss from Litter Decomposition Study in Experimental Plots, Marcell Experimental Forest, Minnesota, 2015-2017

This dataset contains molecular, bulk chemical, and mass loss measurements from a litter decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. This site is in a Sphagnum spp. ombrotrophic bog forest. Litterbags were deployed into the peat in September 2015 across three warming levels (+0, +4.5, and +9°C) under ambient and elevated carbon dioxide (CO₂ - +500 ppm) and retrieved after roughly 0.5, 1, and 2 years of field incubation (2015-09-23 to 2017-08-02). Litterbags containing six peatland litter types: black spruce needles (Picea mariana - SPL), spruce fine roots (SPR), Sphagnum angustifolium (ANG), Sphagnum magellanicum (MAG), Labrador tea leaves (Rhododendron groenlandicum - LTL), and Labrador tea roots (LTR). Molecular composition of water-soluble organic matter extracts was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) at 9.4 Tesla, operated in negative ion mode with electrospray ionization, providing molecular formula assignments and compound-class distributions across the decomposition time series. Bulk chemical characterization included elemental analysis (percent carbon, nitrogen, and phosphorus) and Fourier Transform Infrared Spectroscopy (FTIR) to quantify functional group composition. Litter mass loss was tracked gravimetrically at each retrieval interval, expressed as percent mass remaining relative to initial dry mass for each litter type and treatment combination. These data are valuable for understanding how vegetation shifts driven by increased atmospheric CO2 and temperature in peatlands alter litter inputs and organic matter stabilization trajectories, with implications for projecting and modeling peatland carbon cycling. This dataset contains two data files in comma-separated value (.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

decomposition↗
DOE OSTI2025

Surrogate Model for Distribution Networks Influenced by Weather

Here, we propose a method for generating reduced representations of time series and for constructing low dimensional surrogate models for time dependent calculations of power and voltage in distribution networks. We employ Fourier polynomials. The surrogate model strategy is aimed at reducing the computational cost of time dependent simulations, albeit, at the expense of fidelity. The reduced representation is achieved by identifying a small and most consequential subset of degrees of freedom. In power and voltage distribution networks dynamics that are heavily influenced by strong cyclic weather events, e.g., the hourly, diurnal and seasonal cycles, the weather/climate time series spectrum exposes these most energetic components. Once the degrees of freedom are identified their amplitudes are optimized using training data. The key challenge in using spectral methods in power network surrogates is addressing the computation of quotients. For this we propose a numerically-stable deconvolution strategy.

Weather events↗
DOE OSTI2024

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µ s and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multitaper Magnitude‐Squared Coherence for Time Series With Missing Data: Understanding Oscillatory Processes Traced by Multiple Observables

To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic data sets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman α flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.

Astro-statistics techniques (1886)↗
DOE OSTI2024

Discovery of 15 A-F Main Sequence Pulsating Star Candidates

Delta Scuti stars, spanning spectral types A to F with masses between 1.5 and 2.5 solar masses, exhibit a complex mix of radial and non-radial pressure modes. This study focuses on 15 Delta Scuti, Gamma Doradus, and Hybrid candidate stars using TESS satellite photometry data with a time series of exposures ranging from 20 to 600 seconds across different sky regions. Employing the prewhitening technique via fast Fourier transforms, significant frequencies are extracted from light curves. Three approximation methods are used to estimate critical stellar parameters such as density, radius, and mass. This research also verifies the variability of these stars, which are currently not classified as variable in databases like SIMBAD.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Robust Automatic EXAFS First-Shell Fits

Extended X-ray absorption fine structure (EXAFS) is a widely used technique for atomic structure determination. Fourier transformation connects EXAFS in k space and R space. However, determining the appropriate k-range for the transformation can be challenging, but critical for the first-shell fit. In this study, we present an automatic method to determine the k-range using the Larch package and a Python program. The first step is to estimate spectral noise across a series of k-ranges with a fixed minimum value and identify the optimal maximum value in the k-range (k max ). The k max is determined by an empirical noise threshold that marks the point where the noise level in the Fourier transformed spectrum changes dramatically. Using the obtained k max value, the first shell is modeled to determine the minimum k value (k min ) by optimizing the background function through alignment of the spectrum with theory. The optimal k min corresponds to the point of the minimum R-factor, which quantifies the difference between the experimental and fitted spectrum. Our method was tested on various typical datasets and yielded suitable k-ranges for Fourier transformation and accurate first-shell fits. This approach helps avoid unreliable, irreproducible data analysis, especially for noisy data from diluted samples, and enables robust automatic first-shell EXAFS fitting.

EXAFS analysis↗
DOE OSTI2026

Spectrograph stabilization using a single-delay interferometer on the Hale Telescope

We describe a technique for spectrograph stabilization useful when conventional mitigation techniques of vacuum tanks, thermal insulation, and laser frequency comb may be impractical, expensive, heavy, or bulky. This includes spectrographs on airborne platforms or mounted on telescopes where they suffer a changing gravity vector or other drifts. Placing a fixed-delay interferometer in series with a spectrograph forms an externally dispersed interferometer (EDI). This produces a uniform sinusoidal comb multiplying input spectrum, creating (through heterodyning) beats (moiré patterns). In Fourier space for low frequencies up to the comb frequency, the moiré generated signal counter-rotates to ordinary spectra under an unknown disperser wavenumber drift Δx. This generates a large negative feedback signal useful in a conceptual control loop, to converge rapidly to a stable spectrum and yield Δx. A modified EDI data analysis algorithm (“crossfading”) combines frequency-weighted moiré with conventional spectrum to cancel net output spectrum reaction to Δx. Needing only a single-delay, this is a practical improvement over prior crossfading analyses requiring multiple delays. We test crossfading on ThAr data near 4850 cm−1 taken on Hale telescope in an earlier project. In a single pass, we reduce drift 20 times. Using seven iterations, we reduce 0.5 cm−1 (31 km/s Doppler equivalent) drift to 4×10−7 cm−1 (2.5 cm/s). The interferometer delay can wander, because linearity of phase versus wavenumber interpolates science features between bracketing calibrating spectral references. Second, mathematically reversing the heterodyning effect doubles effective spectral resolution without changing disperser slit.

Erskine, David J [Lawrence Livermore National Labo↗
DOE OSTI2026

Post-fire time series of sensor and geochemistry sample data from surface water, groundwater, precipitation, soil, and vegetation across Oak Creek watershed, Washington

This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Stream sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2 in 2024, affecting the five study sites at varying burn severities. Each site is equipped with YSI EXO2 sondes logging sub-hourly throughout the year, and grab samples are collected approximately every six weeks. YSI sondes are used to measure temporally resolved proxies for groundwater inputs (specific conductivity) and organic matter (fluorescent dissolved organic matter; fDOM) along with basic water quality and depth. Grab samples of surface water, groundwater, and precipitation are analyzed for water stable isotopes and conductivity to understand endmembers for hydrologic mixing Grab samples of surface water, groundwater, soil water, and litter/vegetation/soil leachates are analyzed for organic matter composition measured by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) to understand organic matter dynamics. Game camera photos are provided in a separate data package available at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018598. Future versions of this dataset will include time series data from YSI EXO2 sondes (fDOM, dissolved oxygen, temperature, depth, specific conductance, turbidity, pH), BaroTROLL sensors (air temperature and barometric pressure), rain gauges (precipitation), and data from the soil and vegetation samples. Because this study is ongoing, this data package will be updated regularly to include newly collected data and the additional data types. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data; (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) a data checks report; (5) file-level metadata; (6) data dictionary; (7) field metadata; (8) readme; (9) international generic sample number (IGSN) mapping file; and (10) field protocols. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) stable water isotopes and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Biogeochemistry↗
DOE OSTI2024

The transformation of lepidocrocite (γ-FeOOH) with Fe($\tiny{II}$) (aq) in slightly acidic media: intermediate pathways and biomimetic behavior

Lepidocrocite (LP) is commonly found in natural or anthropogenic environments and oxidized alloy steel waste storage containers. Despite its importance, the end products formed and its mineral transformation pathways, including intermediate steps and underlying mechanisms under Fe(II) (aq) catalysis still need to be clarified due to decades of dispersed research. Here, in this work, we investigated LP's catalytic transformation with 10 mM and 0.2 mM Fe(II) (aq) at their natural solution pH's via bulk (X-ray Diffraction/XRD, Raman and Attenuated Total Reflectance Fourier Transform Infrared/ATR-FTIR) and micro/nano-scale (semi in situ Transmission Electron Microscopy/TEM) analysis. In general, we observed that goethite (GT) and LP were the main end products. However, a series of two major distinct intermediate events that were initiated by a dissolution type of reaction along with an “induction period” (lack of dissolution) on LP occurred. Fascinatingly, two of the intermediate steps along its mineral transformation presented novel types of non-classical mechanisms of crystallization via some type of guided oriented particle attachment. Furthermore, one of these intermediate steps is biomimetic in appearance, similar to what is observed during bacterial particle attachment. However, it uses inorganic nano-wire antennas that have a sensory-like function as observed with bacterial fimbriae and/or flagellum through an electron transparent film (similar to a bio-film matrix). Finally, this work leads us to comprehend the evolution of some well documented crystal morphologies for GT commonly observed in natural and anthropogenic settings.

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

Synthesis and characterization of biobased copolyesters based on pentanediol: (1) Poly(pentylene dodecanoate–co–furandicarboxylate)

A series of biobased aliphatic-aromatic copolyesters, poly(pentylene dodecanoate-co-furandicarboxylates) (PPeDFs) were synthesized via an esterification and polycondensation melt process. The copolyesters were characterized using gel permeation chromatography, Fourier transform infrared spectroscopy, 1 H NMR spectroscopy, differential scanning calorimetry, thermogravimetric analysis, wide angle x-ray scattering, and tensile testing. The thermal transition behavior was strongly dependent on composition, with the melting and glass transition temperatures reaching a minimum at approximately equimolar ratio of D to F. All copolyesters were stable below 300°C with their R 600 (the weight of material remaining at 600°C) values increasing with F fraction. PPeD to PPeDF30 (e.g., mole ratio D/F = 7:3) show sharp PPeD crystalline reflections only while broad PPeF reflections are shown in PPeF and PPeDF90. PPeDF40 to PPeDF80 showed both crystal structures. The fractional crystallinity for the PPeD was much higher than PPeF and the fractional crystallinity of the copolymers showed a minimum at D/F ratios closer to the latter. Here, the stress at break and modulus both exhibited maxima at D/F ratios that were either high or low, but somewhat surprisingly a strong maximum in percent elongation at break of over 600% occurs at PPeDF40. For this composition, a typical plastic behavior curve was found including a yield point, high elongation at break, and strain hardening.

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

Multi-phase characterization of pitch-carbon coated nano-silicon anodes for lithium-ion batteries

Silicon (Si) is a leading next-generation Li-ion battery anode candidate that meets rigorous performance demands for portable power including enhanced power and energy density with robust cycling performance. However, a series of complex and interrelated reactions lead to reduced calendar life in Si-containing systems and therefore challenge practical adoption. In the present work, we probe the mechanisms underlying observed performance improvements by adding a pitch-carbon coating onto nano-Si material. We pair solid-phase (X-ray photoemission spectroscopy, Fourier-transform infrared), semi-volatile phase (solid-phase microextraction-gas chromatography-mass spectrometry), and gas-phase (gas chromatography-flame-ionization detector) characterization signals to comprehensively evaluate the impact of pitch-carbon coating on the evolution of the Si solid-electrolyte interphase (SEI) and the associated impacts on electrode/electrolyte reactivity. The pitch-carbon is found to serve as a physicochemical barrier, reducing the electro-active surface area for Si/electrolyte reactivity and preventing Si oxidation. Further, the pitch-carbon coating promotes the evolution of a more-favorable SEI by subsuming substantial functionality typically associated with the fluoroethylene carbonate (FEC) electrolyte additive - such as alkoxide scavenging and suppression of transesterification pathways - and by shifting the competitive electrolyte degradation pathways' favorability. The multi-phase characterization approach enables holistic end-products evaluation from complex (electro)chemical interfacial reactions, which informs a robust interpretation of the carbon coating's role in electrochemical performance improvements. The present mechanistic evaluation aids the rational design for improved nano-Si materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗