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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 OSTI2025

Brillouin Sensing with PCA, and PCA-Based Neural Networks for Efficient Temperature Monitoring

This work explores peak estimation techniques in Brillouin Optical Time Domain Analysis (BOTDA), emphasizing both accuracy and efficiency. Euclidean distance measurement method is applied to principal components derived from Brillouin Gain Spectrum data. It offers a major speed advantage being 180 170 times faster than traditional curve fitting methods such as Lorentzian curve fitting, while maintaining similar accuracy. Additionally, a PCA- based neural network model shows significant reduction of peak estimation time compared to Lorentzian fitting. Results show Brillouin frequency shift errors lie under 0.75 MHz in both Euclidean distance-based and neural network-based methods, both of which utilize PCA components. For large data sets and long length fibers, PCA- assisted neural network for peak estimation would be an efficient solution.

Distributed optical fiber sensing↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2026

SO(3)-invariant PCA with application to molecular data

Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientations -- common in structural biology -- presents significant challenges. A naive approach requires augmenting the dataset with many rotated copies of each sample, incurring prohibitive computational costs. In this paper, we extend PCA to 3D volumetric datasets with unknown orientations by developing an efficient and principled framework for SO(3)-invariant PCA that implicitly accounts for all rotations without explicit data augmentation. By exploiting underlying algebraic structure, we demonstrate that the computation involves only the square root of the total number of covariance entries, resulting in a substantial reduction in complexity. We validate the method on real-world molecular datasets, demonstrating its effectiveness and opening up new possibilities for large-scale, high-dimensional reconstruction problems.

Fraiman, Michael [Tel Aviv Univ., Tel Aviv (Israel↗
DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗
DOE OSTI2026

PCA -BASED COMPRESSOR HARDWARE DESIGN GENERATOR IN CHISEL

SF-25-077 This repository includes a hardware generator written in Chisel that generates lossy compression hardware designs based on principal component analysis (PCA), along with a flexible testbench. It also includes a Python tool for evaluating the accuracy loss of integer quantization.

Kazutomo, Yoshi [Argonne National Laboratory (ANL)↗
DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Lignin structural changes and high p -coumaroylation in incipient lignification in moso bamboo

Lignification is a crucial process for strengthening plant tissues, facilitating water transport, and providing defense against pathogens. In the Poaceae family, p-hydroxycinnamic acids are commonly incorporated into lignin, with acylation by p-coumarate (pCA) occurring during lignification. In this study, we performed DFRC and 2D HSQC-NMR analyses to investigate changes in lignin substructures and the degree of lignin pCA-acylation throughout bamboo stem development. Furthermore, immunohistochemical analysis was conducted to elucidate the spatial distribution of lignin substructures within different cell types. Our results revealed that, in young tissues, β–O–4-linked lignin units are predominantly derived from monolignol-pCA conjugates, specifically coniferyl- and sinapyl-pCA. Both lignin structure and the pattern of pCA acylation varied depending on the stage of cell wall formation and the cell type, particularly between vascular fiber cells and parenchyma cells. Based on our results, moso bamboo culms exhibit a distinctive feature during incipient lignification, in which monolignols are predominantly acylated with pCA. As a result, this feature has not been reported in other grasses, suggesting that extensive p-coumaroylation of monolignols plays an important role in the rapid elongation of bamboo culms.

Munekata, Noriaki [Kyoto University (Japan); Unive↗
DOE OSTI2025

Using principal component analysis to distinguish different dynamic phases in superconducting vortex matter

Vortices in type-II superconductors driven over random disorder are known to exhibit a remarkable variety of distinct nonequilibrium dynamical phases that arise owing to the competition between vortex-vortex interactions, the quenched disorder, and the drive. These include pinned states, elastic flows, plastic or disordered flows, and dynamically reordered moving crystal or moving smectic states. The plastic flow phases can be particularly difficult to characterize since the flows are strongly disordered. Here, we perform principal component analysis (PCA) on the positions and velocities of vortex matter moving over random disorder for different disorder strengths and drives. We find that PCA can distinguish the known dynamic phases as well as or better than previous measures based on transport signatures or topological defect densities. In addition, PCA recognizes distinct plastic flow regimes, a slowly changing channel flow and a moving amorphous fluid flow, that do not produce distinct signatures in the standard measurements. In conclusion, our results suggest that this position and velocity-based PCA approach could be used to characterize dynamic phases in a broader class of systems that exhibit depinning and nonequilibrium phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

In planta production of the nylon precursor beta-ketoadipate

Beta-ketoadipate (βKA) is an intermediate of the βKA pathway involved in the degradation of aromatic compounds in several bacteria and fungi. Beta-ketoadipate also represents a promising chemical for the manufacturing of performance-advantaged nylons. We established a strategy for the in planta synthesis of βKA via manipulation of the shikimate pathway and the expression of bacterial enzymes from the βKA pathway. Using Nicotiana benthamiana as a transient expression system, we demonstrated the efficient conversion of protocatechuate (PCA) to βKA when plastid-targeted bacterial-derived PCA 3,4-dioxygenase (PcaHG) and 3-carboxy-cis,cis-muconate cycloisomerase (PcaB) were co-expressed with 3-deoxy-D-arabinoheptulosonate 7-phosphate synthase (AroG) and 3-dehydroshikimate dehydratase (QsuB). This metabolic pathway was reconstituted in Arabidopsis by introducing a construct (pAtβKA) with stacked pcaG, pcaH, and pcaB genes into a PCA-overproducing genetic background that expresses AroG and QsuB (referred as QsuB-2). The resulting QsuB-2 x pAtβKA stable lines displayed βKA titers as high as 0.25 % on a dry weight basis in stems, along with a drastic reduction in lignin content and improvement of biomass saccharification efficiency compared to wild-type controls, and without any significant reduction in biomass yields. Using biomass sorghum as a potential crop for large-scale βKA production, techno-economic analysis indicated that βKA accumulated at titers of 0.25 % and 4 % on a dry weight basis could be competitively priced in the range of $2.04-34.49/kg and $0.47-2.12/kg, respectively, depending on the selling price of the residual biomass recovered after βKA extraction. This study lays the foundation for a more environmentally-friendly synthesis of βKA using plants as production hosts.

Kazaz, Sami↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2025

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗
DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗
DOE OSTI2025

Engineering 2‐Pyrone‐4,6‐Dicarboxylic Acid Production Reveals Metabolic Plasticity of Poplar

Woody biomass is a promising source of fermentable sugars for biofuels and bio-based chemicals, but its industrial use is limited by the costly biorefinery process. A viable strategy to reduce costs involves enhancing both biomass processability and the generation of high-value co-products. Here, we report the implementation of a synthetic metabolic pathway in Populus tremula × P. alba to produce 2-pyrone-4,6-dicarboxylic acid (PDC), a key building block for biodegradable plastics and high-performance materials. This artificial pathway—comprising microbial genes AroG, QsuB, PmdA, PmdB, and PmdC—enabled de novo PDC production in the stems of transgenic poplar. Pathway expression also induced substantial metabolic reprogramming and altered cell wall composition. These include the hyperaccumulation of simple phenolics like protocatechuic acid (PCA) and vanillic acid (VA), alongside reduced levels of p-hydroxybenzoic acid. A large portion of VA was ester-linked to cell wall lignin, while PCA was incorporated into the lignin backbone, forming novel benzodioxane units; concurrently, lignin in transgenic plants exhibited a drastic reduction in guaiacyl- and syringyl-units, with a notable increase in p-hydroxyphenyl-units. Hemicellulose content, particularly xylan, was also significantly increased. Moreover, expression of the PDC-pathway led to the formation of novel VA-derived suberin aromatics, enhancing suberization in bark and roots and improving salt stress tolerance. These changes led to improved saccharification efficiency, with up to 25% more glucose and 2.5 times xylose released from woody biomass. These results demonstrate the metabolic flexibility of poplar and highlight its potential for engineering cost-effective, stress-resilient bioenergy crops with enhanced biorefinery traits.

2-pyrone-4↗
DOE OSTI2025

Engineered Methylobacterium extorquens grows well on methoxylated aromatics due to its formaldehyde metabolism and stress response

ABSTRACT Lignin is a vast yet underutilized source of renewable energy. The microbial valorization of lignin is challenging due to the toxicity of its degradation intermediates, particularly formaldehyde. In this study, we engineeredMethylobacterium extorquensPA1 to metabolize lignin-derived methoxylated aromatics, vanillate (VA) and protocatechuate (PCA), by introducing thevanandpcagene clusters. Compared toPseudomonas putida,M. extorquensPA1 exhibited better formaldehyde detoxification, enabling robust growth on VA without accumulation of formaldehyde. Genetic analyses confirmed that formaldehyde oxidation and stress response systems, rather than C 1 assimilation, were important for VA metabolism. Additionally, VA and PCA were found to disrupt membrane potential, contributing to their inherent toxicity. Our findings establishM. extorquensPA1 as a promising chassis for lignin valorization and provide a framework for engineering formaldehyde-resistant microbial platforms. IMPORTANCE In developing biotechnological solutions for a circular economy, it is critical to valorize all parts of renewable resources, such as lignocellulose from vegetative components of agricultural crops and from bioenergy feedstocks. After chemical breakdown, the aromatics arising from lignin present significant challenges for use due to their toxicity. Here, we address one component of this challenge—the methoxy groups that get released as formaldehyde—and show that existing biotechnological platform organisms with strong formaldehyde metabolism, such asMethylobacterium extorquens, can be transformed into highly capable utilizers of methoxylated aromatics.

Microbiology↗
DOE OSTI2026

Effects of 9.5 years warming on SOC concentration and composition in bulk soil and density fractions

Original data of whole-soil warming experiment after 9.5 years at Blodgett Forest Research Station. The Blodgett Forest is a mixed coniferous temperate forest with Mediterranean climate. The annual air temperature is 12.5℃ and the annual precipitation is 1774 mm yr-1- The soil is mesic ultic Alfisol of granitic origin, equivalent to Dystric Cambisol according to The World Reference Base for Soil Resources (WRB) system. The soil is warmed down to 1 m at + 4℃ by vertically installed heating cables. At the time of soil sampling on 1 May 2023, the whole-soil warming experiment had been running for approximately 9.5 years, from January 2014 to May 2023. The dataset includes: - Bulk_EA: C, N content, δ13C, and CN ratio of bulk soil; - Density_fractionation: organic carbon concentration, δ13C, and C/N ratio of free light fraction (fLF), occluded ligh fraction (oLF), and heavy fraction (HF); - PCA_DRIFT_AUC: original data of area under the curve (AUC) values of eight carbon bond types integrated on diffuse reflectance infrared fourier transform spectroscopy for each soil sample and soil fraction, which are consequently used for principal component analysis (PCA); - DRIFTS_stability_index: the calculation of aliphatic C–H (3000–2800 cm-1) to aromatic C=C (1670–1600 cm-1) ratios for each bulk soil sample and soil fraction. All data are provided in CSV format and can be viewed using Microsoft Excel.

Climate change↗