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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Destructive Examination of Shipping Package 9975-02731

Destructive and non-destructive examinations were performed on the components of the package 9975-02731 as part of a comprehensive Savannah River Site surveillance program for plutonium materials stored in the K-Area Complex (KAC). The package stored nuclear materials in KAC for 13.8 years. Inspection of the package during opening in KAC noted one non-conforming condition: a large discolored area on the upper fiberboard subassembly. The package was subsequently transferred to Savannah River National Laboratory for more detailed examination. In addition to the observation noted in KAC, the following conditions were observed: (1) Axial gap of 1.021 inch slightly exceeded the 1 inch maximum criterion, and (2) Lead shield height exceeded drawing tolerance of 24.7 inch by 0.019 inch. Visual examination of the upper fiberboard assembly and analysis of the discolored fiberboard indicate the discoloration was due to wood glue residue. The physical, thermal, and mechanical properties of the fiberboard met the acceptance criteria and/or were within the range of previously tested fiberboard and baseline samples. The visual, microscopic and tensile testing results were consistent with previous O-ring and baseline Viton results. The lead shield surface was covered with corrosion product as observed on other shields but there was no evidence of excessive blistering, flaking, or spalling. Besides minor and expected corrosion, no evidence of a degraded condition was found in this package. Examination of the 9975-02731 corroborates existing evidence that the 9975 package is robust and the components can perform their expected functions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Template Engine Applied to Rapid Modeling

Modeling complex problems can require a significant effort, be subject to human error, and contain repeated patterns of input. SCALE 6.2 and the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) have attempted to mitigate these problems with the use of a template engine and template syntax. The template syntax allows redundant input to be consolidated into its simplest pattern and parameterized on the parts that change, reducing effort and likelihood of errors and encapsulating user experience into a reusable model component. This paper presents a summary of rapid modeling benefits and techniques that are facilitated when using templates. Specifically, this paper will discuss how these are applied in conducting as-loaded spent nuclear fuel canister criticality calculations using UNF-ST&DARDS and model perturbation and sampling using.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Discussion on molecular dynamics (MD) simulations of the asphalt materials

The application of asphalt materials in pavement engineering has been increasingly widespread and sophisticated over the past several decades. Variations in the properties of asphalt binder during mixing, transportation, and paving can affect the performance of asphalt pavement. However, the asphalt material is a non-homogeneous and complex organic substance, consisting of various molecules with widely various molecular weights, elemental compositions, and structures. This complexity leads to difficulties for researchers to clearly and immediately understand the properties of asphalt materials and their variations. The multi-scale research approach combines macroscopic experimental data and microscopic simulation results from a practical engineering perspective. It helps to improve the understanding of asphalt materials. The molecular dynamics (MD) simulation proposes a corresponding molecular model of asphalt material based on experimental data, and the simulation algorithm is able to derive properties similar to those of real asphalt. Here, this paper provides a comprehensive review of the current studies on MD simulation of asphalt materials, including modeling, properties, and multi-scale analysis. As a key part of the computational simulation, this paper discusses the typical asphalt binder and asphalt-aggregate interface models constructed by different groups, and also presents their differences from real samples and their feasibility based on fundamental properties. After the introduction of molecular models, the extensive work made by researchers based on molecular models is categorically reviewed and discussed. The strengths and weaknesses of MD simulation methods in the study of asphalt materials are also summarized in order to provide the reader with a more comprehensive understanding of the relevant contents and to guide subsequent research.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A method for mapping submicron-scale crystallographic order/disorder applied to human tooth enamel

Tooth enamel, the outermost layer of human teeth, is a complex, hierarchically structured biocomposite. The details of this structure are important in multiple human health contexts, from understanding the progression of dental caries (tooth decay) to understanding the process of amelogenesis and related developmental defects. Enamel is composed primarily of long, nanoscale crystallites of hydroxyapatite that are bundled by the thousands to form micron-scale rods. Studies with transmission electron microscopy show the relationships between small groups of crystallites and X-ray diffraction characterize averages over many rods, but the direct measurement of variations in local crystallographic structure across and between enamel rods has been missing. Here, we describe a synchrotron X-ray-based experimental approach and a novel analysis method developed to address this gap in knowledge. A ~500-nm-wide beam of monochromatic X-rays in conjunction with a sample section only 1 μm in thickness enables 2D diffraction patterns to be collected from small well-separated volumes within the enamel microstructure but still probes enough crystallites (~300 per pattern) to extract population-level statistics on crystallographic features like lattice parameter, crystallite size, and orientation distributions. Furthermore, the development of a quantitative metric to characterize relative order and disorder based on the azimuthal autocorrelation of diffracted intensity enables these crystallographic measurements to be correlated with their location within the enamel microstructure (e.g., between rod and interrod regions). These methods represent a step forward in the characterization of human enamel and will elucidate the variation of the crystallographic structure across and between enamel rods for the first time.

crystallographic order/disorder↗
DOE OSTI2022

Magnetic structure and exchange interactions in the Heisenberg pyrochlore antiferromagnet Gd 2 Pt 2 O 7

In this report the Heisenberg pyrochlore antiferromagnet Gd 2 Pt 2 O 7 is one of a series of gadolinium pyrochlore compounds with a variety of B-site cations. Despite the expected simplicity of a spin-only Gd 3+ Heisenberg interaction model, the gadolinium pyrochlore series exhibits various complex magnetic ground states at low temperature. Gd 2 Pt 2 O 7 displays the highest temperature magnetic order of the series with $T_N$=1.6 K, which has been attributed to enhanced superexchange pathways facilitated by empty $5d$ $e_g$ Pt orbitals. In this study, we use various neutron scattering techniques on an isotopically enriched polycrystalline 160 Gd 2 Pt 2 O 7 sample to examine the magnetic structure and spin-wave excitation spectrum below $T_N$ in order to extract the dominant exchange interactions. We find that the ground-state magnetic structure is the Palmer-Chalker state previously seen in Gd 2 Sn 2 O 7 with an associated gapped excitation spectrum consistent with enhanced exchange interactions between further near-neighbor Gd 3+ ions. We confirm this exchange model with analysis of the magnetic diffuse scattering in the paramagnetic regime using polarized neutrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Template engine applied to rapid modeling

Modeling complex problems can require a significant effort, be subject to human error, and contain repeated patterns of input. SCALE 6.2 and the Used Nuclear Fuel-Storage, Transportation and Disposal Analysis Resource and Data System (UNF-ST-DARDS) have attempted to mitigate these problems with the use of a template engine and template syntax. The template syntax allows redundant input to be consolidated into its simplest pattern and parameterized on the parts that change, reducing effort and likelihood of errors and encapsulating user experience into a reusable model component. This paper presents a summary of rapid modeling benefits and techniques that are facilitated when using templates. Specifically, this paper will discuss how these are applied in conducting as-loaded spent nuclear fuel canister criticality calculations using UNF-ST-DARDS and model perturbation and sampling using SCALE 6.2. (authors)

42 ENGINEERING↗
DOE OSTI2016

Recent Advances in Analytical Electron Microscopy for Irradiated Materials

Analytical electron microscopy (AEM) in the scanning transmission electron microscope (STEM) has long been a staple of post-irradiation examination (PIE) and materials development campaigns. Recently, new advances in both STEM hardware and post-processing data-analysis software, along with ancillary advances such as in focused ion beam (FIB) sample preparation, has led to exciting advances in the quantity and quality of data that can be obtained. This talk will summarize recent experiments performed at Oak Ridge National Laboratory (ORNL), especially work on neutron-irradiated materials and fuels taking advantage of a newly-installed latest-generation AEM tool in the ORNL irradiated materials laboratories. ORNL installed a new FEI Talos F200X microscope, a 200 keV, high-brightness Schottky emission AEM system optimized for high-speed, high-efficiency X-ray mapping experiments. The F200X features the FEI 'SuperX' four-sector silicon drift detector (SDD) X-ray detector, which provides 0.9 sr solid angle of collection. By combining the high-brightness electron source, latest-generation electron optics, and fast computers and pulse processors, X-ray mapping speed is increased by a factor of 50-100 x compared to the previous-generation instruments. In addition to improvements in hardware, data mining via multivariate statistical analysis (MVSA) allows for maximum insight to the data obtained from the new hardware. MVSA data mining of the X-ray maps allows, for instance, unbiased computational identification of the phases present, and discovery of small phases or precipitates not expected by the human analyst. This talk will present examples of experiments performed using the most modern AEM tools and software. Silicon carbide is a major candidate for next-generation nuclear applications. Nano-infiltration by transient eutectic (NITE) SiC requires low sintering temperatures. X-ray mapping of the NITE structure find pockets of different oxides, such as Zr-Y-oxide, Y-Al-oxide, and yttria. MVSA data mining finds the regions where different oxides are present and indicates that Y additions preferentially wet grain boundaries, where Zr additions are preferentially concentrated at grain triple points. SiC coatings from AGR-I experiment TRISO particles were found to contain significant quantities of fission products (e.g., Pd) as well as actinides (U, Pu). MVSA data mining found complex precipitates and grain boundary films of the fission products and actinides deep (∼several microns) into the SiC layer. Advanced AEM methods, combined with MVSA data mining, makes possible both rapid and detailed PIE. Ferritic alloys, such as accident-tolerant FeCrAl grades, benefit greatly from advanced AEM examination. Neutron-irradiated FeCrAl precipitates the detrimental Cr-rich α' phase. Because the SuperX SDD system has four detectors, it is possible to obtain high X-ray count rates even on a highly tilted specimen, as might be needed to obtain STEM images of dislocation loops. Results on neutron-irradiated FeCrAl indicates that α' size and number density can be obtained by AEM, and MVSA results determine that minor elements (such as Si) has complex radiation-induced segregation behavior at grain boundaries. We have also developed and validated computer models of the AEM experiment, allowing different microstructures' X-ray map results to be simulated for different STEM instruments. This allows X-ray mapping to be more quantitative, such as by estimating what size nanoclusters in an oxide-dispersion strengthened steel would be visible or invisible in STEM. In summary, new advanced AEM technology provides a major leap forward in experiments possible in PIE of irradiated materials. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2017

Development of low angle grain boundaries in lightly deformed superconducting niobium and their influence on hydride distribution and flux perturbation

This study shows that low angle grain boundaries (LAGBs) can be created by small 5% strains in high purity (RRR ≥ 200) SRF-grade single crystalline niobium (Nb) and that these boundaries act as hydrogen traps as indicated by the distribution of niobium hydrides (Nb 1-x H x ). Nb 1-x H x is detrimental to superconducting radio frequency (SRF) Nb cavities due to its normal conducting properties at cavity operating temperatures. By designing a single crystal tensile sample extracted from a large grain (>5 cm) Nb ingot slice for preferred slip on one slip plane, LAGBs and dense dislocation boundaries developed. With chemical surface treatments following standard SRF cavity fabrication practice, Nb1-xHx phases were densely precipitated at the LAGBs upon cryogenic cooling (8-10 K/min). Micro-crystallographic analysis confirmed heterogeneous hydride precipitation, which included significant hydrogen atom accumulation in LAGBs. Magneto-optical imaging (MOI) analysis showed that these sites can then act as sites for both premature flux penetration and eventually flux trapping. However, this hydrogen related degradation at LAGBs did not completely disappear even after a 800 °C/2hrs anneal typically used for hydrogen removal in SRF Nb cavities. These findings suggest that hydride precipitation at a LAGB is facilitated by a non-equilibrium concentration of vacancy-hydrogen (H) complexes aided by mechanical deformation and the hydride phase interferes with the recovery process under 800°C annealing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-T-148: Are the Approximations in Analytic Semi-Empirical Dose Calculation Algorithms for Intensity Modulated Proton Therapy for Complex Heterogeneities of Head and Neck Clinically Significant?

Purpose: Evaluate the differences in dose distributions between the proton analytic semi-empirical dose calculation algorithm used in the clinic and Monte Carlo calculations for a sample of 50 head-and-neck (H&N) patients and estimate the potential clinical significance of the differences. Methods: A cohort of 50 H&N patients, treated at the University of Texas Cancer Center with Intensity Modulated Proton Therapy (IMPT), were selected for evaluation of clinical significance of approximations in computed dose distributions. H&N site was selected because of the highly inhomogeneous nature of the anatomy. The Fast Dose Calculator (FDC), a fast track-repeating accelerated Monte Carlo algorithm for proton therapy, was utilized for the calculation of dose distributions delivered during treatment plans. Because of its short processing time, FDC allows for the processing of large cohorts of patients. FDC has been validated versus GEANT4, a full Monte Carlo system and measurements in water and for inhomogeneous phantoms. A gamma-index analysis, DVHs, EUDs, and TCP and NTCPs computed using published models were utilized to evaluate the differences between the Treatment Plan System (TPS) and FDC. Results: The Monte Carlo results systematically predict lower dose delivered in the target. The observed differences can be as large as 8 Gy, and should have a clinical impact. Gamma analysis also showed significant differences between both approaches, especially for the target volumes. Conclusion: Monte Carlo calculations with fast algorithms is practical and should be considered for the clinic, at least as a treatment plan verification tool.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Adverse Events Involving Radiation Oncology Medical Devices: Comprehensive Analysis of US Food and Drug Administration Data, 1991 to 2015

Purpose: Radiation oncology relies on rapidly evolving technology and highly complex processes. The US Food and Drug Administration collects reports of adverse events related to medical devices. We sought to characterize all events involving radiation oncology devices (RODs) from the US Food and Drug Administration's postmarket surveillance Manufacturer and User Facility Device Experience (MAUDE) database, comparing these with non–radiation oncology devices. Methods and Materials: MAUDE data on RODs from 1991 to 2015 were sorted into 4 product categories (external beam, brachytherapy, planning systems, and simulation systems) and 5 device problem categories (software, mechanical, electrical, user error, and dose delivery impact). Outcomes included whether the device was evaluated by the manufacturer, adverse event type, remedial action, problem code, device age, and time since 510(k) approval. Descriptive statistics were performed with linear regression of time-series data. Results for RODs were compared with those for other devices by the Pearson χ{sup 2} test for categorical data and 2-sample Kolmogorov-Smirnov test for distributions. Results: There were 4234 ROD and 4,985,698 other device adverse event reports. Adverse event reports increased over time, and events involving RODs peaked in 2011. Most ROD reports involved external beam therapy (50.8%), followed by brachytherapy (24.9%) and treatment planning systems (21.6%). The top problem types were software (30.4%), mechanical (20.9%), and user error (20.4%). RODs differed significantly from other devices in each outcome (P<.001). RODs were more likely to be evaluated by the manufacturer after an event (46.9% vs 33.0%) but less likely to be recalled (10.5% vs 37.9%) (P<.001). Device age and time since 510(k) approval were shorter among RODs (P<.001). Conclusions: Compared with other devices, RODs may experience adverse events sooner after manufacture and market approval. Close postmarket surveillance, improved software design, and manufacturer–user training may help mitigate these events.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Cost Analysis of Complex Radiation Therapy for Patients With Head and Neck Cancer

Purpose: This cost analysis aimed to prospectively assess differences in costs between TomoTherapy and volumetric modulated arc therapy (VMAT) in patients with head and neck cancer. Methods and Materials: Economic data were gathered from a multicenter study. However, randomization was not possible due to the availability of equipment. Costs were calculated using the microcosting technique from the hospital's perspective (in 2013 euros), and the time horizon was radiation therapy. Only resources that entered the hospital production process and which were likely to vary between the strategies being compared were considered. Acute adverse events observed within the time horizon were also assessed. Results: The cost analysis was based on a total of 173 patient treatments given between 2010 and 2012 in 14 French cancer centers: 73 patients were treated with TomoTherapy, 92 with VMAT RapidArc, and 8 with VMAT SmartArc. Estimated costs of SmartArc were removed from the comparison due to the small sample size. The mean ± SD cost per patient of the treatment planning phase was €314 (±€214) for TomoTherapy and €511 (±€590) for RapidArc. Mean costs ± SD per patient of irradiation reached €3144 (±€565) for TomoTherapy and €1350 (±€299) for RapidArc. The most sensitive parameter of irradiation was the annual operating time of accelerators. Ninety-five percent confidence intervals for the mean costs of irradiation were €3016 to €3272 for TomoTherapy and €1281 to €1408 for RapidArc. The number of acute adverse events during radiation therapy was not significantly different between strategies. Conclusions: TomoTherapy appeared to be more expensive than RapidArc mainly due to the higher price of the accelerator, the higher costs of maintenance, and the longer duration of treatment sessions. Because strategies were not significantly different in clinical effect, RapidArc appeared to be the strategy to be recommended at this stage of knowledge.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Physicochemical properties of digital light processing 3D-Printed alumina and mullite ceramics

Alumina ceramics fabricated using conventional techniques such as uniaxial pressing or injection molding are popular due to their low density, excellent insulation, and mechanical properties. However, these methods often limit the fabrication of complex geometries with high dimensional accuracy due to tooling constraints and limited design freedom. To overcome these limitations, this study employed Digital Light Processing (DLP) additive manufacturing (AM), which enables the production of precise structures. In addition, the optimization of sintering parameters to enhance the densification and performance of alumina and mullite ceramics was investigated, with a specific focus on how varying sintering temperatures and hold times affect part shrinkage, geometric accuracy, and material integrity. Flexural strength of both alumina and mullite specimens was clearly influenced by the way the layers was stacked. When layers were arranged across the direction of the applied load (Z = 4; XZ), the strength was higher than when they were stacked along the same direction as the load (Z = 3; XY). Weibull analysis based on these results showed high modulus values across all samples, indicating good reliability in the flexural strength measurements. This reliability, combined with the clear influence of sintering parameters, highlights how processing conditions effect the material properties and mechanical performance of parts produced through DLP additive manufacturing. In conclusion, these findings open new avenues for ceramic AM across various applications, enhancing the potential for innovation in fields such as aerospace, biomedical engineering, and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R 2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Novel Cell-Type-Specific Drought-Responsive Proteins in Root Tips of Field-Grown Perennial Switchgrass

The root-tip region of plants, including the root cap, forms the most basal terminal of the root and exhibits a high degree of cellular complexity in terms of morphology, cytological function, and interaction with environmental cues in the soil. Cells in this region follow a developmental trajectory, transitioning from stem cells to meristematic cells, and ultimately to fully differentiated cell types. However, our understanding of root-tip cell-type specific proteomic responses to abiotic stresses, such as drought, particularly under field conditions, remains limited. This study aimed to identify spatially resolved, cell type-specific proteomes in switchgrass (Panicum virgatum) root tips under drought stress. Root tips were collected from seven-year-old, field-grown switchgrass ‘Alamo’ plants excavated under both well-watered and long-term drought conditions. Cell type-specific proteins were identified using laser capture microdissection (LCM) coupled with nanoPOTS (Nanodroplet Processing in One Pot for Trace Samples) and nano-LC-MS proteomics analysis. Five distinct cell types were targeted: (1) cells in the quiescent center and stem cell niche (QuC), (2) protodermal epidermal cells (PEC) in the meristematic zone, (3) epidermal cells in the transition and elongation zones above the root cap (Epi), (4) peripheral root cap cells (PRC), forming 2–3 layers below the PEC and 1–2 layers above the root border cells, and (5) columella root cap cells (Col) comprising of the columella initials and a single underlying layer of cells undergoing active growth. Principal component analysis (PCA) revealed clear separation among the five targeted cell types, confirming distinct proteomic profiles. Proteins predominantly enriched in each cell type were linked to distinct cellular functions, with QuC cells showing involvement in chromosomal behavior, DNA replication, and mitosis—key processes for stem cell niche regulation. Drought stress resulted in alterations of proteostasis, as evidenced by significant decreases in ribosomal proteins and increases in protein synthesis inhibitors. Moreover, drought stress induced unique cell-type–specific proteins involved in phytohormone biosynthesis and signaling pathways, including auxin, cytokinin, and jasmonic acid. In particular, QuC cells were more highly enriched in proteins associated with DNA repair and mitotic processes. Metabolic pathways related to amino acids, carbohydrates, and lipids were differentially affected in a cell-type–dependent manner, whereas general stress-responsive proteins exhibited consistent changes across all five cell types. Overall, this study provides unique spatially resolved, cell-type-specific proteomic profiles in root tips, representing a significant advancement in our understanding of the cellular mechanisms underlying plant responses to drought stress in natural field conditions.

perennial grass↗
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↗