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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Remote Americium Detection Using an Optical Sensor: A D-Optimal Strategy for Efficient PLS-Based Modeling

A fiber-optic visible–near-infrared absorption spectroscopy system in a glove box was demonstrated for remote quantification of Am(III) (0–500 µM) and HNO 3 (0.1–9 M) using partial least squares regression (PLSR) models. The sensor platform, featuring a simple plug-and-play spectrophotometer, can enable noninvasive, real-time monitoring of actinide process solutions. To establish a flexible PLSR model calibration strategy, a D-optimal design developed using Nd(III) in previous studies was successfully extended to an actinide system with Am(III) to effectively minimize sample set size while maintaining robust prediction performance. The results suggest strong spectral similarities between Nd(III) and Am(III) and validate Nd(III) as an effective optical surrogate for trivalent actinide species. This work also supports the generalizability of a D-optimal training set selection approach for two-factor systems. The PLS1 models for Am(III) and HNO 3 outperformed a PLS2 model and maintained reasonable performance in the presence of interfering U(VI). The resulting sensor system and multivariate approach provides a flexible and scalable solution for process monitoring, control, and safety in diverse nuclear applications.

actinide↗
DOE OSTI2024

Importance of standard terminology in surface chemical analysis: ISO 18115-1:2023, general terms and terms used in spectroscopy

The International Standard ISO 18115-1 on terminology in surface chemical analysis has been revised with clarifications, modifications, and deletions to more than 70 terms and with the addition of more than 50 terms in response to trends, issues, and needs identified by the surface analysis community. This revision adds terminology and concepts associated with emerging methods such as atom probe tomography, near ambient pressure XPS, and hard X-ray photoelectron spectroscopy. It includes 25 new and revised terms to ensure that the description of resolution is consistent across all surface analysis methods. The 630 terms in the document cover words or phrases used in describing the samples, instruments, and concepts involved in surface chemical analysis. Here, the terms have been collated into subject specific sections to ensure that related terms can be found easily.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy

Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods.

09 BIOMASS FUELS↗
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparison of venous and pooled capillary hemoglobin levels for the detection of anemia among adolescent girls

Introduction: Blood source is a known preanalytical factor affecting hemoglobin (Hb) concentrations, and there is evidence that capillary and venous blood may yield disparate Hb levels and anemia prevalence. However, data from adolescents are scarce. Objective: To compare Hb and anemia prevalence measured by venous and individual pooled capillary blood among a sample of girls aged 10–19 years from 232 schools in four regions of Ghana in 2022. Methods: Among girls who had venous blood draws, a random subsample was selected for capillary blood. Hb was measured using HemoCue® Hb-301. We used Lin’s concordance correlation coefficient (CCC) to quantify the strength of the bivariate relationship between venous and capillary Hb and a paired t-test for difference in means. We used McNemar’s test for discordance in anemia cases by blood source and weighted Kappa to quantify agreement by anemia severity. A multivariate generalized estimating equation was used to quantify adjusted population anemia prevalence and assess the association between blood source and predicted anemia risk. Results: We found strong concordance between Hb measures (CCC = 0.86). The difference between mean venous Hb (12.8 g/dL, ± 1.1) and capillary Hb (12.9 g/dL, ± 1.2) was not significant (p = 0.26). Crude anemia prevalence by venous and capillary blood was 20.6% and 19.5%, respectively. Adjusted population anemia prevalence was 23.5% for venous blood and 22.5% for capillary ( = 0.45). Blood source was not associated with predicted anemia risk (risk ratio: 0.99, 95% CI: 0.96, 1.02). Discordance in anemia cases by blood source was not significant (McNemar p = 0.46). Weighted Kappa demonstrated moderate agreement by severity (k =0.67). Among those with anemia by either blood source (n = 111), 59% were identified by both sources. Conclusion: In Ghanaian adolescent girls, there was no difference in mean Hb, anemia prevalence, or predicted anemia risk by blood source. However, only 59% of girls with anemia by either blood source were identified as having anemia by both sources. These findings suggest that pooled capillary blood may be useful for estimating Hb and anemia at the population level, but that caution is needed when interpreting individual-level data.

59 BASIC BIOLOGICAL SCIENCES↗
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 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 OSTI2019

The Relationship Between Gelatin Sponge Preparation Methods and the Incidence of Intrauterine Synechia Following Uterine Artery Embolization for Postpartum Hemorrhage

Purpose: To evaluate the relationship between gelatin sponge preparation methods and the incidence of intrauterine synechia following uterine artery embolization (UAE) for postpartum hemorrhage (PPH). Materials and Methods: In a retrospective monocentric study, we used data from 20 consecutive UAE procedures (19 patients) for PPH, performed in 2007–2016, in which gelatin sponge had been used. The gelatin sponge was processed either into a slurry by pumping it back and forth about 10 times through two syringes connected to a three-way stopcock or into pledgets using a scalpel and small scissors to obtain pieces approximately 2 × 2 × 2 mm in size. Patient information was obtained from medical records, and the data were compared between patients treated with the slurry (n = 7) or pledgets (n = 13) forms. Due to the lack of follow-up data and hysterectomy after UAE, the sample size was 6 and 12 because 1 patient with 2 procedures was excluded. Results: The rate of intrauterine synechia was significantly higher in the slurry group (5/6, 83.3%) than that in the pledgets group (0/12, 0%; P < 0.001). In contrast, there were no significant differences in population characteristics, such as the incidence of placenta accreta, non-placental diseases, and severity of shock (DIC score, shock index, or blood loss) between the groups. Conclusions: Although non-randomization and small sample size were the two main limitations, our observations suggest that UAE using gelatin sponge slurry may be associated with a high incidence of intrauterine synechia compared to UAE using pledgets.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy

The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO 2 , TiO 2 , Al 2 O 3 , FeO T , MgO, CaO, Na 2 O, K 2 O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3 m. In cases with several models of comparable performance at 3 m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.

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

Structure–Activity Relationships in Ether-Functionalized Solid-State Metal–Organic Framework Electrolytes

The structure–property relationships of metal–organic framework (MOF)-based solid-state electrolytes are not well understood. Herein, a systematic investigation of 12 Zr(IV)-based UiO-66 MOFs with varying ether-chain functional groups was carried out to elucidate the critical microscopic interactions that facilitate improved solid-state electrolyte performance. Enhanced sampling molecular dynamics (MD) simulations were employed and revealed a three-tier ion hopping mechanism: linker–linker hopping, linker-counterion hopping, and counterion-counterion hopping. Detailed structural analysis of the MD trajectories revealed that the chemistry and morphology of the linker groups affect the relative stability and population distribution of the electrolyte components, such that crown-ether-based linker groups enhance the probability of extended, low-barrier ion percolation pathways. As a result, we were able to tune the ionic conductivities by rationally manipulating the counterion distributions, linker binding strengths, and the configurational entropy (multivariability of the linkers). The resulting performance of these MOF-based solid-state electrolytes was significantly enhanced, with a methoxy-functionalized framework (UiO-66-L1 100 ) achieving high ionic conductivities of 2.32 × 10 –4 S/cm and 2.07 × 10 –3 S/cm at 30 °C and 90 °C, respectively, an order of magnitude greater than other all-solid-state MOF electrolyte systems. The electrolyte stability was evaluated with LiIn|LPSCl|MOF:LiTFSI|LPSCl|LiIn symmetric cells, showing excellent Li plating/stripping processes for over 2 months.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Absolute Band Intensity of the Iodine Monochloride Fundamental Mode for Infrared Sensing and Quantitative Analysis

Iodine monochloride (ICl) is a gaseous off-product of molten salt reactors; monitoring this heteronuclear diatomic is of great interest for both environmental and safety purposes. In this paper we investigate the possibility of infrared monitoring of ICl by measuring the far-infrared absorption cross section of its fundamental band near 381 cm -1 . We have performed quantitative studies of the neat gas in a 20 cm cell at 25, 35, 50 and 70 oC at multiple pressures up to ~ 9 Torr and investigated the temperature and pressure dependence of the band’s infrared cross section. Quantitative measurements were problematic due to sample adhesion to the cell walls and windows as well as reactions/possible hydrolysis of ICl to form HCl gas. Effects were mitigated by measuring only the neat gas, using short measurement times and subtracting out the partial pressure of the HCl(g). The integrated band strength is shown to be temperature independent and was found to be equal to 9.1 x 10 -19 (cm 2 /molecule) cm-1. As expected, the temperature dependence of the band profile showed only a small effect over this limited temperature range. Furthermore, we have also investigated using the absorption data along with inverse least squares multivariate methods for the quantitative monitoring of ICl effluent concentrations under different scenarios using infrared (standoff) sensing and compare these results with traditional Beer’s Law (univariate) techniques.

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

Development of chemometric models to classify solid-state U materials by micro-Raman spectroscopy

Discerning uranium (U) particles found in environmental sampling is of interest for monitoring the peaceful use of nuclear material. In this study, a soft independent modeling of class analogy (SIMCA) library was successfully developed for the classification of a four-class system consisting of α-U 3 O 8 , UO 2 , UO 2 (NO 3 ) 2 ·6H 2 O (UNH), and UO 2 O 2 ·4H 2 O (studtite) by Raman spectroscopy in the presence of matrix particulates and additional outliers. Spectral variability between numerous particles of each type revealed appreciable differences as a function of particle size with respect to hydration state and potential oxide phase within each class. Interclass variability was accounted for using both unsupervised and supervised chemometric models. The supervised SIMCA model displayed reasonable sensitivity for each U class and a high degree of specificity by returning whether a spectrum belonged to one class or not. This work demonstrates how Raman spectral features and chemometrics can be used to distinguish U materials from one another and from matrix materials such as flint clay. Combining the outlined chemometric approach with Raman mapping sequences could provide a rapid, nondestructive technique to characterize the chemical composition of a diverse collection of U compounds amid background samples for environmental sampling, nuclear forensics, and industrial applications.

Actinide↗
DOE OSTI2023

Search for a new heavy scalar particle decaying into a Higgs boson and a new scalar singlet in final states with one or two light leptons and a pair of τ -leptons with the ATLAS detector

A search for a new heavy scalar particle X decaying into a Standard Model (SM) Higgs boson and a new singlet scalar particle S is presented. The search uses a proton-proton (pp) collision data sample with an integrated luminosity of 140 fb -1 recorded at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV with the ATLAS detector at the Large Hadron Collider. The most sensitive mass parameter space is explored in X mass ranging from 500 to 1500 GeV, with the corresponding S mass in the range 200–500 GeV. The search selects events with two hadronically decaying τ-lepton candidates from H → τ + τ - decays and one or two light leptons (ℓ = e, μ) from S → VV (V = W, Z) decays while the remaining V boson decays hadronically or to neutrinos. A multivariate discriminant based on event kinematics is used to separate the signal from the background. No excess is observed beyond the expected SM background and 95% confidence level upper limits between 72 fb and 542 fb are derived on the cross-section σ(pp → X → SH) assuming the same SM-Higgs boson-like decay branching ratios for the S → VV decay. Upper limits on the visible cross-sections σ(pp → X → SH → WWττ) and σ(pp → X → SH → ZZττ) are also set in the ranges 3–26 fb and 6–33 fb, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Simultaneous calibration of spectro-photometric distances and the Gaia DR2 parallax zero-point offset with deep learning

ABSTRACT Gaia measures the five astrometric parameters for stars in the Milky Way, but only four of them (positions and proper motion, but not distance) are well measured beyond a few kpc from the Sun. Modern spectroscopic surveys such as APOGEE cover a large area of the Milky Way disc and we can use the relation between spectra and luminosity to determine distances to stars beyond Gaia’s parallax reach. Here, we design a deep neural network trained on stars in common between Gaia and APOGEE that determines spectro-photometric distances to APOGEE stars, while including a flexible model to calibrate parallax zero-point biases in Gaia DR2. We determine the zero-point offset to be $-52.3 \pm 2.0\, \mu \mathrm{as}$ when modelling it as a global constant, but also train a multivariate zero-point offset model that depends on G, GBP − GRP colour, and Teff and that can be applied to all ≈58 million stars in Gaia DR2 within APOGEE’s colour–magnitude range and within APOGEE’s sky footprint. Our spectro-photometric distances are more precise than Gaia at distances ${\gtrsim} 2\, \mathrm{kpc}$ from the Sun. We release a catalogue of spectro-photometric distances for the entire APOGEE DR14 data set which covers Galactocentric radii $2\, \mathrm{kpc} \lesssim R \lesssim 19\, \mathrm{kpc}$; ${\approx} 150\, 000$ stars have ${\lt} 10{{\ \rm per\ cent}}$ uncertainty, making this a powerful sample to study the chemo-dynamical structure of the disc. We use this sample to map the mean [Fe/H] and 15 abundance ratios [X/Fe] from the Galactic Centre to the edge of the disc. Among many interesting trends, we find that the bulge and bar region at $R \lesssim 5\, \mathrm{kpc}$ clearly stands out in [Fe/H] and most abundance ratios.

Leung, Henry W.↗
DOE OSTI2026

Design of a Molten Salt Flow Cell for Combined Absorbance and Laser-Induced Breakdown Spectroscopy for Online Measurements

A novel flow cell allowing for multiple optical spectroscopy measurements on flowing molten salts was designed, and demonstrative calibrations of impurities in aqueous samples were performed. Online compositional measurements of molten salts are of high interest to monitor the state of relevant solar and nuclear systems. Here, the Spectroscopic Configuration for Optical Real-Time Characterization of High-Temperature (SCORCH) fluids cell was designed to meet this need by providing optical access to a high-temperature molten salt sample stream without physical contact between the sample and window materials. Laser-induced breakdown spectroscopy (LIBS) was utilized to quantify Li, Cr, Fe, Ni, Sr, and Pr at concentrations ranging nominally from 0 to 315 mmol L −1 . Laser power, frequency, and plasma position were optimized to mitigate challenges associated with sample splashing. Univariate calibration models were built with R 2 > 0.98, percent root mean square error of cross-validation (%RMSECV) as low as 2.7%, and limits of quantification (LOQs) down to 4.1 mmol L −1 . Simultaneously, absorbance calibrations were developed for the applicable analytes (Cr, Ni, and Pr) using Beer’s law with a pathlength of 4.41 ± 0.10 mm. These models provide excellent quantification performance with R 2 > 0.999, %RMSECV as low as 0.6%, and LODs down to 0.08 mmol L −1 . Although these calibrations were performed for each spectroscopic technique separately, the two methods may be combined in the future through multivariate modeling and sensor fusion to provide more robust models with the benefits of both techniques (e.g., absorbance: oxidation state concentrations, LIBS: elemental concentration). Additionally, optimized spectrometers may be deployed to enhance sensitivity.

absorbance spectroscopy↗
DOE OSTI2018

Comparing vis–NIRS, LIBS, and Combined vis–NIRS‐LIBS for Intact Soil Core Soil Carbon Measurement

Core Ideas First rigorous integration of VisNIR and LIBS spectroscopic techniques for soil profile C measurement. Field‐moist intact soil cores scanned with LIBS and VisNIR simulating in situ measurements. Combined VisNIR‐LIBS wavelengths selected by MRCE were split nearly equally between sensors, suggesting molecular and elemental information was successfully integrated for predicting TC, IC, and SOC. Stoichiometric relationships in LIBS elemental emissions are more strongly correlated to IC concentration than were carbonate absorptions in VisNIR. Lack of variation and consistent, definable chemical composition and stoichiometry likely contributed to poor LIBS and combined VisNIR – LIBS prediction accuracy for SOC. Soil organic carbon (SOC) measurement is critically important to quantify regional and global soil C stocks and better understand soil C biogeochemical processes. Recent studies employing laser‐induced breakdown spectroscopy (LIBS) and visible‐near infrared diffuse reflectance spectroscopy (vis–NIRS) indicate their potential for in situ soil C determination. Visible and near infrared diffuse reflectance spectroscopy and LIBS spectroscopy fundamentally differ and we hypothesize that their integration would provide improved soil C predictions. We report the first rigorous integration of vis–NIRS and LIBS, evaluating the precision of vis–NIRS, LIBS, and combined vis–NIRS‐LIBS spectra for simulated in situ soil profile total C (TC), inorganic C (IC) and SOC measurement. Three multivariate variable selection and regression approaches were evaluated for soil C prediction. The highest soil C prediction accuracies were observed using multivariate regression with covariance estimation (MRCE). Inorganic C was best predicted by LIBS, vis–NIRS provided better SOC predictions, and TC was best predicted using combined vis–NIRS‐LIBS data. Combined vis–NIRS‐LIBS did not consistently increase soil C prediction accuracy. Soil organic C was not well predicted, presumably due to challenges associated with scanning surfaces of intact soil cores, variable SOC chemistries, and low SOC variation in the dataset. Considering the challenging conditions under which combined vis–NIRS– LIBS was tested for soil C measurement, data integration and model calibrations had acceptable performance. Further testing under more controlled soil conditions with samples containing greater SOC diversity is necessary to determine the technical potential of combined vis–NIRS/LIBS for soil C determination.

Bricklemyer, Ross S.↗
DOE OSTI2025

Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. In conclusion, based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.

Inertial confinement fusion↗
DOE OSTI2020

CsI(Tl) pulse shape discrimination with the Belle II electromagnetic calorimeter as a novel method to improve particle identification at electron–positron colliders

Here we describe the implementation and performance of CsI(Tl) pulse shape discrimination for the Belle II electromagnetic calorimeter, representing the first application of CsI(Tl) pulse shape discrimination for particle identification at an electron–positron collider. The pulse shape characterization algorithms applied by the Belle II calorimeter are described. Control samples of $γ, μ^+, π^±, K^±$ and $ρ/ \bar{ρ}$ are used to demonstrate the significant insight into the secondary particle composition of calorimeter clusters that is provided by CsI(Tl) pulse shape discrimination. Comparisons with simulation are presented and provide further validation for newly developed CsI(Tl) scintillation response simulation techniques, which when incorporated with GEANT4 simulations allow the particle dependent scintillation response of CsI(Tl) to be modelled. Comparisons between data and simulation also demonstrate that pulse shape discrimination can be a new tool to identify sources of improvement in the simulation of hadronic interactions in materials. The $K^0_L$ efficiency and photon-as-hadron fake-rate of a multivariate classifier that is trained to use pulse shape discrimination is presented and comparisons are made to a shower-shape based approach. CsI(Tl) pulse shape discrimination is shown to reduce the photon-as-hadron fake-rate by over a factor of 3 at photon energies of 0.2 GeV and over a factor 10 at photon energies of 1 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗