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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 1,459 records · Page 81
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DOE OSTI2016

SU-F-T-119: Development of Heart Prediction Model to Increase Accuracy of Dose Reconstruction for Radiotherapy Patients

Purpose: To assess individual variation in heart volume and location in order to develop a prediction model of the heart. This heart prediction model will be used to calculate individualized heart doses for radiotherapy patients in epidemiological studies. Methods: Chest CT images for 30 adult male and 30 adult female patients were obtained from NIH Clinical Center. Image-analysis computer programs were used to segment the whole heart and 8 sub-regions and to measure the volume of each sub- region and the dimension of the whole heart. An analytical dosimetry method was used for the 30 adult female patients to estimate mean heart dose during conventional left breast radiotherapy. Results: The average volumes of the whole heart were 803.37 cm{sup 3} (COV 18.8%) and 570.19 cm{sup 3} (COV 18.8%) for adult male and female patients, respectively, which are comparable with the international reference volumes of 807.69 cm{sup 3} for males and 596.15 cm{sup 3} for females. Some patient characteristics were strongly correlated (R{sup 2}>0.5) with heart volume and heart dimensions (e.g., Body Mass Index vs. heart depth in males: R{sup 2}=0.54; weight vs. heart width in the adult females: R{sup 2}=0.63). We found that the mean heart dose 3.805 Gy (assuming prescribed dose of 50 Gy) in the breast radiotherapy simulations of the 30 adult females could be an underestimate (up to 1.6-fold) or overestimate (up to 1.8-fold) of the patient-specific heart dose. Conclusion: The study showed the significant variation in patient heart volumes and dimensions, resulting in substantial dose errors when a single average heart model is used for retrospective dose reconstruction. We are completing a multivariate analysis to develop a prediction model of the heart. This model will increase accuracy in dose reconstruction for radiotherapy patients and allow us to individualize heart dose calculations for patients whose CT images are not available.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-D-207B-02: Delta-Radiomics: The Prognostic Value of Therapy-Induced Changes in Radiomics Features for Stage III Non-Small Cell Lung Cancer Patients

Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co-occurrence, run-length, gray-tone difference, and shape features, were calculated from pretreatment and weekly intra-treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit-depth resample and/or a smoothing filter were tested for their impact on volume-correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized features were calculated from the intratreatment images and tested in linear mixed-effects models to determine which features changed significantly with dose-fraction. The slopes in these significant features were defined as delta-radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta-radiomics features for overall-survival, local-recurrence, and distant-metastases. Leave-one-out cross validation was used for model-fitting and patient predictions. Concordance indices(c-index) and p-values for the log-rank test with patients stratified at the median were calculated. Results: Approximately one-half of the 62 optimized features required no preprocessing, one-fourth required smoothing, and one-fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall-survival, the c-index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta-radiomics features. For distant-metastases, the c-index improved from 0.53 to 0.58, while for local-recurrence it did not improve. Patient stratification significantly improved (p-value<0.05) for overallsurvival and distant-metastases when delta-radiomics features were included. The delta-radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave-one-out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High-risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-D-207B-05: Intra-Tumor Partitioning and Texture Analysis of DCE-MRI Identifies Relevant Tumor Subregions to Predict Early Pathological Response of Breast Cancer to Neoadjuvant Chemotherapy

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Methodological Issues of Gamma-Ray and Neutrino Searches at the Yakutsk Array

The method used to perform searches for extensive air showers (EAS) generated by neutral particles, such as high-energy gamma rays and astrophysical neutrinos, and the results of such searches are presented. A comprehensive analysis of relevant data, including those on EAS electrons, muons, and Cherenkov light and their time responses in scintillation and Cherenkov detectors, is performed for this purpose. An upper limit on the gamma-ray flux at the energies of 3 × 10{sup 18}, 10{sup 19}, and 3 × 10{sup 19} eV is set. A multivariate analysis of EAS properties of neutrino-induced air showers does not reveal neutrino-induced showers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation

The multivariate adaptive regression spline (MARS) approach of Friedman and its Bayesian counterpart are effective approaches for the emulation of computer models. The traditional assumption of Gaussian errors limits the usefulness of MARS, and many popular alternatives, when dealing with stochastic computer models. Here, we propose a generalized Bayesian MARS (GBMARS) framework which admits the broad class of generalized hyperbolic distributions as the induced likelihood function. This allows us to develop tools for the emulation of stochastic simulators which are parsimonious, scalable, and interpretable and require minimal tuning, while providing powerful predictive and uncertainty quantification capabilities. GBMARS is capable of robust regression with t distributions, quantile regression with asymmetric Laplace distributions, and a general form of “Normal-Wald” regression in which the shape of the error distribution and the structure of the mean function are learned simultaneously. We demonstrate the effectiveness of GBMARS on various stochastic computer models, and we show that it compares favorably to several popular alternatives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0–1 M) and nitric acid (0.1–10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Quantification of Rare Earth Elements in the Parts Per Million Range: A Novel Approach in the Application of Laser-Induced Breakdown Spectroscopy

This work extends a previous percentage level concentration study of the optical emission spectra for six rare earth elements, europium (Eu), gadolinium (Gd), lanthanum (La), praseodymium (Pr), neodymium (Nd), and samarium (Sm), along with the transition metal, yttrium (Y) using laser-induced breakdown spectroscopy (LIBS). The concentration of these six rare earth elements and yttrium has been attempted for the first time systematically down to parts per million (ppm) concentration levels ranging from 30 to 300 ppm. In this study, the authors have developed multivariate models for each element capable of predicting concentration with acceptable to excellent levels of accuracy. Additionally, partial least squares regression coefficients were used to identify key spectral features able to be used in this lower concentration regime. This study has demonstrated that it is conceivable to quantify the six rare earth elements along with yttrium at low concentrations in the parts per million levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

rBahadur: efficient simulation of structured high-dimensional genotype data with applications to assortative mating

Existing methods for generating synthetic genotype data are ill-suited for replicating the effects of assortative mating (AM). We propose rb_dplr, a novel and computationally efficient algorithm for generating high-dimensional binary random variates that effectively recapitulates AM-induced genetic architectures using the Bahadur order-2 approximation of the multivariate Bernoulli distribution. The rBahadur R library is available through the Comprehensive R Archive Network at https://CRAN.R-project.org/package=rBahadur.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

09 BIOMASS FUELS↗
DOE OSTI2019

Breast Reconstruction in an Underserved Population: A Retrospective Study

Background: Breast reconstruction can help restore the shape and appearance of breasts after surgery. Studies have shown that minority and uninsured patients are less likely to receive breast reconstruction after mastectomy. Objective: We sought to determine if post-mastectomy reconstruction varied by patient ethnicity and insurance status in a medically underserved population. Methods: This was a retrospective study of mastectomy patients seen at Bellevue Hospital Center, a safety-net hospital in New York City, between January 2010 and December 2015. The Chi square test was used to compare patient characteristics versus type of reconstruction chosen and likelihood of reconstruction. Logistic regression was used to examine likelihood of reconstruction, controlling for patient insurance status, race, age, stage at presentation, and contralateral prophylactic mastectomy. Results: Of the 750 patients included in the database, 220 underwent mastectomy. Overall, 73.6% of our patient population received breast reconstruction. Patients with Medicare insurance were less likely to get reconstruction compared with patients with other types of insurance (37.5%, p = 0.04). Hispanic patients were most likely to receive reconstruction (89.1%), followed by Black patients (80%) and Asian patients (66.7%) [p = 0.03]. There were no significant associations between patient race or stage at presentation and type of reconstruction. In a multivariate logistic regression, advancing age was associated with a decreased likelihood of reconstruction (adjusted odds ratio 0.91, p < 0.001). Conclusions: In our underserved patient population, patients received breast reconstruction at rates higher than the national average. Institutional availability of patient navigators and preoperative counseling may contribute to more equal access to breast reconstruction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Impact of Preoperative Skeletal Muscle Mass and Nutritional Status on Short-and Long-Term Outcomes After Esophagectomy for Esophageal Cancer: A Retrospective Observational Study

Background: Preoperative muscle volume and body mass index (BMI) are associated with postoperative outcomes. Because esophagectomy for esophageal cancer (EC) is associated with high morbidity and because EC has a poor prognosis, this study investigated the association of preoperative skeletal muscle mass and nutritional status with postoperative outcomes. Methods: The study analyzed 483 patients who underwent esophagectomy from 2009 to 2012. The cross-sectional area of the psoas muscle index (PMI) was measured at the third lumbar vertebral level using computed tomography. Clavien–Dindo classifications were used to analyze postoperative complications. Because skeletal muscle mass varies according to sex, all analyses were performed accordingly (390 males, 93 females). Results: For male patients, BMI was a significant multivariate factor, and PMI, a univariate factor, predicted postoperative complications and overall survival (OS). Using a preoperative nutritional and muscular (PNM) score derived from BMI and PMI results (patients were allocated 1 point if their BMI was < 18.5 kg/m{sup 2} and 1 point if their PMI was < 600 mm{sup 2}/m{sup 2}, for a possible maximum total of 2 points), male patients were categorized as high risk (score 2), moderate risk (score 1), or low risk (score 0). In the low-risk group, anastomotic leakage was significantly less (p = 0.01), and the 3-year OS was significantly better (p < 0.01). On the other hand, in female patients, neither BMI nor PMI was a significant factor for postoperative outcomes. Conclusions: For male patients, the PNM score is a promising tool for predicting postoperative outcomes and identifying patients requiring preoperative nutritional intervention and rehabilitation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Influence of Neoadjuvant Therapy on Poor Long-Term Outcomes of Postoperative Complications in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Cohort Study

Background: Postoperative complications have a negative impact on overall survival after esophagectomy because systemic inflammation may induce residual cancer cell growth. A solution that could suppress micrometastasis is neoadjuvant therapy; however, to date, no study has shown that neoadjuvant therapy suppresses proliferation of cancer cells due to postoperative complications after esophagectomy. The aim of this study is to investigate the influence of neoadjuvant therapy on poor long-term outcomes of postoperative complications in patients with esophageal carcinoma. Patients and Methods: In total, 431 patients who underwent esophagectomy for esophageal squamous cell carcinoma were included in this study. We investigated the relationship between complications, such as pneumonia, and long-term oncologic outcomes with and without neoadjuvant therapy. Results: Among the patients, the 3-year overall survival (OS) rate was 69.5% and the disease-free survival (DFS) rate was 59.0%. The patients were categorized into two groups: the neoadjuvant therapy (+) group (n = 217) and neoadjuvant therapy (−) group (n = 214). Among patients not undergoing neoadjuvant therapy, patients with pneumonia or pyothorax had significantly poorer OS and DFS than patients without these complications. However, among patients undergoing neoadjuvant therapy, there were no significant differences in long-term outcomes, regardless of presence of complications. On multivariate analysis, pneumonia (p = 0.003), pyothorax (p < 0.001), and chylothorax (p = 0.002) were identified as predictors of death in the neoadjuvant therapy (−) group. Conclusion: The negative impact of postoperative complications on long-term prognoses can be reduced by performing neoadjuvant therapy in patients with esophageal carcinoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Clinical Importance of Sputum in the Respiratory Tract as a Predictive Marker of Postoperative Morbidity After Esophagectomy for Esophageal Cancer

Background: Respiratory morbidity is common after esophagectomy and can be a major cause of surgery-related mortality. Thus, it is important to identify novel predictors that can preoperatively estimate the incidence of postoperative respiratory morbidity. Asymptomatic sputum in the respiratory tract is sometimes observed on preoperative computed tomography (CT). This study aimed to determine the clinical importance of sputum in the respiratory tract as a predictor of postoperative morbidity after esophagectomy for esophageal cancer. Patients and Methods: The study included 609 consecutive patients who underwent three-incisional esophagectomy for esophageal cancer between April 2005 and November 2018. Results: Among the patients, 76 (12.5%) had sputum in the respiratory tract on preoperative CT. This finding was significantly associated with older age, more extreme smoking habit, worse performance status, lower forced expiratory volume 1%, and more frequent pulmonary comorbidities. Additionally, the incidence of postoperative pneumonia was higher in these patients than in those without sputum (16 vs 8%, p = 0.028). Sputum in the main bronchus was associated with higher frequencies of morbidity of Clavien–Dindo classification (CDc) ≥ II (p = 0.019), severe morbidity of CDc ≥ IIIb (p = 0.058), pneumonia (p = 0.10), and pulmonary morbidity (p = 0.19) compared with the finding of sputum in the trachea alone. On multivariate analysis, sputum in the respiratory tract was an independent risk factor (hazard ratio, 2.07; 95% confidence interval, 1.019–4.207; p = 0.044) for postoperative pneumonia. Conclusions: Sputum in the respiratory tract is a novel predictor of postesophagectomy pneumonia. Patients with sputum in the more distal respiratory tract might have high risk of postoperative morbidities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A machine learning-based fast frequency response control for a VSC-HVDC system

An HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation. However, most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control. Since the traditional system frequency control is dominated by the thermal generators, the advantage of the fast response of the HVDC system is not made fully used. The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system. Different from other frequency deviation tracking strategies, a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost. In this paper, a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression (MRFR) algorithm is proposed. The simulation is carried out with an integrated power system model based on the North American interconnections. The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event, while stabilizing the frequency in advance.

42 ENGINEERING↗
DOE OSTI2019

Cumulative Spatial Impact Layers™

Cumulative Spatial Impact Layers™ (CSIL) is a GIS-based tool that summarizes spatio-temporal datasets based on overlapping features and attributes. Applying a recursive quadtree method and multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets simultaneously by calculating data, record, or attribute density. The CSIL tool was designed based on the original approach (Bauer et al. 2015) and more information on the tool overall along with applications can be found in Romeo et al. 2019). Providing an efficient summary of disparate geospatial data, CSIL bridges the gap between understanding data and analysis. Bauer, J. R., Nelson, J., Romeo, L., Eynard, J., Sim, L., Halama, J., Rose, K., & Graham, J. (2015). A spatio-temporal approach to analyze broad risks and potential impacts associated with uncontrolled hydrocarbon release events in the offshore Gulf of Mexico (NETL-TRS-2-2015 EPAct Technical Report Series). U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV; p 60. Romeo, L., Nelson, J., Wingo, P., Bauer, J., Justman, D., & Rose, K. (2019). Cumulative spatial impact layers: A novel multivariate spatio-temporal analytical summarization tool. Transactions in GIS.

Multivariate Visualization↗
DOE OSTI2016

Studies of QCD structure in high-energy collisions

”Studies of QCD structure in high-energy collisions” is a research project in theoretical particle physics at Southern Methodist University funded by US DOE Award DE-SC0013681. The award furnished bridge funding for one year (2015/04/15-2016/03/31) between the periods funded by Nadolsky’s DOE Early Career Research Award DE-SC0003870 (in 2010-2015) and a DOE grant DE-SC0010129 for SMU Department of Physics (starting in April 2016). The primary objective of the research is to provide theoretical predictions for Run-2 of the CERN Large Hadron Collider (LHC). The LHC physics program relies on state-of-the-art predictions in the field of quantum chromodynamics. The main effort of our group went into the global analysis of parton distribution functions (PDFs) employed by the bulk of LHC computations. Parton distributions describe internal structure of protons during ultrarelivistic collisions. A new generation of CTEQ parton distribution functions (PDFs), CT14, was released in summer 2015 and quickly adopted by the HEP community. The new CT14 parametrizations of PDFs were obtained using benchmarked NNLO calculations and latest data from LHC and Tevatron experiments. The group developed advanced methods for the PDF analysis and estimation of uncertainties in LHC predictions associated with the PDFs. We invented and refined a new ’meta-parametrization’ technique that streamlines usage of PDFs in Higgs boson production and other numerous LHC processes, by combining PDFs from various groups using multivariate stochastic sampling. In 2015, the PDF4LHC working group recommended to LHC experimental collaborations to use ’meta-parametrizations’ as a standard technique for computing PDF uncertainties. Finally, to include new QCD processes into the global fits, our group worked on several (N)NNLO calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Pilot-Scale Pelleting Tests on High-Moisture Pine, Switchgrass, and Their Blends: Impact on Pellet Physical Properties, Chemical Composition, and Heating Values

In this study, we evaluated the pelleting characteristics of southern yellow pine (SYP), switchgrass (SG), and their blends for thermochemical conversion processes, such as pyrolysis and gasification. Using a pilot-scale ring-die pellet mill, we specifically assessed the impact of blend moisture, length-to-diameter (L/D) ratio in the pellet die, and ratio of pine to SG on the physico-chemical properties of the resulting pellets. We found that an increase in pine content by 25–50% marginally affected the bulk density; however, it also led to an increase in calorific value by 7% and a decrease in ash content by 72%. A moisture content of 25% (wet basis) and an L/D ratio of 5 resulted in poor pellet durability at <90% and bulk density values of <500 kg/m 3 , but increasing the L/D ratio to 9 and lowering the moisture content to 20% (w.b.) improved the pellet durability to >90% and the bulk density to >500 kg/m 3 . Blends with ≥50% pine content resulted in lower energy consumption, while a lower L/D ratio resulted in higher pelleting energy. Based on these findings, we successfully demonstrated the high-moisture pelleting of 2.5 ton of pine top residues blended with SG at 60:40 and 50:50 ratios. The quality of the pellets was monitored off-line and at-line by near infrared (NIR) spectroscopy. Multivariate models constructed by combining the NIR data and the pelleting process variables could successfully predict the pine content (R 2 = 0.99), higher heating value (R 2 = 0.98), ash (R 2 = 0.95), durability (R 2 = 0.94), and bulk density (R 2 = 0.86) of the pellets. Thus, we established how blending and densification of SYP and SG biomass could improve feedstock specifications and that NIR spectroscopy can effectively monitor the pellet properties during the high-moisture pelleting process.

09 BIOMASS FUELS↗
DOE OSTI2023

Quantifying neptunium oxidation states in nitric acid through spectroelectrochemistry and chemometrics

Controlled-potential in situ thin-layer spectropotentiometry was leveraged to generate visible/near-infrared (VIS/NIR) absorption spectral data sets for the development of chemometric models to quantify Np(III/IV/V/VI) oxidation states in HNO 3 . This technology would be valuable in laboratory studies and when monitoring process solutions to guide feed adjustments for radiochemical separations—the performance of which depends on oxidation state. This approach successfully isolated and stabilized Np species in pure (~99%) oxidation states without compromising solution optical properties. Multivariate curve resolution–alternating least squares models were evaluated to resolve spectral and component concentrations from a scan that sequentially produced Np(VI), Np(V), Np(IV), and Np(III) spectra with mixtures of two valences at a time. Although it provided a useful approximation, the method was not able to quantitively resolve each component likely because of rotational ambiguity. Additionally, partial least squares regression models were built from artificial and electrochemically generated VIS/NIR spectral training sets to study the effect of interionic interactions on spectral characteristics. Models built with true Bi-chemical mixtures of coexisting Np oxidation states and spectra generated from additive combinations of pure end points had similar prediction performance. This methodology can be used to directly quantify Np concentration and the ratio of Np oxidation states and other actinides in remote settings such as hot cells.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗