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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluating the X2 initial core zero power physics tests with Serpent-Ants

The validation of the Ants nodal neutronics code for VVER applications is started by modelling the zero power physics tests for the initial core of the Khmelnitsky 2 nuclear power plant as described in the X2 benchmark using the Serpent-Ants two step neutronics calculation chain. The Ants prediction compare favorably against an earlier continuous energy Monte-Carlo reference solution as well as the measured data, except for discrepancies in the SCRAM worth between predicted and measured values. Similar discrepancies have been previously reported for VVER-1000 reactors, and the Ants predicted SCRAM worths match well with the Serpent results. Additionally, the assembly power distribution and axial power distribution predicted by Ants for the critical HZP state of the reactor are also compared to a reference Serpent solution with very good results. Lastly, an investigation into the effects of the few-group structure used in the nodal calculations and the use or lack of leakage correction for group constants on the results shows that the best accuracy is reached with seven or eight energy groups without leakage correction, although a reasonable accuracy can also be obtained with two or three energy groups using fundamental mode leakage correction. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

BioSecure Digital Twin: Manufacturing Innovation and Cybersecurity Resilience

U.S. national security, prosperity, economy, and well-being require secure, flexible, and resilient Biopharmaceutical Manufacturing. The COVID-19 pandemic reaffirmed that the biomedical production value-chain is vulnerable to disruption and has been under attack from sophisticated nation-state adversaries. Current cyber defenses are inadequate, and the integrity of critical production systems and processes are inherently vulnerable to cyber-attacks, human error, and supply chain disruptions. The following chapter explores how a BioSecure Digital Twin will improve U.S. manufacturing resilience and preparedness to respond to these hazards by significantly improving monitoring, integrity, security, and agility of our manufacturing infrastructure and systems. The BioSecure Digital Twin combines a scalable manufacturing framework with a robust platform for monitoring and control to increase U.S. biopharma manufacturing resilience. Then, the chapter discusses some of the inherent vulnerabilities and challenges at the nexus of health and advanced manufacturing. Next, the chapter highlights that as the Pandemic evolves, we need agility and resilience to overcome significant obstacles. This section highlights an innovative application of Cyber Informed Engineering to developing and deploying a BioSecure Digital Twin to improve the resilience and security of the biopharma industrial supply chain and production processes. Finally, the chapter concludes with a process framework to complement the Digital Twin platform, called the Biopharma (Observe, Orient, Decide, Act) OODA Loop Framework (BOLF), a four-step approach to decision-making outputs from the Digital Twin. The BOLF will help end users leverage twin technology by distilling the available information, focusing the data on context, and rapidly making the best decision while remaining cognizant of changes that can be made as more data becomes available.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

The pesticide chlorpyrifos promotes obesity by inhibiting diet-induced thermogenesis in brown adipose tissue

Obesity results from a caloric imbalance between energy intake, absorption and expenditure. In both rodents and humans, diet-induced thermogenesis contributes to energy expenditure and involves the activation of brown adipose tissue (BAT). We hypothesize that environmental toxicants commonly used as food additives or pesticides might reduce BAT thermogenesis through suppression of uncoupling protein 1 (UCP1) and this may contribute to the development of obesity. Using a step-wise screening approach, we discover that the organophosphate insecticide chlorpyrifos suppresses UCP1 and mitochondrial respiration in BAT at concentrations as low as 1 pM. In mice housed at thermoneutrality and fed a high-fat diet, chlorpyrifos impairs BAT mitochondrial function and diet-induced thermogenesis, promoting greater obesity, non-alcoholic fatty liver disease (NAFLD) and insulin resistance. This is associated with reductions in cAMP; activation of p38MAPK and AMPK; protein kinases critical for maintaining UCP1 and mitophagy, respectively in BAT. These data indicate that the commonly used pesticide chlorpyrifos, suppresses diet-induced thermogenesis and the activation of BAT, suggesting its use may contribute to the obesity epidemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants

Abstract Root nodule symbiosis (RNS) is a complex trait that enables plants to access atmospheric nitrogen converted into usable forms through a mutualistic relationship with soil bacteria. Pinpointing the evolutionary origins of RNS is critical for understanding its genetic basis, but building this evolutionary context is complicated by data limitations and the intermittent presence of RNS in a single clade of ca. 30,000 species of flowering plants, i.e., the nitrogen-fixing clade (NFC). We developed the most extensive de novo phylogeny for the NFC and an RNS trait database to reconstruct the evolution of RNS. Our analysis identifies evolutionary rate heterogeneity associated with a two-step process: An ancestral precursor state transitioned to a more labile state from which RNS was rapidly gained at multiple points in the NFC. We illustrate how a two-step process could explain multiple independent gains and losses of RNS, contrary to recent hypotheses suggesting one gain and numerous losses, and suggest a broader phylogenetic and genetic scope may be required for genome-phenome mapping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evidence of a liquid–liquid transition in a glass-forming ionic liquid

A liquid–liquid transition (LLT) is a transformation from one liquid to another through a first-order transition. The LLT is fundamental to the understanding of the liquid state and has been reported in a few materials such as silicon, phosphorus, triphenyl phosphite, and water. Furthermore, it has been suggested that the unique properties of materials such as water, which is critical for life on the planet, are linked to the existence of the LLT. However, the experimental evidence for the existence of an LLT in many molecular liquids remains controversial, due to the prevalence and high propensity of the materials to crystallize. In this work, we show evidence of an LLT in a glass-forming trihexyltetradecylphosphonium borohydride ionic liquid that shows no tendency to crystallize under normal laboratory conditions. We observe a step-like increase in the static dielectric permittivity at the transition. Furthermore, the sizes of nonpolar local domains and ion-coordination numbers deduced from wide-angle X-ray scattering also change abruptly at the LLT. We independently corroborate these changes in local organization using Raman spectroscopy. The experimental access to the evolution of local order and structural dynamics across a liquid–liquid transition opens up unprecedented possibilities to understand the nature of the liquid state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantum Gravity and Laser Interferometry: Towards Observable Predictions

Understanding quantum gravity remains one of the deepest challenges in modern physics, as direct experimental access to Planck-scale effects is beyond current technological reach. However, recent theoretical advances indicate that quantum fluctuations of spacetime may produce measurable effects in precision experiments, particularly near causal horizons. This opens new avenues for testing quantum gravity phenomena through high-precision measurement techniques. This dissertation develops multiple theoretical models to characterize these effects and examines their potential observational signatures in future gravitational wave interferometers. We begin by investigating the role of quantum fluctuations in near-horizon geometries through the lens of the AdS/CFT correspondence, which provides a powerful framework for understanding the interplay between quantum field theory and general relativity via holographic principles. By modeling stochastic energy-momentum sources in Rindler-AdS spacetime, we demonstrate that vacuum fluctuations transform the Einstein equations into a Langevin-type stochastic differential equation, leading to potentially observable fluctuations in photon traversal times. Extending this approach to Minkowski spacetime, we establish a correspondence between gravitational shockwaves and fluid dynamics, showing that near-horizon perturbations satisfy an equation analogous to that governing incompressible fluids, thereby reinforcing the membrane paradigm and hydrodynamic analogies in the context of the fluid/gravity duality. Furthermore, we construct the covariant phase space of a spherically symmetric causal diamond in Minkowski spacetime, identifying two fundamental charges that govern its evolution. These results provide a foundation for quantizing causal horizons and understanding their microscopic degrees of freedom. Building upon these theoretical developments, we further examine a related stochastic phenomenon: the gravitational wave memory background arising from the cumulative memory steps produced by supermassive black hole mergers. After reviewing the standard stochastic gravitational wave background, gravitational memory effects, and BMS symmetries, we model the stochastic memory background using a Brownian motion framework. We show that while the cumulative memory background initially appears above the sensitivity curve of space-based interferometers like LISA, the realistic subtraction of individually resolvable merger events substantially suppresses the residual signal, making its detection more challenging. This highlights the critical importance of source subtraction when evaluating the detectability of gravitational memory effects. By bridging fundamental theory with experimental prospects, this dissertation contributes to the ongoing effort to uncover the quantum nature of spacetime through precision measurement techniques. Whether through detecting quantum spacetime fluctuations, gravitational memory backgrounds, or probing the symmetries of causal horizons, the pursuit of observable quantum gravity phenomena continues to expand the frontiers of both theory and experiment.

Zhang, Yiwen [Caltech] (ORCID:0000000323559416)↗
DOE OSTI2021

Alcator C-Mod: Research in support of ITER and steps beyond

This paper presents an overview of recent highlights from research on Alcator C-Mod. Significant progress has been made across all research areas over the last two years, with particular emphasis on divertor physics and power handling, plasma-material-interaction studies, ELM-suppressed pedestal dynamics, core transport and turbulence, and RF heating and current drive utilizing Ion Cyclotron and Lower Hybrid tools. Specific results of particular relevance to ITER include: inner wall SOL transport studies that have led, together with results from other experiments, to the change of the detailed shape of the inner wall in ITER; runaway electron studies showing that the critical electric field required for runaway generation is much higher than predicted from collisional theory; core tungsten impurity transport studies reveal that tungsten accumulation is naturally avoided in typical C-Mod conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Extracting Critical Metals from Authentic Bauxite Residue

Securing domestic sources of critical minerals (CM) is paramount to ensuring a robust and self-sustaining technology infrastructure. Utilizing untapped non-conventional sources, like acid mine drainage, coal ash, bauxite residue/red mud, and more is an emerging approach that addresses this national concern while identifying new value-added pathways originating from waste streams. Red mud is a byproduct of the Bayer process that produces alumina and contains a wealth of CM – 50 µg/g Ga, 2.9 mg/g total rare earth elements plus yttrium, 12 mg/g Mn, 58 mg/g Al, and others. About 170 MM tonnes of red mud are co-produced annually alongside the 142 MM tonnes of alumina generated, highlighting the abundancy of this feedstock. In this work, different strategies were explored for extracting CM from authentic bauxite residue that involve thermal heating, microwave heating, and sonication all with different acids and buffers. CM recovery was then explored one step further by testing the adsorption of the extracted metals onto NETL’s Multi-functional Sorbent Technology (MUST). Microwave treatment of red mud proved the most effective CM extraction, whereas sonication was less desirable due to scale-up concerns. Successful recovery of CM from this leachate with MUST supports further studies toward optimizing the overall process.

bauxite residue↗
DOE OSTI2020

Kirigami Engineering—Nanoscale Structures Exhibiting a Range of Controllable 3D Configurations

Abstract Kirigami structures provide a promising approach to transform flat films into 3D complex structures that are difficult to achieve by conventional fabrication approaches. By designing the cutting geometry, it is shown that distinct buckling‐induced out‐of‐plane configurations can be obtained, separated by a sharp transition characterized by a critical geometric dimension of the structures. In situ electron microscopy experiments reveal the effect of the ratio between the in‐plane cut size and film thickness on out‐of‐plane configurations. Moreover, geometrically nonlinear finite element analyses (FEA) accurately predict the out‐of‐plane modes measured experimentally, their transition as a function of cut geometry, and provide the stress–strain response of the kirigami structures. The combined computational–experimental approach and results reported here represent a step forward in the characterization of thin films experiencing buckling‐induced out‐of‐plane shape transformations and provide a path to control 3D configurations of micro‐ and nanoscale buckling‐induced kirigami structures. The out‐of‐plane configurations promise great utility in the creation of micro‐ and nanoscale systems that can harness such structural behavior, such as optical scanning micromirrors, novel actuators, and nanorobotics. This work is of particular significance as the kirigami dimensions approach the sub‐micrometer scale which is challenging to achieve with conventional micro‐electromechanical system technologies.

Zhang, Xu↗
DOE OSTI2026

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Calibrating Microscopic Car-Following Models for Adaptive Cruise Control Vehicles: Multiobjective Approach

Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Evidence for autotrophic growth of purple sulfur bacteria using pyrite as electron and sulfur source

ABSTRACT Purple sulfur bacteria (PSB) are capable of anoxygenic photosynthesis via oxidizing reduced sulfur compounds and are considered key drivers of the sulfur cycle in a range of anoxic environments. In this study, we show that Allochromatium vinosum (a PSB species) is capable of autotrophic growth using pyrite as the electron and sulfur source. Comparative growth profile, substrate characterization, and transcriptomic sequencing data provided valuable insight into the molecular mechanisms underlying the bacterial utilization of pyrite and autotrophic growth. Specifically, the pyrite-supported cell cultures (“py”’) demonstrated robust but much slower growth rates and distinct patterns from their sodium sulfide-amended positive controls. Up to ~200-fold upregulation of genes encoding various c - and b -type cytochromes was observed in “py,” pointing to the high relevance of these molecules in scavenging and relaying electrons from pyrite to cytoplasmic metabolisms. Conversely, extensive downregulation of genes related to LH and RC complex components indicates that the electron source may have direct control over the bacterial cells’ photosynthetic activity. In terms of sulfur metabolism, genes encoding periplasmic or membrane-bound proteins (e.g., FccAB and SoxYZ) were largely upregulated, whereas those encoding cytoplasmic proteins (e.g., Dsr and Apr groups) are extensively suppressed. Other notable differentially expressed genes are related to flagella/fimbriae/pilin(+), metal efflux(+), ferrienterochelin(−), and [NiFe] hydrogenases(+). Characterization of the biologically reacted pyrite indicates the presence of polymeric sulfur. These results have, for the first time, put the interplay of PSB and transition metal sulfide chemistry under the spotlight, with the potential to advance multiple fields, including metal and sulfur biogeochemistry, bacterial extracellular electron transfer, and artificial photosynthesis. IMPORTANCE Microbial utilization of solid-phase substrates constitutes a critical area of focus in environmental microbiology, offering valuable insights into microbial metabolic processes and adaptability. Recent advancements in this field have profoundly deepened our knowledge of microbial physiology pertinent to these scenarios and spurred innovations in biosynthesis and energy production. Furthermore, research into interactions between microbes and solid-phase substrates has directly linked microbial activities to the surrounding mineralogical environments, thereby enhancing our understanding of the relevant biogeochemical cycles. Our study represents a significant step forward in this field by demonstrating, for the first time, the autotrophic growth of purple sulfur bacteria using insoluble pyrite (FeS2) as both the electron and sulfur source. The presented comparative growth profiles, substrate characterizations, and transcriptomic sequencing data shed light on the relationships between electron donor types, photosynthetic reaction center activities, and potential extracellular electron transfer in these organisms capable of anoxygenic photosynthesis. Furthermore, the findings of our study may provide new insights into early-Earth biogeochemical evolutions, offering valuable constraints for understanding the environmental conditions and microbial processes that shaped our planet’s history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2020

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initio molecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car–Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exx algorithm implemented in the open-source Quantum ESPRESSO program, which employs a hybrid MPI/OpenMP parallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. Furthermore, this is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exx enables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500–1000 atoms (e.g., (H₂O)₂₅₆) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exx takes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

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

Matrix Approach to Land Carbon Cycle Modeling

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin-up of land carbon cycle models by tens of times. The accelerated spin-up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever-increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solvent-based plastic recycling technologies

Solvent-based recycling approaches are receiving industrial and academic interest for their ability to produce high-quality plastic resins from a variety of plastic waste sources without breaking the polymer chains. Here we highlight the development of solvent-based technologies, focusing on the underlying principles, techno-economic and life-cycle analyses, and commercialization. The basic steps in solvent-based recycling include plastic size reduction, plastic dissolution, filtration or centrifugation, and optional additional cleaning steps such as adsorption, precipitation and solvent removal. Impurities that build up in the solvent must also be removed. The goal of solvent-based technologies is to produce a high-quality resin without plastic contaminants or other added substances. Disadvantages of these solvent-based technologies are their physicochemical complexity and the difficulty in scaling up to achieve continuous operations with high polymer and solvent yields and throughputs. As a result, chemical engineering is thus critical in bringing solvent-based recycling technologies to market.

Chemical engineering↗
DOE OSTI2023

Producing Evaluation-quality 239 Pu Average Prompt Fission Neutron Multiplicities using a Correlated Fission Model

An evaluation of the average prompt fission neutron multiplicity, $\bar{ν}_p$, of 239 Pu(n,f) is shown. This evaluation includes (a) the correlated fission model CGMF, and (b) a detailed analysis of past and recently published experimental data. Using CGMF-calculated $\bar{ν}_p$ as prior enables to link, through the use of evaluated model input parameters, $\bar{ν}_p$ to other fission observables such as the prompt fission neutron spectrum (PFNS), preneutron emission fission yields as a function of mass, and the average total kinetic energy of the fragments. These evaluated parameters produce realistic predictions of many fission observables, while the evaluated $\bar{ν}_p$ agrees well (χ 2 ≈ 1) with data. Moreover, with the new evaluated $\bar{ν}_p$, the effective neutron multiplication factor of fast Pu ICSBEP critical assemblies are predicted with a mean bias of 58 pcm compared to 18 pcm with ENDF/B-VIII.0, when paired with a new 239 Pu PFNS and fission cross section. Due to these encouraging validation results, the evaluated $\bar{ν}_p$ is currently part of a release candidate for the 239 Pu ENDF/B-VIII.1 file. Hence, a correlated fission model was used for the first time for evaluating $\bar{ν}_p$ that is of evaluation quality. This is an important step towards consistent evaluations of prompt fission observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗