Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “environmental partitioning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Viral niche-partitioning: comparative genomics of giant viruses across environmental gradients in a high Arctic freshwater-saltwater lake

Giant viruses (GVs; Nucleocytoviricota) impact the biology and ecology of a wide range of eukaryotic hosts, with implications for global biogeochemical cycles. Here, we investigated GV niche separation in highly stratified Lake A at the northern coast of Ellesmere Island, Nunavut, Canada. This lake is composed of a layer of ice-covered freshwater that overlies saltwater derived from the ancient Arctic Ocean, and it therefore provides a broad gradient of environmental conditions and ecological habitats, each with a distinct protist community and rich assemblages of associated GVs. The upper layer (mixolimnion) had measurable light and oxygen, and contained diverse GVs linked to photosynthetic protists, indicating adaptation to surface biotic and abiotic conditions. In contrast, the saline lower layer (monimolimnion), lacking oxygen and light, hosted GVs associated with predicted heterotrophic protists, some of which are known for a predatory lifestyle, and with several viral genes suggesting adaptation to deep-water anaerobic conditions. Our observations underscore the coupling between physical and chemical gradients, microeukaryotes and their associated GVs in Lake A, and provide insight into the potential for GVs to directly and indirectly impact host metabolism. There were similarities between the genetic composition of GVs and the metabolic processes of their potential hosts, implying co-evolution and niche-adaptation within the lake habitats. Notably, we found a greater presence of viral rhodopsins in deeper water layers, suggesting an evolutionary relationship with potential hosts capable of supplementing their energetic needs to thrive in low energy, anoxic conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity

The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (K OW ) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc) 0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.

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

Evapotranspiration Partitioning Using Flux Tower Data in a Semi-Arid Ecosystem

Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2025

Senescence-driven solubilization of biomass is the main source of kelp-derived dissolved organic carbon to the coastal ocean

Abstract Kelp forests form some of the most productive areas on earth and are proposed to sequester carbon in the ocean, largely in the form of released dissolved organic carbon (DOC). Here we investigate the role of environmental, seasonal and age-related physiological gradients on the partitioning of net primary production (NPP) into DOC by the canopy forming giant kelp (Macrocystis pyrifera). Rates of DOC production were strongly influenced by an age-related decline in physiological condition (i.e. senescence). During the mature stage of giant kelp development, DOC production was a small and constant fraction of NPP regardless of tissue nitrogen content or light intensity. When giant kelp entered its senescent phase, DOC production increased substantially and was uncoupled from NPP and light intensity. Compositional analysis of giant kelp-derived DOC showed that elevated DOC production during senescence was due to the solubilization of biomass carbon, rather than by direct exudation. We coupled our incubation and physiological experiments to a novel satellite-derived 20-year time series of giant kelp canopy biomass and physiology. Annual DOC production by giant kelp varied due to differences in standing biomass between years, but on average, 74% of the annual DOC production by giant kelp was due to senescence. This study suggests DOC may be a more important fate of macroalgal NPP than previously recognized.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2025

Real-Time Measurements of Gas-Phase Medium-Chain Chlorinated Paraffins Reveal Daily Changes in Gas-Particle Partitioning Controlled by Ambient Temperature

Chlorinated paraffins (CPs) are synthetic polychlorinated n-alkanes produced as mixtures of a range of C x Cl y H 2x–y+2 formulas. CPs have numerous industrial applications but are toxic, long-lived, and environmentally ubiquitous with environmental releases occurring throughout their production, use, and disposal. Short-chain chlorinated paraffins (SCCPs, C 10–13 ) have been regulated by the United States Environmental Protection Agency since 2009 and by the Stockholm Convention since 2017. SCCP regulation is expected to cause increased production of medium-chain chlorinated paraffins (MCCPs; C 14–17 ), which are currently under consideration for Stockholm Convention regulations. Thus, there is a need to improve the understanding of MCCP environmental transport, distribution, and fate. Existing measurements are limited in their spatial and temporal coverage. Measurements of CP atmospheric loading are particularly scarce. Historically, these measurements have required long sampling times, obscuring the temporal behavior of atmospheric CPs. We report real-time in situ measurements of 18 gas-phase MCCPs. These measurements were made in the United States Southern Great Plains with nitrate ion chemical ionization mass spectrometry (NO 3 –CIMS). Here, the estimated average lower-limit concentration of MCCPs is on the order of single-digit ng/m 3 . MCCP diel behavior is partially explained by gas-particle partitioning with implications for MCCP transport and lifetimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗
DOE OSTI2025

Environmental life cycle of fentanyl: From the cradle to an unknown grave

The lack of available information on the presence and persistence of fentanyl in the environment is a significant gap in the technical literature. Although the origins of the opioid in the environment are well-known because they follow the same pathways of other drug-related environmental contaminants, the downstream effects of fentanyl in the water supply and its retention in soil are less understood. The characterization of fentanyl and its potential degradation products in complex environmental samples such as soil is severely understudied. Very few articles are available that work to identify fentanyl and its degradation products in complex samples or name the possible hazards that may result from environmental exposure and degradation. Therefore, the objectives were to identify available articles focused on environmental fentanyl and its pathways and highlight quantifiable research or results that included specific degradation products or downstream effects. Research articles focused on fentanyl between 2000 and 2024 were identified and reviewed and then filtered using Boolean search terms for environmental parameters. Various studies have determined that trace levels of fentanyl can be found in a variety of environments, and additional data suggest preferential partitioning into soils from water and long-term persistence. Despite this knowledge, very little data exists on the long-term downstream effects of fentanyl or its analogs. As the chronic effects from low-level fentanyl exposure are currently unknown, this lack of insight brings to the forefront the need for further research to improve our understanding of fentanyl persistence, degradation, and toxicity within the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

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

Seasonal changes in occupancy and activity patterns in native Collared Peccary and non-native wild pig and Common Warthog in southern Texas, the United States

Abstract Southern Texas, USA, is home to native collared peccaries (Dicotyles tajacu) and introduced populations of invasive wild pigs (generally Sus scrofa × domesticus hybrids) and non-native African warthogs (Phacochoerus africanus). Although these ecologically similar mammals co-occur in this region, the potential impacts of invasive suids on collared peccaries are poorly understood. We examined co-occupancy and activity pattern overlap of collared peccaries, wild pigs, and warthogs across 3 seasons (fall, winter, and spring) using remote camera data collected from Chaparral Wildlife Management Area, Cotulla, Texas (November 2020 to June 2021). Using activity pattern analyses and single and 2-species occupancy models incorporating habitat and climate characteristics, we found evidence of extensive spatial overlap between all species pairs and varying degrees of temporal overlap depending on species pairs and season. Collared peccaries and wild pigs displayed moderate temporal overlap across all seasons. Collared peccaries appeared to alter fall activity in the presence of wild pigs to be active earlier in the morning and less active during the day. Collared peccaries and warthogs had low-to-moderate temporal overlap (low in fall, winter; moderate in spring). Wild pigs and warthogs had low temporal overlap (fall, spring) and wild pigs appeared to alter activity in the presence of warthogs (fall), becoming less active during the day during peak warthog activity. Overall, our results suggest that peccary, wild pig, and warthog interactions and activity are dynamic and vary seasonally according to the ecology and preferred environmental conditions of each species. Given the potential for interspecific competition and disease transmission with Collared Peccary and other native wildlife, resource partitioning between wild pigs and warthogs needs to be further examined to aid in effective management strategies.

Kupferman, Caitlin A. (ORCID:0009000953554860)↗
DOE OSTI2026

Novel Zwitterionic Chromatography to Separate Lithium from Unconventional Resources

Since lithium (Li) is a key element for clean energy technologies, the global lithium demand is anticipated to increase rapidly. As a result, efficient lithium extraction technologies that allow the exploitation of unconventional lithium resources, such as geothermal brines and inland brine streams, are essentially needed to meet the Li demand and keep supply chains stable. However, because of low Li concentration, low ratio of Li/Mg or Li/Ca, and complex feed compositions in these feedstocks, using conventional hydrometallurgy or current Direct Lithium Extraction (DLE) processes are often chemical-intensive, requiring substantial amounts of reagent chemicals to recover Li at scale, resulting in large chemical footprints. Therefore, we developed a novel Zwitterionic Chromatography (ZIC) process to separate Li from these resources. Because Li can be partitioned from divalent salts on ZIC under water elution without using chemicals, exploiting the unconventional resources via a ZIC approach can be environmentally benign and sustainable. In this work, we will present the development of the ZIC process for DLE applications. Simulation studies for the Li separation mechanism in ZIC and the demonstration of a continuous ZIC process to separate Li from domestic inland brine and mining wastewater will be presented. Finally, the chemical and energy footprints of ZIC in comparison to other DLE processes will be discussed.

09 BIOMASS FUELS↗
DOE OSTI2026

Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems

Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.

bioenergy systems↗
DOE OSTI2025

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.

PubChem↗
DOE OSTI2025

Uncertainty quantification for nuclear forensics with population analyses

Although neural networks offer cutting-edge predictive power, their deployment in high-consequence nuclear forensic applications is limited, partly because of their black-box nature. Incorporating robust uncertainty quantification methods into the predictive frameworks of neural networks is progress towards their future deployment in such scenarios. This work integrates uncertainty quantification into neural networks for nuclear reactor core-average burnup estimation from simulated environmental samples. We test two regimes (homogeneous and heterogeneous events) on DeepSets and Set Transformer architectures, we find both quantify predictive uncertainty effectively, but Set Transformer excels in partitioning latent events, offering superior predictive power and more informative uncertainty estimates.

Hatton, Conner [ORNL] (ORCID:0009000804970959)↗
DOE OSTI2025

The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis

In the future, plants may encounter increased light and elevated CO 2 levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light ( R L ) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and R L under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring R L under high light and high CO 2 (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate RL and investigate photosynthetic carbon flux, unveiling nuanced adjustments in Camelina sativa under HLHC. Despite numerous flux alterations in HLHC, RL remained stable. HLHC affects several factors influencing RL, such as starch and sucrose partitioning, v o /v c ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of A/C i curve operational points reveals that HLHC’s major changes primarily stem from CO 2 suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during R L , offering insights into manipulating labeling through adjustments in photosynthetic rates.

Elevated CO2↗
DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effect of iodides on thermal behavior and phase partitioning in LiCl-KCl

Liquid-fueled molten salt reactors (MSRs) are designed to operate with fissile materials and, ultimately, fission products dissolved in the primary molten salt coolant. Understanding the speciation and transport of iodine—a high-yield fission product—is essential because this element’s accidental release poses significant environmental concerns due to its capacity to be readily absorbed by the human thyroid gland. Here, we report the impact of iodide species (LiI and KI) on phase transitions, phase distribution, and phase stability in LiCl–KCl-eutectic salt mixtures. The study employed a combination of computational and experimental techniques, including thermodynamic FactSage calculations, differential scanning calorimetry, and high-temperature X-ray diffraction. The results indicate that the presence of iodide (10–25 wt%) significantly alters the melting behavior of the LiCl–KCleutectic system. Adding 10 wt% LiI has a more-pronounced effect than 10 wt% KI, as LiI converts to KI, leading to formation of LiCl, thereby, altering the LiCl-KCl ratio which significantly affects the melting temperature of the mixture. Furthermore, the evolution of crystalline structure, solid-fraction composition, and the dynamics of mixed-halide solid–liquid partitioning as a function of temperature indicate the potential for selective iodide separation from chloride-salt mixtures via solid–liquid separation techniques. Overall, the presented findings provide valuable insights that are beneficial for the design and operation of MSRs, as well as for the safe handling and effective processing of used nuclear fuel using advanced pyrochemical techniques.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗