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DOE OSTI2024

An interaction network in the polymerase active site is a prerequisite for Watson-Crick base pairing in Pol γ

The replication accuracy of DNA polymerase gamma (Pol γ) is essential for mitochondrial genome integrity. Mutation of human Pol γ arginine-853 has been linked to neurological diseases. Although not a catalytic residue, Pol γ arginine-853 mutants are void of polymerase activity. To identify the structural basis for the disease, we determined a crystal structure of the Pol γ mutant ternary complex with correct incoming nucleotide 2'-deoxycytidine 5'-triphosphate (dCTP). Opposite to the wild type that undergoes open-to-closed conformational changes when bound to a correct nucleotide that is essential for forming a catalytically competent active site, the mutant complex failed to undergo the conformational change, and the dCTP did not base pair with its Watson-Crick complementary templating residue. Our studies revealed that arginine-853 coordinates an interaction network that aligns the 3'-end of primer and dCTP with the catalytic residues. Disruption of the network precludes the formation of Watson-Crick base pairing and closing of the active site, resulting in an inactive polymerase.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A split luciferase system for studying coronavirus Mpro dimerization in vitro and in living cells

The main protease enzyme (Mpro) of coronaviruses cleaves the viral polyprotein into functional units essential for virus replication. Prior work has demonstrated that Mpro functions as a homodimer. However, studies on the mechanism of dimerization have been challenging because the purified protease is mostly dimeric, dimerization-defective mutants lack proteolytic activity, and robust cell-based assays have yet to be reported. To enable work on Mpro dimerization, we have developed a quantitative luciferase-based SARS-CoV-2 (SARS2) Mpro biosensor that accurately reports protein dimerization in living cells and, upon purification, also in vitro. Co-transfection of cells with a construct expressing Mpro fused to the 18 kDa LargeBiT of luciferase (LgBiT) and a second construct with Mpro fused to the 1 kDa SmallBiT of luciferase (SmBiT) results in a reconstitution of luciferase activity in a dose-dependent manner that requires conserved residues within the dimerization interface. Proteolytic activity is dispensable for dimerization and, uniquely, a C145A catalytically inactive mutant exhibits enhanced dimerization signal likely due to lower cytotoxicity. Mpro enzymes from multiple different coronaviruses also dimerize in this system, indicating mechanistic conservation. Interestingly, this dimerization biosensor also provides a quantitative read-out of inhibitor-facilitated dimerization. Covalent SARS2 Mpro inhibitors such as nirmatrelvir cause a 3- to 5-fold increase in luciferase activity. Together with corroborating structural, biophysical, and molecular dynamics experiments, our studies support a model in which covalent Mpro inhibitors such as nirmatrelvir simultaneously block catalytic activity and induce allosteric stabilization of the dimeric complex.

SARS-CoV-2 main protease (Mpro/3CLpro)↗

DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Nichol, J. Jake [Univ. of New Mexico, Albuquerque,↗

DOE OSTI2024

Autonomous hybrid optimization of a SiO 2 plasma etching mechanism

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO 2 using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Measurements of the inert Hugoniot and observation of mechanical ignition in Ni(V) + Al reactive multilayers via x-ray diffraction

This study investigates the inert Hugoniot response, mechanical ignition, and reaction dynamics of Ni(V)+Al multilayers during longitudinal, laser-driven shock compression experiments. Ni(V)+Al multilayers, known for their self-propagating exothermic reactions, were subjected to longitudinal stresses exceeding 50 GPa using the laser shock facility within the Dynamic Compression Sector (DCS) at the Advanced Photon Source (APS). In situ x-ray diffraction (XRD) revealed that Ni(V) and Al were not in equilibrium during compression, with stress discrepancies attributed to twinning, grain structure effects, and/or dislocation density. However, the measured inert Hugoniot closely matched prior experimental and computational studies, confirming the utility of XRD for measuring the equation of state of thin, complex materials. Additionally, reaction was observed at significantly higher stresses than reported previously using laser-launched flyers. This discrepancy suggests a strong influence of externally imposed shear stress on reaction thresholds, which likely arose from deviations in flyer planarity during past experiments. Full reaction of the multilayer occurred within 40 ns after shock-wave passage, evidenced by complete melting of the constituents. Eulerian hydrocode simulations replicated experimental conditions, providing insights into equilibrium dynamics and experimental artifacts. The results highlight how even small shear forces facilitate ignition in Ni(V)+Al multilayers at lower stresses.

Crystallography↗

DOE OSTI2024

Gold–Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential

Monolayer protected metal clusters comprise a rich class of molecular systems and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthesized and characterized in crystalline forms, their dynamical behavior in solution, including prenucleation cluster formation, is not well understood due to limitations both in characterization and first-principles modeling techniques. Recent advancements in machine-learned interatomic potentials are rapidly enabling the study of complex interactions such as dynamical behavior and reactivity on the nanoscale. Here, we develop an Au-S-C-H atomic cluster expansion (ACE) interatomic potential for efficient and accurate molecular dynamics simulations of thiolate-protected gold nanoclusters (Au n (SCH 3 ) m ). Trained on more than 30,000 density functional theory calculations of gold nanoclusters, the interatomic potential exhibits ab initio level accuracy in energies and forces and replicates nanocluster dynamics including thermal vibration and chiral inversion. Long dynamics simulations (up to 0.1 μs time scale) reveal a mechanism explaining the thermal instability of neutral Au 25 (SR) 18 clusters. Specifically, we observe multiple stages of isomerization of the Au 25 (SR) 18 cluster, including a chiral isomer. Additionally, we simulate coalescence of two Au 25 (SR) 18 clusters and observe series of clusters where the formation mechanisms are critically mediated by ligand exchange in the form of [Au-S] n rings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗

DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Paul, Lazlo↗

DOE OSTI2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymer brushes are well-known for their exceptional resistance to unwanted protein adsorption. While experimental studies have extensively characterized protein–polymer brush interactions, computational investigations remain limited, largely due to the challenges in accurately modeling and integrating these complex, multicomponent systems without resorting to oversimplification. To address this challenge, this study presents one of the most comprehensive and realistic model systems to date, comprising the substrate, grafted polymers, and proteins. Our work interprets the interactions between polymer brush and protein based on realistic modeling without simplification. In particular, this study utilizes molecular modeling and simulation of polycationic and polyzwitterionic brushes─poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)─grafted onto α-quartz substrates via polymerization initiator linkers. The brush models were developed to closely replicate experimentally synthesized samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. Cα root-mean-square deviation and interaction pattern analyses further reveal that the PSBMA brush also induces the most significant destabilization of lysozyme, while the PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush, primarily due to electrostatic repulsion caused by a short carbon spacer length. Hydration analysis reveals that both the PSBMA brush and the lysozyme interacting with it exhibit the most rapid dehydration, attributed to the brush’s high local chain density, which results in the greatest lysozyme destabilization and the highest adsorption force. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics. In conclusion, this study provides valuable insights for the rational design of next-generation antifouling materials and offers a framework for realistic model development in complex multicomponent systems.

Adsorption↗

DOE OSTI2025

Structure of coxsackievirus cloverleaf RNA and 3C pro dimer establishes the RNA-binding mechanism of enterovirus protease 3C pro

In positive-strand RNA viruses, the genome serves as a template for both protein translation and negative-strand RNA synthesis. Enteroviruses use the cloverleaf RNA structure at the 5′ end of the genome to balance these two processes. Cloverleaf acts as a promoter for RNA synthesis and forms a complex with viral 3CD protein, the precursor to 3C pro protease, and 3D pol polymerase. The interaction between cloverleaf and 3CD is mediated by the 3C pro domain, yet how 3C pro promotes specific RNA-binding is not clear. We report the structure of coxsackievirus cloverleaf RNA-3C pro complex, wherein two 3C pro molecules interact with cloverleaf stem-loop D. 3C pro dimer mainly recognizes the shape of the dsRNA helix through symmetric interactions, suggesting that 3C pro is a previously undiscovered type of RNA binding protein. We show that 3CD protein also dimerizes on cloverleaf RNA and binds the RNA with higher affinity than 3C pro . The structure provides insight into the RNA-binding mechanism of 3C pro or 3CD with other cis-acting replication elements.

Science & Technology - Other Topics↗

DOE OSTI2025

Influence of Steric and Electronic Properties of P2 Groups on Covalent Inhibitor Binding to SARS-CoV-2 Main Protease

The main protease (MPro) of SARS-CoV-2 is a critical enzyme required for viral replication, making it a prime target for antiviral drug development. Covalent inhibitors, which form a stable interaction with the catalytic C145, have demonstrated strong inhibition of MPro, but the influence of steric and electronic properties of P2 substituents, designed to engage the S2 substrate-binding subsite within the MPro active site, on inhibitor binding affinity remains underexplored. Here, in this study, we design and characterize two hybrid covalent inhibitors, BBH-3 and BBH-4, and present their X-ray crystallographic structures in complex with MPro, providing molecular insights into how their distinct P2 groups, a dichlorobenzyl moiety in BBH-3 and an adamantyl substituent in BBH-4, affect binding conformation and active site adaptability. Comparative structural analyses with previously characterized inhibitors, including BBH-2 and Mcule-5948770040, reveal how the P2 bulkiness and electronic properties influence active site dynamics, particularly through interactions with the S2 and S5 subsites. The P2 group of BBH-3 induces conformational shifts in the S2 helix and the S5 loop, while BBH-4 displaces M49, stabilizing its binding through hydrophobic interactions. Isothermal titration calorimetry further elucidates the impact of P2 modifications on inhibitor affinity, revealing a delicate balance between enthalpic and entropic contributions. The data demonstrate that BBH-3 exhibits less favorable binding, affirming that dichlorobenzyl substitution at the P2 position has a more negative impact on the affinity for MPro than bulky saturated cyclic groups. This underscores the feature that MPro active site malleability may be accompanied by a conformational strain.

SARS-CoV-2↗

DOE OSTI2024

The kinetics of SARS-CoV-2 infection based on a human challenge study

Studying the early events that occur after viral infection in humans is difficult unless one intentionally infects volunteers in a human challenge study. Here, we use data about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in such a study in combination with mathematical modeling to gain insights into the relationship between the amount of virus in the upper respiratory tract and the immune response it generates. We propose a set of dynamic models of increasing complexity to dissect the roles of target cell limitation, innate immunity, and adaptive immunity in determining the observed viral kinetics. We introduce an approach for modeling the effect of humoral immunity that describes a decline in infectious virus after immune activation. We fit our models to viral load and infectious titer data from all the untreated infected participants in the study simultaneously. We found that a power-law with a power h < 1 describes the relationship between infectious virus and viral load. Viral replication at the early stage of infection is rapid, with a doubling time of ~2 h for viral RNA and ~3 h for infectious virus. We estimate that adaptive immunity is initiated ~7 to 10 d postinfection and appears to contribute to a multiphasic viral decline experienced by some participants; the viral rebound experienced by other participants is consistent with a decline in the interferon response. Altogether, we quantified the kinetics of SARS-CoV-2 infection, shedding light on the early dynamics of the virus and the potential role of innate and adaptive immunity in promoting viral decline during infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Enhanced chloroplast FtsZ-ring constriction by the ARC6–ARC3 module in Arabidopsis

Chloroplast division, a process tightly linked to the energy demands of plants, is initiated by the formation of the stromal filamenting temperature-sensitive Z (FtsZ) ring. The Z ring is highly dynamic, and its constriction provides the essential force for chloroplast division. However, the regulatory mechanisms governing Z-ring dynamics and constriction remain poorly understood. Here, we report that the chloroplast inner envelope membrane (IEM) protein ACCUMULATION AND REPLICATION OF CHLOROPLASTS6 (ARC6) interacts with the chloroplast stromal protein ARC3, and this interaction is negatively regulated by the conserved J-like domain of ARC6. ARC3 is found both distributed throughout the stroma and localized to a ring-like structure at the chloroplast division site. We demonstrate that ARC6 recruits ARC3 to the division site to form a ring-like structure, likely through direct interaction. This ARC6–ARC3 interaction enables ARC3 to bind FtsZs. Furthermore, we show that the ARC6–ARC3 complex significantly promotes the dynamics of chloroplast Z rings reconstituted in a heterologous system. Finally, the constriction of these reconstituted Z rings is markedly enhanced by ARC6–ARC3. Our findings reveal a regulatory mechanism that governs Z-ring dynamics and constriction, shedding light on the molecular mechanisms underlying chloroplast division.

Science & Technology - Other Topics↗

DOE OSTI2025

Conformational flexibility is a critical factor in designing broad-spectrum human norovirus protease inhibitors

Human norovirus (HuNoV) is a leading cause of gastroenteritis worldwide and is associated with significant morbidity, mortality, and economic impact. There are currently no licensed antiviral drugs for the treatment of HuNoV-associated gastroenteritis. The HuNoV protease plays a critical role in the initiation of virus replication by cleaving the polyprotein. Thus, it is an ideal target for developing antiviral small-molecule inhibitors. While rupintrivir, a potent small-molecule inhibitor of several picornavirus proteases, effectively inhibits GI.1 protease, it is an order of magnitude less effective against GII protease. Other GI.1 protease inhibitors also tend to be less effective against GII proteases. To understand the structural basis for the potency difference, we determined the crystal structures of proteases of GI.1, pandemic GII.4 (Houston and Sydney), and GII.3 in complex with rupintrivir. These structures show that the open substrate pocket in GI protease binds rupintrivir without requiring significant conformational changes, whereas, in GII proteases, the closed pocket flexibly extends, reorienting arginine-112 in the BII-CII loop to accommodate rupintrivir. Structures of R112A protease mutants with rupintrivir, coupled with enzymatic and inhibition studies, suggest R112 is involved in displacing both substrate and ligands from the active site, implying a role in the release of cleaved products during polyprotein processing. Thus, the primary determinant for differential inhibitor potency between the GI and GII proteases is the increased flexibility in the BII-CII loop of the GII proteases caused by the H-G mutation in this loop. Therefore, the inherent flexibility of the BII-CII loop in GII proteases is a critical factor to consider when developing broad-spectrum inhibitors for HuNoV proteases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Dynamic Binary Complexes (DBC) as Super-Adjustable Viscosity Modifiers for Hydraulic Fracturing Fluids

In the preceding project year two, we refined three DBC formulations from a selection of over 50 different chemistries. The optimization study primarily encompassed testing for (i) reversibility extent, (ii) performance in the presence of chemical additives, (iii) adhesion and friction behavior during displacement in wellbores and pipelines, (iv) corrosion protection performance, and (v) injection performance with model fracture systems at the laboratory scale. Highly promising results obtained from all these tests signify the significant potential of DBCs in enhancing hydrocarbon recovery from unconventional reservoirs. The primary activities in the third project year included publishing experimental findings across multiple articles and conducting outreach initiatives. Throughout the year, we undertook tasks such as replicating experimental results, further optimizing various formulations and their associated experimental sets, and conducting additional tests to address missing components based on reviewer feedback and suggestions. We also explored the surfactant and friction-reduction aspects of selected formulations through drag reduction tests. In addition, we constructed an improved fracturing performance setup and performed flow injection tests. The specific DBC formulations focused on during this project period were A8/B1, A12/B5, and A10/B12. We also obtained results for additional DBC formulations and a select few commercial fracturing fluids for the purpose of comparison. Within the project's scope, we aim to enhance the experimental findings with the development of various models. The first two years focused on two key aspects: (i) the creation of a high-fidelity hydraulic fracturing model for non-Newtonian fluids to gain insights into the implementation of DBC fluids in fracking environments, and (ii) the development of a multiphase flow simulator for estimating total production, fluid saturation in the reservoir, and the creation of a fracture propagation model and kinetic Monte Carlo (kMC) models for diverse applications. In the third year, we delved into the fundamental nanostructural properties of DBCs, exploring aspects such as material chemistry, pH tunability, and control of DBC formation and stability. Subsequently, in the extension year, we conducted a systematic investigation of various building blocks containing primary, secondary, and tertiary amine functional groups to understand their impact on rheological and viscoelastic properties. Furthermore, we explored a Dissipative Particle Dynamics (DPD) model to simulate self-assembly processes with precision, creating a high-fidelity representation of relevant nanostructures. The tasks performed this year with the significant results obtained have been discussed in Section 2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Partial wrapping of single-stranded DNA by replication protein A and modulation through phosphorylation

Abstract Single-stranded DNA (ssDNA) intermediates which emerge during DNA metabolic processes are shielded by replication protein A (RPA). RPA binds to ssDNA and acts as a gatekeeper to direct the ssDNA towards downstream DNA metabolic pathways with exceptional specificity. Understanding the mechanistic basis for such RPA-dependent functional specificity requires knowledge of the structural conformation of ssDNA when RPA-bound. Previous studies suggested a stretching of ssDNA by RPA. However, structural investigations uncovered a partial wrapping of ssDNA around RPA. Therefore, to reconcile the models, in this study, we measured the end-to-end distances of free ssDNA and RPA–ssDNA complexes using single-molecule FRET and double electron–electron resonance (DEER) spectroscopy and found only a small systematic increase in the end-to-end distance of ssDNA upon RPA binding. This change does not align with a linear stretching model but rather supports partial wrapping of ssDNA around the contour of DNA binding domains of RPA. Furthermore, we reveal how phosphorylation at the key Ser-384 site in the RPA70 subunit provides access to the wrapped ssDNA by remodeling the DNA-binding domains. These findings establish a precise structural model for RPA-bound ssDNA, providing valuable insights into how RPA facilitates the remodeling of ssDNA for subsequent downstream processes.

Biochemistry & Molecular Biology↗

DOE OSTI2026

The solubility and speciation of REE phosphate endmembers (CePO 4 and YPO 4 ) in Cl-rich aqueous fluids from 350 to 450 °C and implications for natural systems

The rare earth elements (REE) are important metals used increasingly in advanced technologies. Within the crust, the elements Ce and Y are commonly more abundant compared to other lanthanides and comprise important end-member constituents of REE-bearing minerals. Specifically, Ce is part of the light (L) REE which have larger ionic radii than the heavy (H) REE, which are grouped together with Y. These differences in ionic radius can lead to important physico-chemical trends within the lanthanide group. Despite a recent increase in experimental and thermodynamic data for the REE at high temperature and pressure, there is still a significant lack of these data at supercritical conditions. In this study we conducted batch-type experiments to measure the solubility of REE phosphates (CePO 4 and YPO 4 ) at varying starting pH (1.5–10), and salinity (0.01–1.4 mol/kg NaCl) at 350 and P sat , and from 400 to 450 °C at 700 bar. Results show that the solubility of Ce (33–0.14 ppb) is generally higher than Y (13–0.13 ppb) and that Ce complexes more strongly with both chloride and hydroxyl ligands compared to Y. The solubilities of both REE phosphates are highly pH-dependent and, to a lesser extent, depend on salinity at the studied conditions. The solubility data from this study were implemented into the GEMSFITS program to optimize the thermodynamic properties of Ce and Y hydroxyl and chloride species. The updated standard partial molal Gibbs energies of formation (Δ f G 0 T,P ) are used within the experimental temperature and pressure range to accurately predict the CePO 4 and YPO 4 solubility and Ce and Y speciation behavior. Based on the updated thermodynamic properties we also provide formation constants (log β n Cl,OH ) for Ce and Y hydroxyl and chloride species. Updated thermodynamic properties are applied to model REE-apatite dissolution and REE mobility based on the Pea Ridge iron oxide apatite deposit in Missouri, USA. The apatite dissolution model replicates natural observations including the replacement of monazite and xenotime after apatite and is an example of the utility of the new thermodynamic constants applied to supercritical crustal fluids. Furthermore, the findings of this study advance the predictive capabilities of geochemical models, our understanding of the behavior of individual REE, and permit modeling the overarching fractionation trends between LREE and HREE in supercritical crustal fluids.

58 GEOSCIENCES↗