Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reactive force field”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SPARC-X: Quantum simulations at extreme scale - reactive dynamics from first principles

We have developed the massively parallel electronic structure code SPARC-X: a computational framework for performing Kohn-Sham Density Functional Theory (DFT) calculations that can scale linearly with the number of atoms in the system, while being able to leverage petascale and emerging exascale parallel computers to study chemical phenomena at unprecedented length and time scales. SPARC-X exploits a recent breakthrough in electronic structure methodologies: systematically improvable, strictly local, orthonormal, discontinuous real-space bases that efficiently and systematically capture the local chemistry of the system. With further adaptation using new machine-learning techniques and the use of the massively parallel Spectral Quadrature (SQ) electronic structure method, the algorithmic complexity and prefactor associated with DFT calculations involving semilocal as well as hybrid functionals are dramatically reduced. Using petascale computational resources, SPARC-X enables quantum mechanical simulations at length and time scales previously accessible only by empirical approaches, e.g., 1,000,000 atoms for a few picoseconds using semilocal functionals or 1,000 atoms for a few picoseconds using hybrid functionals. Using exascale resources, the sizes and times targeted are two orders of magnitude larger. Such a capability has applications in a wide variety of chemical sciences, including reactive interfaces where large length- and/or long time-scales are needed and traditional force fields fail. This is particularly important in dynamic catalysis, where bond breaking and formation must be understood in detail. We developed, tested, and employed the SPARC-X framework to understand the photocatalytic properties of TiO 2 nanoparticles, revealing finite size effects that cannot be captured with standard model systems or functionals. This integrated development and application strategy ensures that SPARC-X remains a robust, efficient, and scalable software package for quantum simulations on current petascale and emerging exascale computing resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

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

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2026

Surface Segregation of Liquid Metal Plasma-Facing Component Alloys: A ReaxFF Investigation

Engineering liquid metal alloys offers a transformative pathway for plasma-facing components by enabling chemically tailored surfaces that can simultaneously optimize plasma-material interactions, reduce divertor heat flux, and enhance core plasma confinement, thereby advancing the commercial viability of nuclear fusion power plants. This study, employing an atomistic simulation approach, provides direct evidence that incorporating nonmetal surface-active agents (such as O and H, or their combination) enables strong surface segregation. This capability makes tin−aluminum (Sn−Al) and tin−lithium (Sn−Li) alloys, with suitable compositions, good candidates for PFC applications. Specifically, the presence of low-Z solutes (Li, Al) leads to preferential surface enrichment, which imparts low-Z sputtering characteristics, while the Sn solvent maintains thermophysical stability. To systematically examine this behavior, we developed a ReaxFF force field spanning the full Sn/Al/Li/O/H chemistry, validated it against formation energies and elastic constants, and applied it in reactive molecular dynamics simulations at fusion-relevant temperatures. We also introduced an overlapbased segregation index that captures interfacial compositional separation directly from atomistic density distributions. Here, this metric reveals a clear hierarchy of segregation regimes and provides a unified view across all systems studied. Together, these findings establish a mechanistic link between nonmetal chemistry and interfacial structure, providing a predictive framework for designing self-adaptive, low-sputtering liquid metal alloys for fusion applications.

Alloys↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
DOE OSTI2025

Spin-State and Reorganization Energy Considerations for Metal-Centered Photoredox Catalysis

Transition-metal complexes featuring metal-centered excited states have recently emerged as mechanistically distinct platforms for selective photochemistry, including photoredox catalysis. Among these, Co(III) complexes have demonstrated productive photoinduced electron transfer via the 3 T 1 metal-centered state. In contrast, photoreactivity from the 5 T 2 metal-centered state in Fe(II) polypyridyl complexes remains limited. Building on our prior report concerning reactivity associated with the 5 T 2 state in [Fe(tren(py) 3 )] 2+ (tren(py) 3 = tris(2-pyridylmethyliminoethyl)-amine), we introduced stronger-field ligands in an effort to increase excited-state energies of Fe(II) polypyridyl complexes and enhance reactivity. Despite achieving nanosecond-scale excited-state lifetimes and favorable thermodynamic driving forces, no photoreactivity was observed. Reinvestigation of the observations previously reported for [Fe(tren(py) 3 )] 2+ revealed interactions between the metal complex and the substrate in their respective ground states that mimicked dynamic quenching of the chromophore, prompting a reassessment of mechanistic considerations inherent in leveraging reductive chemistry from the 5 T 2 excited state of Fe(II). Our analysis indicates that electron transfer from the 5 T 2 excited state of a low-spin d6 metal is subject to significant barriers both in terms of reorganization energies and spin conservation that undermines its ability to act as an electron donor for photoredox catalysis. In contrast, ligand fields that are sufficient to stabilize the 3 T 1 excited state have available to them numerous spin-allowed and, in certain cases, near-barrierless pathways to engage in excited-state electron transfer (both oxidative and reductive depending on the identity of the metal). These results highlight the critical role of spin-state changes and their associated reorganization energy requirements in metal-centered photoredox catalysis.

charge transfer↗
DOE OSTI2026

Inductance meets memory in a quantum magnet

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn₃Si₂Te₆, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.

Cao, Tristan [University of Colorado, Boulder]↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Extended Lagrangian Born–Oppenheimer molecular dynamics using a Krylov subspace approximation

It is shown how the electronic equations of motion in extended Lagrangian Born–Oppenheimer molecular dynamics simulations can be integrated using low-rank approximations of the inverse Jacobian kernel. This kernel determines the metric tensor in the harmonic oscillator extension of the Lagrangian that drives the evolution of the electronic degrees of freedom. The proposed kernel approximation is derived from a pseudoinverse of a low-rank estimate of the Jacobian, which is expressed in terms of a generalized set of directional derivatives with directions that are given from a Krylov subspace approximation. The approach allows a tunable and adaptive approximation that can take advantage of efficient preconditioning techniques. The proposed kernel approximation for the integration of the electronic equations of motion makes it possible to apply extended Lagrangian first-principles molecular dynamics simulations to a broader range of problems, including reactive chemical systems with numerically sensitive and unsteady charge solutions. This can be achieved without requiring exact full calculations of the inverse Jacobian kernel in each time step or relying on iterative non-linear self-consistent field optimization of the electronic ground state prior to the force evaluations as in regular direct Born–Oppenheimer molecular dynamics. We note the low-rank approximation of the Jacobian is directly related to Broyden’s class of quasi-Newton algorithms and Jacobian-free Newton–Krylov methods and provides a complementary formulation for the solution of nonlinear systems of equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The influence of particle size on the fluid dynamics of a laser-induced plasma

The interaction of a laser-induced shock wave with nanoparticles and microparticles of aluminum oxide is investigated through experiments and modeling. The chemistry and physics of the interaction between the particles and plasma generated from laser ablation shows similarities and discrete differences for the two particle sizes. For both particle sizes, early stage (< 10 μs) ionization was dominant and evidenced by higher concentrations of Al II. While both sizes exhibit ionization over the same duration, the intensity of emission was greater for nanoparticles indicating greater concentrations of ionized species. Moreover, the dispersion of species was notably more elongated for microparticles while radial dispersion was more pronounced for nanoparticles with elevated drag forces. At later stages (i.e., > 10 μs), oxidation reactions were dominant for both particle sizes, but the same distinctions in flow field were observed and attributed to particle drag. In all stages of interaction, microparticles expand axially with less drag that suppresses their radial expansion. As a result, the dispersion of reactive species was mapped over an up to 80% larger area for nanoparticles relative to microparticles. Results shown here can be applied toward advancing experimental diagnostics and particle-shock wave modeling and simulation efforts for energetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts

The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd 8 Au 92 nanoparticles supported on silica using surface-sensitive techniques, we report that whether Pd overgrowth occurs before or after Au nanoparticle deposition onto the support controls the surface Pd ensemble size and abundance. These differences in Pd ensembles influence catalytic reactivity in H 2 –D 2 isotope exchange and benzaldehyde hydrogenation, which, in correlation with theoretical calculations, is used to elucidate the active site(s) in each reaction. To clarify how the synthetic sequence controls the formation of Pd ensembles, we combine numerical wetting calculations and molecular dynamics simulations (with a machine-learned force field) to visualize Pd deposition and migration on the nanoparticle surface, respectively. Our results suggest that the nanoparticle–support interface restricts nanoparticle accessibility to Pd deposition, which consequently controls the Pd ensemble size, illustrating the critical role of nanoscale wetting phenomena during bimetallic catalyst preparation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modular Approach for the Synthesis of Bottlebrush Diblock Copolymers from Poly(Glycidyl Methacrylate)-block-Poly(Vinyldimethylazlactone) Backbones

A strategy that enables the facile synthesis of bottlebrush block copolymers with flexible backbones was developed. A demonstration of the strategy’s utility was carried out by grafting end-functionalized polymethylmethacrylate (PMMA) and polystyrene (PS) to the dually reactive block copolymer, poly(glycidyl methacrylate)-block-poly(vinyldimethylazlactone) (PGMA-b-PVDMA). Five different bottlebrush diblock copolymers were investigated by size-exclusion chromatography (SEC), 1H NMR, Fourier transform infrared (FT-IR), differential scanning calorimetry (DSC), X-ray scattering methods, atomic force microscopy (AFM), rheology and computational simulations using molecular dynamics (MD), and self-consistent field theory (SCFT). A relationship between the glass transition temperature and the fraction of chain ends was demonstrated by DSC and highlights the potential of this synthetic method to tailor polymer properties. All five samples were found to be in a disordered phase exhibiting multiscale structures revealed by two broad peaks in small-angle X-ray scattering (SAXS) that can be attributed to graft-to-graft and backbone-to-backbone density correlations using MD simulations. The SCFT-based simulations justify the observation of a disordered phase due to its stabilization by the grafts. Additionally, this modular approach can be easily extended to other grafts, including responsive, conducting, and charged polymers with the prerequisite end groups. The versatility and ease of assembling these functional bottlebrushes constitute a powerful “toolbox” method for the rapid and scalable synthesis of novel bottlebrush block copolymers with desired properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials

Abstract The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy.

Kanhaiya, Krishan (ORCID:0000000236222655)↗
DOE OSTI2020

Recent Advances in the Application of Functionalized Lignin in Value-Added Polymeric Materials

The quest for converting lignin into high-value products has been continuously pursued in the past few decades. In its native form, lignin is a group of heterogeneous polymers comprised of phenylpropanoids. The major commercial lignin streams, including Kraft lignin, lignosulfonates, soda lignin and organosolv lignin, are produced from industrial processes including the paper and pulping industry and emerging lignocellulosic biorefineries. Although lignin has been viewed as a low-cost and renewable feedstock to replace petroleum-based materials, its utilization in polymeric materials has been suppressed due to the low reactivity and inherent physicochemical properties of lignin. Hence, various lignin modification strategies have been developed to overcome these problems. Herein, we review recent progress made in the utilization of functionalized lignins in commodity polymers including thermoset resins, blends/composites, grafted functionalized copolymers and carbon fiber precursors. In the synthesis of thermoset resins such as polyurethane, phenol-formaldehyde and epoxy, they are covalently incorporated into the polymer matrix, and the discussion is focused on chemical modifications improving the reactivity of technical lignins. In blends/composites, functionalization of technical lignins is based upon tuning the intermolecular forces between polymer components. In addition, grafted functional polymers have expanded the utilization of lignin-based copolymers to biomedical materials and value-added additives. Different modification approaches have also been applied to facilitate the application of lignin as carbon fiber precursors, heavy metal adsorbents and nanoparticles. These emerging fields will create new opportunities in cost-effectively integrating the lignin valorization into lignocellulosic biorefineries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electric-Field-Induced Assembly of an Ionic Liquid–Water Interphase Enables Efficient Heavy Metal Electrosorption

Controlling ion desolvation, transport, and charge transfer at the electrode-electrolyte interface (EEI) is critical to enabling the rational design of efficient and selective separation of targeted heavy metals and decontamination of industrial waste water. One challenge is to sufficiently resolve and interrogate the intermediate transformation steps between solvated metal cations and bare cations for electroreduction at the EEI and establish pathways to modulate these steps to achieve efficient energy transfer for targeted reactive separations. Herein, we obtained a predictive understanding of the effect of adding a hydrophobic ionic liquid, 1-ethyl-3-methylimidazolium chloride (EMIMCl), to an aqueous electrolyte on modulating the desolvation and electrosorption of Pb 2+ cations using a combination of experimental and theoretical techniques. Here, we reveal formation of a compact interphase layer consisting of EMIMCl-Pb complex clusters under an applied electric field using operando electrochemical Raman spectroscopy, atomic force microscopy, and electrochemical impedance spectroscopy measurements combined with classical molecular dynamics simulations. The application of a larger negative potential is shown to result in formation of a well oriented layer with the positive imidazolium ring of the EMIMCl lying on the electrode and the long hydrophobic alkyl chain extending into the bulk electrolyte. This oriented layer is demonstrated to facilitate desolvation of incoming solvated Pb 2+ cations and decrease the charge transfer resistance for Pb electrodeposition, which has important implications for selective removal of Pb from contaminated mixtures. Overall, our studies open up new opportunities to modulate ion desolvation using hydrophobic ionic liquids in aqueous electrolytes for efficient heavy metal separation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Summary Report of the Reactive CO 2 Capture: Process Integration for the New Carbon Economy Workshop

Decarbonization of our global economy is required to limit planetary warming to +1.5⁰C above pre-industrial levels, an ambitious goal set into motion by the Paris agreement. Given the scale and urgency, the solution demands international, cross-sector advancements spanning policy, social responsibility, and technology, with emphasis on step changes over incremental changes. To that end, technological revolutions that disrupt the status quo need to be envisioned and enacted. One potential technological revolution is the production of fuels, chemicals, and materials from carbon dioxide (CO 2 ) as the starting feedstock, leveraging renewable energy as the driving force. Over the past decades, significant research, development, and deployment has occurred on technologies for capturing CO 2 from point sources or the air and utilizing this CO 2 as a working fluid or as a chemical reactant; however, most of this work has been siloed in these two categories. Recently, an emerging field has started to explore the direct integration of CO 2 capture and conversion technologies as a means to reduce overall energy demand (i.e., avoid energy penalty of CO 2 desorption/regeneration of capture media) and capital expense through process intensification. This strategy represents an opportunity to leapfrog forward this technological revolution. However, the field is in its infancy and the technologies are at an early stage of development, thus it is critically important to define and assess the value proposition of this strategy relative to alternatives (e.g., separated capture and conversion technologies, fuels and chemicals derived from renewable feedstocks like biomass, and industrial electrification) to chart a path forward. To identify next steps, we organized a workshop titled “Reactive CO 2 Capture: Process Integration for the New Carbon Economy” which was held in Golden, Colorado, February 18–19, 2020. The focus of this workshop was to discuss approaches for merging CO 2 capture and CO 2 conversion/utilization systems into what we denoted as an integrated "reactive capture" strategy. By our definition, reactive capture of CO 2 is the coupled process of capturing CO 2 from a mixed gas stream and converting it into a valuable product without going through a purified CO 2 intermediate (see full definition in the Introduction section). This report seeks to summarize feedback from the approximately 125 participants and subject matter experts in attendance from academia, industry, U.S. Department of Energy (DOE), and DOE national laboratories. The workshop agenda is included in Appendix A and the full list of attendees can be found in Appendix B. To elucidate a path forward, we first asked the attendees to define what success would look like for reactive capture in the short term (0–5 years), midterm (5–10 years), and long term (10+ years) and then asked them to answer four questions related to how we could achieve that success: (1) What are the key barriers and challenges to success? (2) What are needed activities to overcome barriers and challenges? (3) What opportunities will arise from these activities? (4) What is a target outcome and what metrics need to be met?

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Modelling and analysis of salt-convection effect on oxide reduction process for uranium oxides using smoothed particle hydrodynamics

Oxygen ion transport via molten salt continuum is a key kinetic feature of electrochemical reduction process for uranium oxides in molten salt. During the electrolysis, the evolution of oxygen ion concentration field in molten salt continuum appears from porous metallic layer to open bulk salt. Despite the important role affecting process throughput, its effect coupled with controlled salt movement was not adequately investigated. To capture the reactive oxygen ion transport phenomena, a multidimensional reduction model based on smoothed particle hydrodynamics (SPH) was combined with the advection-diffusion model. A pelletized oxide fuel bed capturing interconnected salt continuum is considered as a reactant material geometry, which allows forced convective delivery of molten salt through the reactant materials with a relatively low pressure head. Here, in this study, reduction time of a pelletized-fuel bed was quantified with respect to key design parameters of the reactant material configuration. The simulation results demonstrated the advantage of salt convection which effectively removes oxygen ion products from the reactant materials and consequently accelerates the reduction process. Moreover, the reduction-time correlation of a single pelletized fuel was developed as a function of relevant dimensionless parameters and was extended to the application for a pelletized-fuel bed. Finally, several insights and implications in the use of a pelletized oxide fuel were presented. The developed SPH-based framework enabled multi-physics analysis of governing dynamics in appreciating and screening candidate reactant material configurations in a cost-effective manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗