Publications

Abstract not provided.

Under IER-305, critical experiments were done with and without molybdenum sleeves on 7uPCX fuel rods. The experiments were done in new critical assembly hardware designed to support the 7uPCX fuel in a 1.55 cm triangular-pitched array.

The generating value from lignin through depolymerization and biological conversion to valuable fuels, chemicals, or intermediates has great promise but is limited by several factors including lack of cost-effective depolymerization methods, toxicity within the breakdown products, and low bioconversion of the breakdown products. High yield depolymerization of natural lignins requires cleaving carbon-carbon bonds in addition to ether bonds. To address that need, we report that a chelator-mediated Fenton reaction can efficiently cleave C-C bonds in sulfonated polymers at or near room temperature, and that unwanted repolymerization can be minimized through optimizing reaction conditions. This method was used to depolymerize lignosulfonate from M_w = 28,000 g/mol to M_w = 800 g/mol. The breakdown products were characterized by SEC, FTIR and NMR and evaluated for bioavailability. The breakdown products are rich in acid, aldehyde, and alcohol functionalities but are largely devoid of aromatics and aliphatic dienes. A panel of nine organisms were tested for the ability to grow on the breakdown products. Growth at a low level was observed for several monocultures on the depolymerized LS in absence of glucose. Much stronger growth was observed in the presence of 0.2% glucose and for one organism we demonstrate doubling of melanin production in the presence of depolymerized LS. The results suggest that this chelator-mediated Fenton method is a promising new approach for biological conversion of lignin into higher value chemicals or intermediates.

In this report broadband microwave spectra were recorded over the 2-18 GHz frequency range for a series of four model aromatic components of lignin; namely, guaiacol (ortho-methoxy phenol, G), syringol (2,6-dimethoxy phenol, S), 4-methyl guaiacol (MG), and 4-vinyl guaiacol (VG), under jet-cooled conditions in the gas phase. Using a combination of ¹³C isotopic data and electronic structure calculations, distortions of the phenyl ring by the substituents on the ring are identified. In all four molecules, the r_C(1)-C(6) bond between the two substituted C-atoms lengthens, leading to clear bond alternation that reflects an increase in the phenyl ring resonance structure with double bonds at r_C(1)-C(2), r_C(3)-C(4) and r_C(5)-C(6). Syringol, with its symmetric methoxy substituents, possesses a microwave spectrum with tunneling doublets in the a-type transitions associated with H-atom tunneling. These splittings were fit to determine a barrier to hindered rotation of the OH group of 1975 cm^-1, a value nearly 50% greater than that in phenol, due to the presence of the intramolecular OH…OCH₃ H-bonds at the two equivalent planar geometries. In 4-methyl guaiacol, methyl rotor splittings are observed and used to confirm and refine an earlier measurement of the three-fold barrier V₃ = 67 cm^-1. Finally, 4-vinyl guaiacol shows transitions due to two conformers differing in the relative orientations of the vinyl and OH groups.

Sandia provided technical assistance to the Alaska Ocean Cluster to assess potential market opportunities regarding byproducts of crab in the greater Alaska region. Crab contains a wide variety of proteins, chitin, lipids, minerals, and pigments. Currently, only a small portion of these components are utilized, primarily proteins associated with crab meat. Sandia provided an assessment of the current market landscape and opportunities related to crab byproducts including market size and applications. Sandia subject matter experts conducted an analysis and provide the Alaska Ocean Cluster team with a report describing the state of research and market opportunities offered by Alaska crab byproducts. The final report focused on market opportunities regarding chitosan production, chitin extraction, as well as an overview of the key market players and applications.

Composite structures inherently develop residual stresses during their curing process. Driven predominately by mismatched thermal strains between differing materials or ply orientations, but also affected by curing process phenomena like polymer shrinkage, these residual stresses can lead to failure within composite structures. There are several methods varying in complexity that can be used to model the development of residual stresses, all of which are capable of capturing sufficient detail to understand the residual stress state at the ply level. However, explicitly modeling all plies of a layup in a composite structure can be prohibitively expensive based on the number of plies, structure size, and required element size. The computational cost can be reduced through the homogenization of the composite layup without losing much fidelity of the overall response of the structure. The homogenization process reduces the many plies of a laminate to a single lamina that reduces complexity and increases the mesh size where a single element can span multiple plies. This report focuses on verification and validation efforts for a homogenization process using a suite of finite element simulations rather than an analytic solution derived from classical laminate theory. Initial verification using representative element volumes indicated there was minimal error in the homogenization process; however, this compounded to a small, but acceptable error in strip and split ring experimental composite structures. The error does under predict the residual stress state in the strip and split ring and should be accounted for when simulating composite structures with homogenized properties.

A graph is a mathematical representation of a network; we say it consists of a set of vertices, which are connected by edges. Graphs have numerous applications in various fields, as they can model all sorts of connections, processes, or relations. For example, graphs can model intricate transit systems or the human nervous system. However, graphs that are large or complicated become difficult to analyze. This is why there is an increased interest in the area of graph partitioning, reducing the size of the graph into multiple partitions. For example, partitions of a graph representing a social network might help identify clusters of friends or colleagues. Graph partitioning is also a widely used approach to load balancing in parallel computing. The partitioning of a graph is extremely useful to decompose the graph into smaller parts and allow for easier analysis. There are different ways to solve graph partitioning problems. For this work, we focus on a spectral partitioning method which forms a partition based upon the eigenvectors of the graph Laplacian (details presented in Acer, et. al.). This method uses the LOBPCG algorithm to compute these eigenvectors. LOBPCG can be accelerated by an operator called a preconditioner. For this internship, we evaluate a randomized Cholesky (rchol) preconditioner for its effectiveness on graph partitioning problems with LOBPCG. We compare it with two standard preconditioners: Jacobi and Incomplete Cholesky (ichol). This research was conducted from August to December 2021 in conjunction with Sandia National Laboratories.

This all-inclusive document describes the components, installation, and usage of the Seascape system. Additionally, this manual outlines the step-by-step processes for setting up your own local instance of Seascape, incorporating new datasets and algorithms into Seascape, and how to use the system itself. A brief overview of Seascape is provided in Section 1.2. System components and the various roles of the intended users of the system are described in Section 1.3. Next, steps on how each role uses Seascape are explained in Section 2.1. Finally, the steps to incorporate data into Seascape-DB and an algorithm into Seascape-VV are outlined in Sections 2.2 and 2.3, respectively. Steps to set up an instance of Seascape can be found in Appendix A.1. Finally, Seascape usage can be found in Section 2.1. The appendix includes code examples, frequently asked questions, terminology, and a list of acronyms.

Understanding hydrogen incorporation into palladium requires detailed knowledge of surface and subsurface structure and atomic interactions as surface hydrogen is being embedded. Using density functional theory (DFT), we examine the energies of hydrogen layers of varying coverage adsorbed on Pd(111). Here we find that H–H and H–Pd interactions promote the formation of the well-known ($\sqrt{3}$ x $\sqrt{3}$) phases but also favor an unreported (3 × 3) phase at high H coverages for which we present experimental evidence. We relate the stability of isolated H vacancies of the (3 × 3) phase to the need of H₂ molecules to access bare Pd before they can dissociate. Following higher hydrogen dosage, we observe initial steps of hydride formation, starting with small clusters of subsurface hydrogen. The interaction between H and Pd is complicated by the persistent presence of carbon at the surface. X-ray photoelectron spectroscopy experiments show that trace amounts of carbon, emerging from the Pd bulk despite many surface cleaning cycles, become mobile enough to repopulate the C-depleted surface at temperatures above 200 K. When exposed to hydrogen, these surface carbon atoms react to form benzene, as evidenced by scanning tunneling microscopy observations interpreted with DFT.

Mechanisms underlying the mechanically induced amorphization of metal-organic frameworks (MOFs) are of current interest, and both high-pressure experimentation and molecular dynamics simulations have been used to reveal the fundamentals of load bearing, deformation, and pressure-induced amorphization (PIA) in these highly porous materials. Unfortunately, MOFs are typically highly susceptible to amorphization, which limits the conditions under which they can be processed and used. However, their flexible structures can be stabilized at high pressures by incorporating guest species into the framework matrix. In this study, a large-molecule pressure transmitting medium (DAPHNE 7575) is used as a structure-fortifying guest species to stabilize the prototypical MOF-5 at high pressures (>9 GPa) and enable the recovery of crystalline material upon decompression. Structural changes associated with the penetration of the pressure transmitting medium on compression are examined using a combination of high-pressure synchrotron powder diffraction and molecular dynamics simulations. This work enhances the understanding of PIA in MOFs while showcasing a potential route for the stabilization of MOFs at surprisingly high pressures.

Alloying is often employed to stabilize nanocrystalline materials against microstructural coarsening. The stabilization process results from the combined effects of thermodynamically reducing the curvature-dominated driving force of grain-boundary motion via solute segregation and kinetically pinning these same grain boundaries by solute drag and Zener pinning. The competition between these stabilization mechanisms depends not only on the grain-boundary character but can also be affected by imposed compositional and thermal fields that further promote or inhibit grain growth. In this work, we study the origin of the stability of immiscible nanocrystalline alloys in both homogeneous and heterogeneous compositional and thermal fields by using a multi-phase-field formulation for anisotropic grain growth with grain-boundary character-dependent segregation properties. This generalized formulation allows us to model the distribution of mobilities of segregated grain boundaries and the role of grain-boundary heterogeneity on solute-induced stabilization. As an illustration, we compare our model predictions to experimental results of microstructures in platinum-gold nanocrystalline alloys. Our results reveal that increasing the initial concentration of available solute progressively slows the rate of grain growth via both heterogeneous grain-boundary segregation and Zener pinning, while increasing the temperature generally weakens thermodynamic stabilization effects due to entropic contributions. Finally, we demonstrate as a proof-of-concept that spatially-varying compositional and thermal fields can be used to construct dynamically-stable, graded, nanostructured materials. We discuss the implications of using such concepts as alternatives to conventional plastic deformation methods.

As the length scales of materials decrease, the heterogeneities associated with interfaces become almost as important as the surrounding materials. This has led to extensive studies of emergent electronic and magnetic interface properties in superlattices. However, the interfacial vibrations that affect the phonon-mediated properties, such as thermal conductivity, are measured using macroscopic techniques that lack spatial resolution. Although it is accepted that intrinsic phonons change near boundaries, the physical mechanisms and length scales through which interfacial effects influence materials remain unclear. Here we demonstrate the localized vibrational response of interfaces in strontium titanate–calcium titanate superlattices by combining advanced scanning transmission electron microscopy imaging and spectroscopy, density functional theory calculations and ultrafast optical spectroscopy. Structurally diffuse interfaces that bridge the bounding materials are observed and this local structure creates phonon modes that determine the global response of the superlattice once the spacing of the interfaces approaches the phonon spatial extent. Our results provide direct visualization of the progression of the local atomic structure and interface vibrations as they come to determine the vibrational response of an entire superlattice. Direct observation of such local atomic and vibrational phenomena demonstrates that their spatial extent needs to be quantified to understand macroscopic behaviour. Tailoring interfaces, and knowing their local vibrational response, provides a means of pursuing designer solids with emergent infrared and thermal responses.

Sandia National Labs has created a noteworthy and effective internship program whose focus is creating a talent pipeline for the laboratory. Our program utilizes industry standard conversion calculations to examine the effectiveness of the program and to compare to our competitors. Sandia defines students eligible for conversion as graduating in the given fiscal year and in their final degree program. Students indicate to SIP upon hire and at certain checkpoints throughout their internship if they are in their final degree program or not. This means that they will not continue to a higher degree program after they graduate. For instance, someone who is graduating with a master’s degree in the current FY and does not plan to pursue a PhD would be considered eligible, while an undergrad student who is graduating in the same year, but plans to pursue a graduate degree, would not be considered eligible for conversion. Conversion data pulled for this report includes all eligible interns for fiscal year 2021. We use a rolling population, which includes anyone who was an intern at some point during FY21. To calculate conversion, we narrow our population down to the students who graduated between October 2020 through September 2021, who have indicated that they are in their final degree program. The conversion data was pulled on 10/29/2021, so any conversions completed after this date will not be included in the calculation. Our conversion data includes students who separated from Sandia and returned as a staff member. Conversions also include FTE, LTE, and postdoc positions. We do not include conversion to contractor positions in our calculations.

We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an $\textit{N}$-way rank-R parametric tensor 𝓜 ϵ (0, ∞)^{$I$Χ∙∙∙Χ$I$} generating Poisson observations can be accurately estimated from approximately $IR^2 \text{log}^2_2(I)$ non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known $\textit{a priori}$.

The following report summarizes the status update during this quarter for the National Nuclear Security Agency (NNSA) initiated Minority Serving Institution Partnership Plan's (MSIPP) project titled, Partnership for Advanced Manufacturing Education and Research (PAMER). In 2016, the National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) targeting Tribal Colleges and Universities (TCUs) to offer programs that will prepare students for technical careers in NNSA’s laboratories and production plants. The MSIPP consortium’s approach is as follows: 1) align investments at the college and university level to develop a curriculum and workforce needed to support NNSA’s nuclear weapon enterprise mission, and 2) to enhance research and education at under-represented colleges and universities.

The Machine Learning for Correlated Intelligence Laboratory Directed Research & Development (LDRD) Project explored competing a variety of machine learning (ML) classification techniques against a known, open source dataset through the use of a rapid and automated algorithm research & development (RD) infrastructure. This approach relied heavily on creating an infrastructure in which to provide a pipeline for automatic target recognition (ATR) ML algorithm competition. Results are presented for nine ML classifiers against a primary dataset using the pipeline infrastructure developed for this project. New approaches to feature set extraction are presented and discussed as well.

Anomalous behavior is ubiquitous in subsurface solute transport due to the presence of high degrees of heterogeneity at different scales in the media. Although fractional models have been extensively used to describe the anomalous transport in various subsurface applications, their application is hindered by computational challenges. Simpler nonlocal models characterized by integrable kernels and finite interaction length represent a computationally feasible alternative to fractional models; yet, the informed choice of their kernel functions still remains an open problem. We propose a general data-driven framework for the discovery of optimal kernels on the basis of very small and sparse data sets in the context of anomalous subsurface transport. Using spatially sparse breakthrough curves recovered from fine-scale particle-density simulations, we learn the best coarse-scale nonlocal model using a nonlocal operator regression technique. Predictions of the breakthrough curves obtained using the optimal nonlocal model show good agreement with fine-scale simulation results even at locations and time intervals different from the ones used to train the kernel, confirming the excellent generalization properties of the proposed algorithm. A comparison with trained classical models and with black-box deep neural networks confirms the superiority of the predictive capability of the proposed model.

Nuclear spins were among the first physical platforms to be considered for quantum information processing1,2, because of their exceptional quantum coherence3 and atomic-scale footprint. However, their full potential for quantum computing has not yet been realized, owing to the lack of methods with which to link nuclear qubits within a scalable device combined with multi-qubit operations with sufficient fidelity to sustain fault-tolerant quantum computation. Here we demonstrate universal quantum logic operations using a pair of ion-implanted 31P donor nuclei in a silicon nanoelectronic device. A nuclear two-qubit controlled-Z gate is obtained by imparting a geometric phase to a shared electron spin4, and used to prepare entangled Bell states with fidelities up to 94.2(2.7)%. The quantum operations are precisely characterized using gate set tomography (GST)5, yielding one-qubit average gate fidelities up to 99.95(2)%, two-qubit average gate fidelity of 99.37(11)% and two-qubit preparation/measurement fidelities of 98.95(4)%. These three metrics indicate that nuclear spins in silicon are approaching the performance demanded in fault-tolerant quantum processors6. We then demonstrate entanglement between the two nuclei and the shared electron by producing a Greenberger–Horne–Zeilinger three-qubit state with 92.5(1.0)% fidelity. Because electron spin qubits in semiconductors can be further coupled to other electrons7–9 or physically shuttled across different locations10,11, these results establish a viable route for scalable quantum information processing using donor nuclear and electron spins.

Optical anticounterfeiting tags utilize the photoluminescent properties of materials to encode unique patterns, enabling identification and validation of important items and assets. These tags must combine optical complexity with ease of production and authentication to both prevent counterfeiting and to remain practical for widespread use. Metal-organic frameworks (MOFs) based on polynuclear, rare earth clusters are ideal materials platforms for this purpose, combining fine control over structure and composition, with tunable, complex energy transfer mechanisms via both linker and metal components. Here we report the design and synthesis of a set of heterometallic MOFs based on combinations of Eu, Nd, and Yb with the tetratopic linker 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene. The energetics of this linker facilitate the intentional concealment of the visible emissions from Eu while retaining the infrared emissions of Nd and Yb, creating an optical tag with multiple covert elements. Unique to the materials system reported herein, we document the occurrence of a previously not observed 11-metal cluster correlated with the presence of Yb in the MOFs, coexisting with a commonly encountered 9-metal cluster. We demonstrate the utility of these materials as intricate optical tags with both rapid and in-depth screening techniques, utilizing orthogonal identifiers across composition, emission spectra, and emission decay dynamics. This work highlights the important effect of linker selection in controlling the resulting photoluminescent properties in MOFs and opens an avenue for the targeted design of highly complex, multifunctional optical tags.

High-protein algal biomass is an important bio-commodity that has the potential to provide a new source of sustainable protein products. Herein is a critical review that identifies (1) the most relevant sustainability findings related to the processing of proteinaceous algal biomass to higher value protein products and (2) the potential pathways to improve life cycle assessment (LCA) and techno-economic analysis (TEA) metrics, including life-cycle carbon dioxide equivalent (CO2eq), life cycle energy, and minimum selling price (MSP) of these products. The critical review of the literature revealed a large variation in model input parameters relating to these metrics. Therefore, a Monte Carlo analysis was conducted to assess the risk associated with these input variations. To understand the uncertainties that propagate into high-protein algae to products' systems, we reviewed more than 20 state-of-the-art unit operations for algal biomass processing., including cell disruption, protein solubilization, protein precipitation and purification, and protein concentration. We evaluated displacement of proteinaceous products by algal-bioproducts, including ruminant feed, aquaculture feed, protein tablets, and biopolymers and biopolyesters, with prices in the market ranging from 1.9 to 120 $ kg―1 protein. This review realized that the MSP of ruminant and non-ruminant feed ranges from 0.65 ± 0.56 to 2.9 ± 1.1 $ kg―1 protein, and bioplastics' MSP ranges from 0.97 to 7.0 $ kg―1 protein. Regarding LCA metrics, there is limited research on life cycle energy in proteinaceous biomass concentration and bioproduct systems, reported at 32.7 MJ kgprotein―1, for animal feed displacement. Animal feed emissions in the literature report negative fluxes, representing environmental benefits, as low as ―3.7 kgCO2eq kg―1 protein and positive fluxes, i.e., global warming potential, as high as 12.8 kgCO2eq kg―1 protein. There is limited research on bioplastics life cycle emissions reported at 0.6 kgCO2eq kg―1 protein. In general, the studies to date of algae-derived protein bioproducts showed similar life cycle emissions to soybean meals, nylon, polymers, and polystyrenes. Our risk analysis realized that more than 50% of scenarios can result in negative-net life cycle CO2eq emissions. This review and risk analysis assess and demonstrate the scenarios that improve economic and environmental sustainability metrics in high-protein algal bioproduct systems.

Increasing growth in land-based wind turbine blades to enable higher machine capacities and capacity factors is creating challenges in design, manufacturing, logistics, and operation. Enabling further blade growth will require technology innovation. An emerging solution to overcome logistics constraints is to segment the blades spanwise and chordwise, which is effective, but the additional field-assembled joints result in added mass and loads, as well as increased reliability concerns in operation. An alternative to this methodology is to design slender flexible blades that can be shipped on rail lines by flexing during transport. However, the increased flexibility is challenging to accommodate with a typical glass-fiber, upwind design. In a two-part paper series, several design options are evaluated to enable slender flexible blades: downwind machines, optimized carbon fiber, and active aerodynamic controls. Part 1 presents the system-level optimization of the rotor variants as compared to conventional and segmented baselines, with a low-fidelity representation of the blades. The present work, Part 2, supplements the system-level optimization in Part 1 with high-fidelity blade structural optimization to ensure that the designs are at feasible optima with respect to material strength and fatigue limits, as well as global stability and structural dynamics constraints. To accommodate the requirements of the design process, a new version of the Numerical Manufacturing And Design (NuMAD) code has been developed and released. The code now supports laminate-level blade optimization and an interface to the International Energy Agency Wind Task 37 blade ontology. Transporting long, flexible blades via controlled flapwise bending is found to be a viable approach for blades of up to 100m. The results confirm that blade mass can be substantially reduced by going either to a downwind design or to a highly coned and tilted upwind design. A discussion of active and inactive constraints consisting of material rupture, fatigue damage, buckling, deflection, and resonant frequencies is presented. An analysis of driving load cases revealed that the downwind designs are dominated by loads from sudden, abrupt events like gusts rather than fatigue. Finally, an analysis of carbon fiber spar caps for downwind machines finds that, compared to typical carbon fibers, the use of a new heavy-tow carbon fiber in the spar caps is found to yield between 9% and 13% cost savings. Copyright:

Research on renewable and alternative fuels is crucial for improving the energy and environmental efficiency of modern gasoline internal combustion engines. To highlight the influence of fuel rheological and thermodynamic properties on phase change and atomisation processes, three types of gasoline blends were tested. More specifically, the campaign comprised a reference gasoline, an ethanol/gasoline blend (10% v/v) representative of renewable fuels, and an additised gasoline sample treated with viscoelasticity-inducing agents. High-speed imaging of the transient two-phase flow field arising in the internal geometry and the near-nozzle spray region of gasoline injectors was performed employing Diffuse Backlight Illumination. The metallic body of a commercial injector was modified to fit transparent tips realising two nozzle layouts, namely a two-hole real size model resembling the Engine Combustion Network spray G injector and an enraged replica with an offset hole. Experiments were conducted at realistic operating conditions comprising an injection pressure of 100 bar and ambient pressures in the range of 0.1–6.0 bar to cover the entire range of chamber pressures prevailing in Gasoline Direct Injection engines. The action of viscoelastic additives was verified to have a suppressive effect on in-nozzle cavitation (6% reduction in cavitation extent), while also enhancing spray atomisation at flash-boing conditions, in a manner resembling the more volatile gasoline/ethanol blends. Finally, persisting liquid ligaments were found to form after the end of injection for the additised sample, owing to the surfactant nature of the additives.

The controlled fabrication of vertical, tapered, and high-aspect ratio GaN nanowires via a two-step top-down process consisting of an inductively coupled plasma reactive ion etch followed by a hot, 85% H3PO4 crystallographic wet etch is explored. The vertical nanowires are oriented in the [0001] direction and are bound by sidewalls comprising of 3362 ¯ } semipolar planes which are at a 12° angle from the [0001] axis. High temperature H3PO4 etching between 60 °C and 95 °C result in smooth semipolar faceting with no visible micro-faceting, whereas a 50 °C etch reveals a micro-faceted etch evolution. High-angle annular dark-field scanning transmission electron microscopy imaging confirms nanowire tip dimensions down to 8–12 nanometers. The activation energy associated with the etch process is 0.90 ± 0.09 eV, which is consistent with a reaction-rate limited dissolution process. The exposure of the 3362 ¯ } type planes is consistent with etching barrier index calculations. The field emission properties of the nanowires were investigated via a nanoprobe in a scanning electron microscope as well as by a vacuum field emission electron microscope. The measurements show a gap size dependent turn-on voltage, with a maximum current of 33 nA and turn-on field of 1.92 V nm−1 for a 50 nm gap, and uniform emission across the array.

We present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)2), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.