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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Electropolishing-induced topographic defects in niobium: insights and implications for superconducting radio frequency applications

Electropolishing (EP) is the premier surface preparation method for high-Q, high-gradient superconducting RF cavities made of Nb. This leaves behind an apparently smooth surface, yet the achievable peak magnetic fields fall well below the superheating field of Nb, in most cases. Here, in this work, the ultimate surface finish of EP was investigated by studying its effect on highly polished Nb samples. EP introduces high slope angle sloped-steps at grain boundaries. The magnetic field enhancement and superheating field suppression factors associated with such a geometry are calculated in the London theory. Despite the by-eye smoothness of electropolished Nb, such defects compromise the stability of the low-loss Meissner state, likely limiting the achievable peak accelerating fields in superconducting RF cavities. Finally, the impact of surface roughness on impurity diffusion is investigated which can link surface roughness to the effectiveness of heat treatments like low-temperature baking or nitrogen infusion in the vortex nucleation or hydride hypotheses. Surface roughness tends to decrease the effective dose of impurities as a result of the expansion of impurities into regions with greater internal angle. The effective dose of impurities can be protected by minimizing slope angles and step heights, ensuring uniformity.

Hryhorenko, Oleksandr [Thomas Jefferson National A↗
DOE OSTI2025

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, in this study, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets, and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM and cryo-ET as a high-throughput tool for structural biology and discovery.

Levy, Axel [Stanford Univ., CA (United States); SL↗
DOE OSTI2024

Concurrent Measurement of O 2 Production and Isoprene Emission During Photosynthesis: Pros, Cons and Metabolic Implications of Responses to Light, CO 2 and Temperature

Traditional leaf gas exchange experiments have focused on net CO 2 exchange (A net ). Here, using California poplar (Populus trichocarpa), we coupled measurements of net oxygen production (NOP), isoprene emissions and δ 18 O in O 2 to traditional CO 2 /H 2 O gas exchange with chlorophyll fluorescence, and measured light, CO 2 and temperature response curves. This allowed us to obtain a comprehensive picture of the photosynthetic redox budget including electron transport rate (ETR) and estimates of the mean assimilatory quotient (AQ = A net /NOP). We found that A net and NOP were linearly correlated across environmental gradients with similar observed AQ values during light (1.25 ± 0.05) and CO 2 responses (1.23 ± 0.07). In contrast, AQ was suppressed during leaf temperature responses in the light (0.87 ± 0.28), potentially due to the acceleration of alternative ETR sinks like lipid synthesis. A net and NOP had an optimum temperature (Topt) of 31°C, while ETR and δ 18 O in O2 (35°C) and isoprene emissions (39°C) had distinctly higher T opt . The results confirm a tight connection between water oxidation and ETR and support a view of light-dependent lipid synthesis primarily driven by photosynthetic ATP/NADPH not consumed by the Calvin–Benson cycle, as an important thermotolerance mechanism linked with high rates of (photo)respiration and CO 2 /O 2 recycling.

H218O labelling↗
DOE OSTI2025

Enabling Scientific Applications with Performance-Portability and High-Productivity for Multi-GPU Programming with JACC.Multi

This work bridges the gap between multi-GPU computing and high-productivity, performance-portable programming solutions. Our goal is to enhance scientific applications with a productive and portable solution—program once, deploy everywhere—for multi-GPU programming with no cost to programmability. To accomplish this, we implemented JACC.Multi, which is part of the Julia for ACCelerators (JACC) performance-portable framework. JACC. Multi is the only high-level, portable metaprogramming solution that targets multi-GPU environments and is integrated in a readily accessible programming language (e.g., Julia language). With transparent GPU-to-GPU communication, JACC. Multi is optimized for scientific application workloads and is portable for NVIDIA and AMD accelerators. For the evaluation, we use two modern multi-GPU systems: Hudson, which features two NVIDIA H100 Hopper GPUs per node, and Frontier, which features four AMD MI250X GPUs per node, each with two Graphics Compute Dies (GCDs) for a total of eight GCDs per node. Additionally, as part of the evaluation, we use JACC (one GPU), MPI+JACC, and JACC. Multi codes that implement well-known and widely used scientific algorithms/kernels such as the conjugate gradient algorithm and an explicit forward Euler solver that requires GPU-to-GPU communication. Overall, JACC. Multi codes achieve better performance than MPI+JACC codes and significant speedups over JACC (one GPU), with up to 1.9× on Hudson and 6× on Frontier.

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2025

Heating of Cs2Te photocathode via field emission and radiofrequency pulsed heating: Implication toward breakdown

The occurrence of radiofrequency (RF) breakdown limits operational electromagnetic gradients in accelerator structures. Experimental evidence often suggests that breakdown events are associated with temperature and dark current spikes on the surface of RF devices. In the past decade, there has been increased interest in unveiling the mechanism behind breakdown initiation in metal copper and copper alloys; however, efforts regarding a breakdown phenomenon in photocathode-relevant semiconductors have been more limited. In this work, we explore field-emission-assisted heating via Nottingham and Joule processes, as a possible candidate for breakdown initiation. For this, field emission from an intrinsic Cs2Te ultrathin film coated on a copper substrate was modeled within the Stratton–Baskin–Lvov–Fursey formalism, describing the processes and effects in the bulk and on the surface of a photocathode exposed to high RF electromagnetic fields. It is shown that a field-emission characteristic deviates significantly from the classical Fowler–Nordheim (FN) theory, whereby predicting that dark current is orders of magnitude lower than the one expected by FN law. Conventional pulsed heating was also found to impose negligible heating to the photocathode. Both conclusions suggest that a Cs2Te photocathode coated on a metal substrate would be insensitive to catastrophic thermal-material runaway breakdown, unlike what is observed for metal surfaces. Finally, a few unconventional breakdown candidate scenarios are identified and discussed, including thermoelastic deformation and avalanche breakdown.

Shinohara, Ryo (ORCID:0000000276995732)↗
DOE OSTI2026

How Does Water Dissociation Work in Bipolar Membranes?

Bipolar membranes (BPMs) create counteracting spatial gradients of pH and electrostatic potential in electrochemical systems, enabling applications in pH regulation, electrocatalysis, and separations. At the polarized junction of a BPM the water dissociation (WD, 2H2O ⇌ H3O+ + OH-) reaction can be driven, but it remains poorly understood. In this Perspective, we integrate molecular insights from bulk-water autoionization and the associated field effects with continuum descriptions of BPM electrostatics and experimental WD kinetic analyses to describe possible mechanisms of voltage-driven WD. Pristine BPM junctions highlight both the limits of primarily electric-field-driven WD and the practical challenges of junction stability at extreme reverse bias. Introducing heterogeneous catalyst layers, commonly metal oxides and graphene oxides, accelerates WD by orders of magnitude through hypothesized coupled effects in which surface acid-base functionality and high-density hydroxyl sites mediate proton-transfer steps, and catalyst mobile electronic/ionic charges redistribute the junction electric potential drop to shape the local electric fields and reactive microenvironments. Kinetic analyses suggest two regimes of heterogeneous WD mechanism, including field-driven ordering of interfacial water and a Second-Wien-Effect dissociation-barrier lowering. We conclude by defining the key unknown variables (local pH, electrostatic potential, catalyst charge state and relationships among mechanisms) and outlining experimental and multiscale modeling strategies needed for predictive WD catalysis and for controlling related ion-transfer reactions.

Wu, Yifan↗
DOE OSTI2026

A dual dynamic shutter system for accelerating ion irradiation sample throughput via lateral gas implantation gradients

Ion irradiation for material performance testing is limited due to its serial nature, which allows for only one value of the implantation (appm) versus dose (dpa) parameter space to be explored for each ion and experiment at a time. While ion irradiation can accelerate the process by up to three orders of magnitude compared to neutron irradiation experiments, the sample throughput for ion irradiation remains relatively low. To address these limitations, a novel capability has been developed at the Michigan Ion Beam Laboratory (MIBL), enabling for the creation of single- and two-dimensional lateral ion implantation gradients using recently installed motorized-controlled ion-beam shutters. This advancement can generate a wide scope of the two-dimensional (H+, He2+) implantation parameter space within a single sample. Integration of this new capability now allows for dual- and triple-ion beam experiments to be performed with full user control over not only the ion implantation depth, but also laterally across the sample by imposing ion implantation concentration gradients, thus providing researchers with a high-throughput means for material testing under various irradiation conditions. Furthermore, recent improvements in MIBL's microbeam ion-beam analysis (IBA) target station now allow for probing these concentration gradients in irradiated alloys with exceptional spatial resolution, down to 10 µm. These two approaches promise to significantly improve ion irradiation capabilities and increase the sample throughput by several orders of magnitude. The application of the shutter technique plus the subsequent microbeam characterization of the imposed implantation gradients are showcased by two proof-of-principle ion-irradiated experiments, one performed on single-crystal Si and the other on the fusion-candidate alloy F82H-IEA. These advancements mark a substantial leap in ion-beam technology, offering researchers a robust, high-throughput method to efficiently investigate candidate alloys with high technological readiness for both advanced fission and fusion reactor applications, in a time- and cost-effective manner.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Horizontal Splitter Design For FFA@CEBAF Energy Upgrade: Current Status

Thomas Jefferson National Accelerator Facility (Jefferson Lab) is currently studying the feasibility of an energy upgrade based upon Fixed-Field Alternating Gradient (FFA) permanent magnet technology. The current plan is to replace the highest-energy recirculation arcs with FFA arcs, increasing the total number of beam recirculations, thus the energy. In order to accommodate multiple passes in the FFA arcs, horizontal splitters are being designed to control the beam parameters entering the FFA arcs, as well as the time of flight and R_56. In the current design, six passes will recirculate through the FFA arcs, necessitating the design of six independent beamlines to control the optics and beam dynamics matching into the arcs. These beamlines must fit into the current CEBAF tunnel, while allowing for personnel and equipment access. They must also be flexible enough to accommodate the beam under realistic operational conditions and fluctuations. The constraints on the system are highly restrictive, complicating the design. This document will describe the current state of the design, and indicate the work remaining for a complete conceptual design.

Benesch, Jay↗
DOE OSTI2026

Status of 22 GeV CEBAF Upgrade Using FFA Arcs

Jefferson Lab is exploring an upgrade to extend CEBAF’s energy to ~22 GeV within the existing tunnel by incorporating Fixed-Field Alternating-Gradient (FFA) arcs. In this scheme, the two lowest-energy electromagnetic arcs are removed, the remaining arcs are reassigned to lower energies, and a new pair of high-energy FFA arcs enables six additional recirculations using the existing SRF linacs. The non-scaling FFA lattice employs Halbach-derived permanent magnets with provisions for dipole, quadrupole, and higher-order components, offering large momentum acceptance and reduced operating cost. Implementing this concept requires updated linac optics, modifications to the remaining arcs, and a redesigned electromagnetic switchyard. Ongoing beam-dynamics studies address synchrotron-radiation–induced energy loss and emittance growth, along with strategies for their mitigation.

Ogur, S. [Thomas Jefferson National Accelerator Fa↗
DOE OSTI2024

Accelerating CO2 Storage Site Characterization through a New Understanding of Favorable Formation Properties and the Impact of Core-Scale Heterogeneities

CO2 sequestration in deep geologic formations can permanently reduce atmospheric CO2 emissions and help to abate climate change. Target formations must undergo a time- and resource-intensive site evaluation process, assessing storage capacity, environmental safety, and suitability for CO2 trapping via reactive transport models based on data from a limited number of core samples. As such, simulations are often simplified and omit heterogeneities in formation properties that may be significant but are not well understood. To facilitate more rapid site assessment, this work first defines the aquifer properties of favorable storage formations through the analysis of promising and active storage sites. Data show quartz is the most prevalent formation mineral with carbonate minerals, highly reactive with injected CO2, present in over 75% of formations. Porosity and permeability data are highly clustered at 10–30% and 10–1000 mD. Field-scale reactive transport simulations are then constructed and used to analyze CO2 trapping efficiency. The models consider porosity and carbonate mineral heterogeneity as well as the impacts of typical temperature gradients. Simulated sequestration efficiencies are compared to results from a comparable homogenous model to understand the implications of aquifer non-uniformities. The results show a lower sequestration efficiency in the homogeneous model during the injection phase. During the post-injection phase, the homogenization of porosity and carbonate mineralogy results in a higher sequestration efficiency. Incorporating the temperature gradient also increases the sequestration efficiency. Importantly, the maximum deviation between the homogeneous and heterogeneous simulations at the end of the 50-year study period is only ~10%. Larger impacts may be incurred for properties outside the defined, promising ranges suggested here.

Environmental Sciences & Ecology↗
DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗
DOE OSTI2024

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2026

Organo-mineral interactions in active layer and permafrost soils along aging Arctic landscapes

Rising temperatures are accelerating permafrost thaw, exposing large soil organic carbon (SOC) stocks to microbial decomposition with implications for global climate. Understanding how permafrost carbon is stored and protected through associations with minerals is critical for predicting its vulnerability to decomposition upon thaw. However, how landscape age, substrate chemistry, and soil depth influence mineral associations remain relatively unexplored. We investigated organo-mineral associations in active layer and permafrost soils across a landscape age and geochemical gradient on Alaska’s North Slope, spanning three glaciated (~11,500–125,000 years) and one unglaciated site. Using selective dissolution extractions, X-ray diffraction, and Mössbauer spectroscopy, we characterized minerals and their relationship with SOC. The three recently deglaciated sites had low soil pH that decreased with age and greater abundances of pyrophosphate- and oxalate-extractable Al and Fe, whereas the oldest unglaciated site exhibited near-neutral pH, greater pyrophosphate-extractable Ca, and distinct mineralogy. Across sites, SOC was positively associated with Al and Fe mineral phases, with stronger relationships in acidic soils. Pyrophosphate-extractable Ca also showed strong relationships with SOC at the acidic sites (up to ~10x greater), suggesting that Ca-mediated protection may operate beyond traditionally recognized high-pH soils. Permafrost soils showed depth-related changes in pH, SOC, and Fe mineralogy, suggesting chemically active, heterogeneous layers may shape mineral dynamics and associated carbon. Our results highlight how landscape age, parent material, and depth create distinct geochemical environments that govern mineral-organic associations. As thaw exposes soil to new conditions, these mineral-mediated protection mechanisms may be altered, potentially affecting the permafrost carbon-climate feedback.

Synthetic Biology↗
DOE OSTI2026

High-precision scale setting with the Ω-baryon mass and gradient flow

The gradient-flow scale 𝑤 0 in lattice QCD is determined using the mass of the Ω − baryon to set the physical scale. Nine ensembles using the highly improved staggered quark (HISQ) action with lattice spacings of 0.15 fm down to 0.04 fm are used, seven of which have nearly physical light-quark masses. Electromagnetic corrections to the Ω − mass are defined in order to compute a pure-QCD Ω mass. The final result is 𝑤 0 = 0.17187⁢(68) fm, corresponding to a relative uncertainty of 0.40% and a central value in good agreement with previous calculations in the literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Conductivity-Driven Origin of the Limiting Current in Concentrated Electrolytes

Next-generation electrolyte materials are hindered by their ability to support high currents essential for fast-charge and high-power battery applications. The maximum current supported by an electrolyte, the limiting current, is dictated by the formation of concentration gradients across the electrolyte under an electric field. Most of the literature attributes the onset of the limiting current in concentrated electrolytes to the salt concentration at the positive electrode approaching the solubility limit. Here, in this study, we leverage operando X-ray transmission imaging to measure spatiotemporal salt concentration profiles of a polymer electrolyte in a lithium–indium symmetric cell at a current exceeding the limiting current. The measurement of concentration profiles enables mapping the spatiotemporal electric potential, which comprises an ohmic contribution, governed by conductivity, and an overpotential related to maintaining concentration gradients. We find that a precipitous drop in conductivity at the positive electrode drives the divergence of electric potential, rather than a thermodynamic solubility limit.

Abdo, Emily E. [University of California, Berkeley↗