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Highlights • Two concepts naturally capture capillary pressure hysteresis with arbitrary cycling. • Accessivity describes connectivity between different sized pores. • Radius-resolved saturation, unlike saturation, gives pore-scale fluid distribution. • Conceptual framework has broader utility in continuum modeling of porous media. Abstract Continuum models of porous media use macroscopic parameters and state variables to capture essential features of pore-scale physics. We propose a macroscopic property "accessivity" (α) to characterize the network connectivity of different sized pores in a porous medium, and macroscopic state descriptors "radius-resolved saturations" ( ψ w (F) , ψ n (F)) to characterize the distribution of fluid phases within. Small accessivity (α → 0) implies serial connections between different sized pores, while large accessivity (α → 1) corresponds to more parallel arrangements, as the classical capillary bundle model implicitly assumes. Based on these concepts, we develop a statistical theory for quasistatic immiscible drainage-imbibition in arbitrary cycles, and arrive at simple algebraic formulae for updating ψ n F that naturally capture capillary pressure hysteresis, with α controlling the amount of hysteresis. These concepts may be used to interpret hysteretic data, upscale pore-scale observations, and formulate new constitutive laws by providing a simple conceptual framework for quantifying connectivity effects, and may have broader utility in continuum modeling of transport, reactions, and phase transformations in porous media. [ABSTRACT FROM AUTHOR]

The global devastation of the COVID-19 pandemic has led to calls for a revolution in heating, ventilation, and air conditioning (HVAC) systems to improve indoor air quality (IAQ), due to the dominant role of airborne transmission in disease spread. While simple guidelines have recently been suggested to improve IAQ mainly by increasing ventilation and filtration, this goal must be achieved in an energy-efficient and economical manner and include all air cleaning mechanisms. Here, we develop a simple protocol to directly, quantitatively, and optimally control transmission risk while minimizing energy cost. We collect a large dataset of HVAC and IAQ measurements in buildings and show how models of infectious aerosol dynamics and HVAC operation can be combined with sensor data to predict transmission risk and energy consumption. Using this data, we also verify that a simple safety guideline is able to limit transmission risk in full data-driven simulations and thus may be used to guide public health policy. Our results provide a comprehensive framework for quantitative control of transmission risk using all available air cleaning mechanisms in an indoor space while minimizing energy costs to aid in the design and automated operation of healthy, energy-efficient buildings. • Quantitatively connect airborne transmission with HVAC control on real data. • Validate simple, pseudo-steady airborne transmission safety guidelines on real data. • Quantify impact of equivalent outdoor air sources on infection risk and energy use. • Develop HVAC control strategy to directly control airborne infection risk. • Transmission-controlled ventilation offers optimal control protocol. [ABSTRACT FROM AUTHOR]

We present in situ environmental transmission electron microscopy (ETEM) observation of metallic lithium nucleation, growth and shrinkage in a liquid confining cell, where protrusions are seen to grow from their roots or surfaces, depending on the overpotential. The rate of solid-electrolyte interface (SEI) formation affects root vs. surface growth mode, with the former akin to intermittent volcanic eruptions, giving kinked segments of nearly constant diameter. Upon delithiation, root-grown whiskers are highly unstable, because the segmental shrinkage rate depends on Li + transport across SEI, which is the greatest around the latest grown segment with the thinnest SEI, and therefore the near-root segment often dissolves first and the rest of the whisker then loses electrical contact. These electrically isolated dead lithium branches are also easily swept away into the bulk electrolyte to become “nano-lithium flotsam” because the hollowed-out SEI tube is very brittle. Our observations are consistent with SEI-obstructed growth by two competing mechanisms; surface growth of dense Eden-like clusters and root growth of whiskers, resulting from the voltage-dependent competition between lithium electrodeposition and SEI formation reactions. Similar phenomena could occur whenever chemical deposition/dissolution competes with irreversible side reactions that form a passivating layer on the evolving surface. [ABSTRACT FROM AUTHOR]

Lithium-air batteries have been considered as ultimate solutions for the power source of long-range electrified transportation, but state-of-the-art prototypes still suffer from short cycle life, low efficiency and poor power output. Here, a lithium-bromine rechargeable fuel cell using highly concentrated bromine catholytes is demonstrated with comparable specific energy, improved power density, and higher efficiency. The cell is similar in structure to a hybrid-electrolyte Li-air battery, where a lithium metal anode in nonaqueous electrolyte is separated from aqueous bromine catholytes by a lithium-ion conducting ceramic plate. The cell with a flat graphite electrode can discharge at a peak power density around 9 mW cm −2 and in principle could provide a specific energy of 791.8 Wh kg −1 , superior to most existing cathode materials and catholytes. It can also run in the regenerative mode to recover the lithium metal anode and free bromine with 80-90% voltage efficiency, without any catalysts. Degradation of the solid electrolyte and the evaporation of bromine during deep charging are challenges that should be addressed in improved designs to fully exploit the high specific energy of the liquid bromine. The proposed system offers a potential power source for long-range electric vehicles, beyond current Li-ion batteries yet close to envisioned Li-air batteries. [ABSTRACT FROM AUTHOR]

Porous electrodes composed of multiphase active materials are widely used in Li-ion batteries, but their dynamics are poorly understood. Two-phase models are largely empirical, and no models exist for three or more phases. Using a modified porous electrode theory based on non-equilibrium thermodynamics, we show that experimental phase behavior can be accurately predicted from free energy models, without artificially placing phase boundaries or fitting the open circuit voltage. First, we simulate lithium intercalation in porous iron phosphate, a popular two-phase cathode, and show that the zero-current voltage gap, sloping voltage plateau and under-estimated exchange currents all result from size-dependent nucleation and mosaic instability. Next, we simulate porous graphite, the standard anode with three stable phases, and reproduce experimentally observed fronts of color-changing phase transformations. These results provide a framework for physics-based design and control for electrochemical systems with complex thermodynamics. [ABSTRACT FROM AUTHOR]

Abstract: Electrochemical impedance spectra for battery electrodes are usually interpreted using models that assume isotropic active particles, having uniform current density and symmetric diffusivities. While this can be reasonable for amorphous or polycrystalline materials with randomly oriented grains, modern electrode materials increasingly consist of highly anisotropic, single-crystalline, nanoparticles, with different impedance characteristics. In this paper, analytical expressions are derived for the impedance of anisotropic particles with tensorial diffusivities and orientation-dependent surface reaction rates and capacitances. The resulting impedance spectrum contains clear signatures of the anisotropic material properties and aspect ratio, as well as statistical variations in any of these parameters. [Copyright &y& Elsevier]

Abstract: The field of nonlinear “induced-charge” electrokinetics is rapidly advancing, motivated by potential applications in microfluidics as well as by the unique opportunities it provides for probing fundamental scientific issues in electrokinetics. Over the past few years, several surprising theoretical predictions have been observed in experiments: (i) induced-charge electrophoresis of half-metallic Janus particles, perpendicular to a uniform AC field; (ii) microfluidic mixing around metallic structures by induced-charge electro-osmosis, and (iii) fast, high-pressure AC electro-osmotic pumping by non-planar electrode arrays, and ICEK effects upon the collective behavior of polarizable particle suspensions has been studied theoretically and computationally. A new experimental system enables a clean and direct comparison between theoretical predictions and measured ICEK flows, providing a route to fundamental studies of particular surfaces and high-throughput searches for optimal ICEK systems. Systematic discrepancies between theory and experiment have engendered the search for mechanisms, including new theories that account for electrochemical surface reactions, surface contamination, roughness, and the crowding of ions at high voltage. Promising directions for further research, both fundamental and applied, are discussed. [Copyright &y& Elsevier]

Abstract: The diffusion and flow of amorphous materials, such as glasses and granular materials, has resisted a simple microscopic description, analogous to defect theories for crystals. Early models were based on either gas-like inelastic collisions or crystal-like vacancy diffusion, but here we propose a cooperative mechanism for dense random-packing dynamics, based on diffusing “spots” of interstitial free volume. Simulations with the Spot Model can efficiently generate realistic flowing packings, and yet the model is simple enough for mathematical analysis. Starting from a non-local stochastic differential equation, we derive continuum equations for tracer diffusion, given the dynamics of free volume (spots). Throughout the paper, we apply the model to granular drainage in a silo, and we also briefly discuss glassy relaxation. We conclude by discussing the prospects of spot-based multiscale modeling and simulation of amorphous materials. [Copyright &y& Elsevier]

A generalization of the Renormalization Group, which describes order-parameter fluctuations in finite systems, is developed in the specific context of percolation. This “Stochastic Renormalization Group” (SRG) expresses statistical self-similarity through a non-stationary branching process. The SRG provides a theoretical basis for analytical or numerical approximations, both at and away from criticality, whenever the correlation length is much larger than the lattice spacing (regardless of the system size). For example, the SRG predicts order-parameter distributions and finite-size scaling functions for the complete crossover between phases. For percolation, the simplest SRG describes structural quantities conditional on spanning, such as the total cluster mass or the minimum chemical distance between two boundaries. In these cases, the Central Limit Theorem (for independent random variables) holds at the stable, off-critical fixed points, while a “Fractal Central Limit Theorem” (describing long-range correlations) holds at the unstable, critical fixed point. This first part of a series of articles explains these basic concepts and a general theory of crossover. Subsequent parts will focus on limit theorems and comparisons of small-cell SRG approximations with simulation results. [Copyright &y& Elsevier]

The COVID-19 pandemic has renewed interest in assessing how the operation of HVAC systems influences the risk of airborne disease transmission in buildings. Various processes, such as ventilation and filtration, have been shown to reduce the probability of disease spread by removing or deactivating exhaled aerosols that potentially contain infectious material. However, such qualitative recommendations fail to specify how much of these or other disinfection techniques are needed to achieve acceptable risk levels in a particular space. An additional complication is that application of these techniques inevitably increases energy costs, the magnitude of which can vary significantly based on local weather. Moreover, the operational flexibility available to the HVAC system may be inherently limited by equipment capacities and occupant comfort requirements. Given this knowledge gap, we propose a set of dynamical models that can be used to estimate airborne transmission risk and energy consumption for building HVAC systems based on controller setpoints and a forecast of weather conditions. By combining physics-based material balances with phenomenological models of the HVAC control system, it is possible to predict time-varying airflows and other HVAC variables, which are then used to calculate key metrics. Through a variety of examples involving real and simulated commercial buildings, we show that our models can be used for monitoring purposes by applying them directly to transient building data as operated, or they may be embedded within a multi-objective optimization framework to evaluate the tradeoff between infection risk and energy consumption. By combining these applications, building managers can determine which spaces are in need of infection risk reduction and how to provide that reduction at the lowest energy cost. The key finding is that both the baseline infection risk and the most energy-efficient disinfection strategy can vary significantly from space to space and depend sensitively on the weather, thus underscoring the importance of the quantitative predictions provided by the models. [ABSTRACT FROM AUTHOR]

Capacitive deionization (CDI) for water treatment, which relies on the capture of charged species to sustain the electrical double layers (EDLs) established within porous electrodes under an applied electrical potential, can be enhanced by the chemical attachment of fixed charged groups to the porous electrode electrodes (ECDI). It has recently been demonstrated that further improvements in capacity and energy storage can be gained by functionalization of the electrode surfaces with redox polymers in which the charge on the electrodes can be modulated through Faradaic reactions under different cell voltages in a capacitive process that can be called “Faradaic CDI” (FaCDI). Here, we extend recent mathematical models developed for the characterization of CDI and ECDI systems to incorporate the redox mediated contributions by allowing for the variable chemical charges generated by reactions in FaCDI. The lumped model developed here assumes the spacer channel is well-mixed with uniform electrosorption in each electrode. We demonstrate that the salt adsorption performance characterization of the fixed chemical charge ECDI and variable chemical charge FaCDI materials can be unified within a common theoretical framework based on the point of zero charge (PZC) of the electrode material. In the latter case the PZC is determined by the equilibrium potentials of the redox couples immobilized on the porous electrodes. The new model is able to predict the experimentally observed enhanced and inverted performance of CDI cells, and illuminates the benefit of choosing redox active materials for water treatment applications. The deionization performance of FaCDI cells is shown to be superior to that of CDI and ECDI systems with equilibrium adsorption capacities 50–100% higher than attained with CDI systems, and at smaller cell voltages, depending on the redox potentials of the Faradaic moieties. [ABSTRACT FROM AUTHOR]

Abstract: When assembling lithium-ion cells into functional battery packs, it is common to connect multiple cells in parallel. Here we present experimental and modeling results demonstrating that, when lithium ion cells are connected in parallel and cycled at high rate, matching of internal resistance is important in ensuring long cycle life of the battery pack. Specifically, a 20% difference in cell internal resistance between two cells cycled in parallel can lead to approximately 40% reduction in cycle life when compared to two cells parallel-connected with very similar internal resistance. We show that an internal resistance mismatch leads to high current into each cell during part of the charging cycle. Since capacity fading is strongly dependent on temperature, and hence on charging rate when this rate is sufficiently high, the high current leads to substantially accelerated capacity fade in both cells. A model, based on the formation of a solid-electrolyte interphase, is able to explain the dependence of lifetime on resistance mismatch, and also identifies the importance of random sudden capacity losses. [Copyright &y& Elsevier]

Abstract: A general continuum theory is developed for ion intercalation dynamics in a single crystal of rechargeable-battery composite electrode material. It is based on an existing phase-field formulation of the bulk free energy and incorporates two crucial effects: (i) anisotropic ionic mobility in the crystal and (ii) surface reactions governing the flux of ions across the electrode/electrolyte interface, depending on the local free energy difference. Although the phase boundary can form a classical diffusive “shrinking core” when the dynamics is bulk-transport-limited, the theory also predicts a new regime of surface-reaction-limited (SRL) dynamics, where the phase boundary extends from surface to surface along planes of fast ionic diffusion, consistent with recent experiments on LiFePO4. In the SRL regime, the theory produces a fundamentally new equation for phase transformation dynamics, which admits traveling-wave solutions. Rather than forming a shrinking core of untransformed material, the phase boundary advances by filling (or emptying) successive channels of fast diffusion in the crystal. By considering the random nucleation of SRL phase-transformation waves, the theory predicts a very different picture of charge/discharge dynamics from the classical diffusion-limited model, which could affect the interpretation of experimental data for LiFePO4. [Copyright &y& Elsevier]

We present a Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET) model that accurately predicts the performance of Medtronic's implantable medical device battery lithium/carbon monofluoride (C F x) - silver vanadium oxide (SVO) under both low-rate background monitoring and high-rate pulsing currents. The distinct properties of multiple active materials are reflected by parameterizing their thermodynamics, kinetics, and mass transport properties separately. Diffusion limitations of Li + in SVO are used to explain cell voltage transient behavior during pulse and post-pulse relaxation. We also introduce change in cathode electronic conductivity, Li metal anode surface morphology, and film resistance buildup to capture evolution of cell internal resistance throughout multi-year electrical tests. We share our insights on how the Li + redistribution process between active materials can restore pulse capability of the hybrid electrode, allow C F x to indirectly contribute to capacity release during pulsing, and affect the operation protocols and design principles of batteries with other hybrid electrodes. We also discuss additional complexities in porous electrode model parameterization and electrochemical characterization techniques due to parallel reactions and solid diffusion pathways across active materials. We hope our models can complement future experimental research and accelerate development of multi-active material electrodes with targeted performance. • Hybrid-MPET model of Medtronic's high-rate Li/CF x -SVO battery is presented. • Separate material properties, diffusion limitation, and aging are accounted for. • Model prediction accuracy is validated on large battery dataset. • Cell pulse capability restoration is explained by Li＋ redistribution across materials. • Li＋ redistribution impact on general cell operation and design principles are discussed. [ABSTRACT FROM AUTHOR]

Medtronic Implantable Cardioverter Defibrillators (ICDs) and Cardiac Resynchronization Therapy Defibrillators (CRT-Ds) rely on high-energy density, lithium batteries, which are manufactured with a special lithium/carbon monofluoride (C F x)–silver vanadium oxide (SVO) hybrid cathode design. Consistently high battery performance is crucial for this application, since poor performance may result in ineffective patient treatment, whereas early replacement may involve surgery and increase in maintenance costs. To evaluate performance, batteries are tested, both at the time of production and post-production, through periodic sampling carried out over multiple years. This considerable amount of experimental data is exploited for the first time in this work to develop a data-driven, machine learning approach, relying on Generalized Additive Models (GAMs) to predict battery performance, based on production data. GAMs combine prediction accuracy, which enables evaluation of battery performance immediately after production, with model interpretability, which provides clues on how to further improve battery design and production. Model interpretation allows to identify key features from the battery production data that offer physical insights to support future battery development, and foster the development of physics-based model for hybrid cathode batteries. The proposed approach is validated on 21 different datasets, targeting several performance-related features, and delivers consistently high prediction accuracy on test data. • ICD battery reliability is ensured with life-test experiments spanning multiple years. • ML provides accurate prediction of life-test experiments based on production data. • Interpretable ML fosters the development of battery design and physics-based models. • Approach is validated on 21 datasets, analysed for the first time in the literature. [ABSTRACT FROM AUTHOR]

Electrochemically mediated adsorption is an emerging technology that utilizes redox active (or Faradaic) materials and has exhibited high salt adsorption capacity and superb ion selectivity. Here, we use a redox polymer polyvinylferrocene (PVFc) as the anode and a conducting polymer polypyrrole doped with a large anionic surfactant (pPy-DBS) as the cathode for selective electrochemical removal of inorganic and organic components. We fabricated a flow system with alternating adsorption/desorption steps incorporating an electrosorption cell and inline probes (ultraviolet–visible spectroscopy, conductivity and pH sensors) to demonstrate on-the-fly quantification of the ion adsorption performance. The flow system provides a more realistic evaluation of dynamic selectivity for the active materials during cyclic operation than that based on a single equilibrium adsorption step in batch. Our results show a three-fold (cycle) selectivity toward the removal of benzoate, as a representative organic anion, against a 50-fold abundance of perchlorate supporting anion, indicating that electrochemically mediated adsorption is a promising technology for waste water remediation applications. Image 1 • Redox-active electrodes for electrochemically-mediated selective adsorption. • Design and fabrication of a flow platform with multiple inline detectors. • Ultra-high salt adsorption rate of 5.6 mg g−1 min−1. • Selective removal of benzoate as a model compound of toxic carboxylate pollutants. • Selectivity of 3 in presence of highly abundant supporting anions (50-fold). [ABSTRACT FROM AUTHOR]

The design of fast charging protocols is fundamental to improving the performance and lifetime of lithium-ion batteries. It is well-known that charging operations consistently performed at very high current will negatively impact operational safety and battery lifetime, although a quantitative understanding of these relationships remains lacking. The protocol design problem is typically formulated as a model-based dynamic optimization, where safety of operations can be encoded by constraining relevant battery states. However, all models are affected by uncertainty, which in turn propagates to state predictions. In this case, charging protocols based on nominal predictions may not satisfy the operating constraints. To overcome this issue, this work proposes a stochastic optimal control approach for the efficient computation of safe, fast charging protocols, able to explicitly account for parametric uncertainties affecting the battery model and guarantee probabilistically robust constraint satisfaction. Given a description of uncertainty affecting model parameters, linearized sensitivity analysis is exploited to propagate uncertainty to the battery states, and suitable backoff values for safety constraints are computed for each time instant. The effectiveness of the methodology is demonstrated in silico, by computing five different protocols, with a detailed Multiphase Porous Electrode Theory-based model of commercially available lithium-iron-phosphate batteries. • A stochastic optimal control method is proposed for design of safe, fast protocols. • The method explicitly accounts for uncertainties in model parameters. • Uncertainties are propagated to the battery states using sensitivity analysis. • Backoff values for safety constraints are satisfied at each time instance. • The methodology is demonstrated for lithium-ion phosphate batteries. [ABSTRACT FROM AUTHOR]

High-dimensional linear regression is important in many scientific fields. This article considers discrete measured data of underlying smooth latent processes, as is often obtained from chemical or biological systems. Interpretation in high dimensions is challenging because the nullspace and its interplay with regularization shapes regression coefficients. The data's nullspace contains all coefficients that satisfy Xw = 0 , thus allowing very different coefficients to yield identical predictions. We developed an optimization formulation to compare regression coefficients and coefficients obtained by physical engineering knowledge to understand which part of the coefficient differences are close to the nullspace. This nullspace method is tested on a synthetic example and lithium-ion battery data. The case studies show that regularization and z-scoring are design choices that, if chosen corresponding to prior physical knowledge, lead to interpretable regression results. Otherwise, the combination of the nullspace and regularization hinders interpretability and can make it impossible to obtain regression coefficients close to the true coefficients when there is a true underlying linear model. Furthermore, we demonstrate that regression methods that do not produce coefficients orthogonal to the nullspace, such as fused lasso, can improve interpretability. In conclusion, the insights gained from the nullspace perspective help to make informed design choices for building regression models on high-dimensional data and reasoning about potential underlying linear models, which are important for system optimization and improving scientific understanding. • The impact of the nullspace on the regression coefficients is assessed. • An optimization formulation is proposed for the nullspace analysis. • Regression coefficients not orthogonal to the nullspace can be more interpretable. • Regularization and z-scoring are design choices that affect interpretability. • The nullspace analysis leads to insights into lithium-ion battery degradation. [ABSTRACT FROM AUTHOR]

Fast charging is a desirable feature for lithium-ion batteries. Charging at high currents, however, can damage the battery and accelerate aging processes. Fast charging protocols are typically computed by solving an optimization in which the cost function and constraints encode the conflicting requirements of safety and speed. A key element of the optimization is the choice of the dynamic model of the battery, with an inherent tradeoff between model accuracy and computational complexity. An oversimplified model may result in unreliable protocols, whereas a complex model may result in an optimization that is too computationally expensive to be suitable for real-time applications. This article describes an approach for embedding a complex battery model into charging optimization while having low computational cost. Multiphase Porous Electrode Theory is used to provide an accurate description of batteries characterized by multiphase materials, and the optimization is solved by transformation into mixed discrete-continuous simulation of a set of Differential–Algebraic Equations. The methodology is applied to an MPET model of commercially available Lithium Iron Phosphate batteries. Protocols based on a variety of operational constraints are computed to assess both the effectiveness of the approach, and the advantages and disadvantages of the charging protocols. • Fast charging protocols designed for multiphase batteries. • Computationally efficient protocol design by solving as a hybrid simulation. • Protocols designed for commercial lithium-ion batteries. • Protocols minimize charging time, subject to safety and operational constraints. • First such analysis based on Multiphase Porous Electrode Theory. [ABSTRACT FROM AUTHOR]

Porous electrode theory (PET) is widely used to model battery dynamics by describing electrochemical kinetics and transport in solid particles and electrolyte. Standard PET models rely on black-box descriptions of the thermodynamics of active materials, typically obtained by fitting an open-circuit potential which does not allow for a consistent description of phase-separating materials. Multiphase PET (MPET) was recently developed to describe batteries using white- or gray-box descriptions of the thermodynamics with additional parameters that need to be estimated from experimental data. This work analyzes the identifiability of parameters in the MPET model, including the standard kinetics and diffusion parameters, as well as MPET-specific parameters for the free energy of active materials. Based on synthetic discharge data, both linearized and nonlinear identifiability analyses are performed for an MPET model of a commercial Lithium Iron Phosphate/Graphite battery, which identify which model parameters are not identifiable and which are identifiable only with large uncertainty. The identifiable parameters control phase separation, reaction kinetics, and electrolyte transport, but not solid diffusion, consistent with rate limitation by intercalation reactions at low rates and by electrolyte diffusion at high rates. The article also proposes approaches for reducing parameter identifiability issues. • First identifiability analysis for Multiphase Porous Electrode Theory-based models. • The analysis is carried out for discharge data from a lithium iron phosphate battery. • The analysis identifies which parameters cannot be estimated from the data. • The lack of identifiability is explained in terms of the battery physics. • Approaches are proposed for removing the lack of parameter identifiability. [ABSTRACT FROM AUTHOR]

Solid-state programmable metallization cells have attracted considerable attention as memristive elements for Redox-based Resistive Random Access Memory (ReRAM) for low-power and low-voltage applications. In principle, liquid-state metallization cells could offer the same advantages for aqueous systems, such as biomedical lab-on-a-chip devices, but robust resistive switching has not yet been achieved in liquid electrolytes, where electrodeposition is notoriously unstable to the formation of fractal dendrites. Here, the recently discovered physics of shock electrodeposition are harnessed to stabilize aqueous copper growth in polycarbonate nanopores, whose surfaces are modified with charged polymers. Stable bipolar resistive switching is demonstrated for 500 cycles with <10 s retention times, prior to any optimization of the geometry or materials. [ABSTRACT FROM AUTHOR]

A physics-based, reduced order framework is developed to calculate the charge capacity loss contributions from spatially homogeneous and heterogeneous degradation mechanisms, chemomechanical cycling and initial capacity recovery. The formulation goes well beyond prevalent coulomb-counting models and is tuned solely based on experimentally measurable parameters, although it neglects any buildup of internal resistance. The model is compared against the largest data set available to date for commercial LiFePO 4 -graphite cells and shows less than 10% error for 92% of the cells. Results suggest that in most cells the charge capacity increases through the first ∼ 50 cycles, beyond which homogeneous SEI growth dominates capacity loss up to ∼ 500 cycles. Above ∼ 500 cycles and at high current densities, the model attributes capacity loss primarily to heterogeneous SEI growth proceeding at microstructurally favored locations, further assisted by chemomechanical failure of the graphite anode particles towards the end of cell life. The developed model sets the stage for on-the-fly capacity loss calculations in hybrid and electric vehicles, especially at low currents and constant voltage holds and could be extended to capture the deleterious effects of high current densities in fast-charging scenarios by including resistive losses. • A physics-based, reduced order framework is developed to compute charge capacity loss. • Contributions from 5 degradation mechanisms for anode and cathode were identified. • The model shows less than 10% error for 92% of a 127-cell dataset. [ABSTRACT FROM AUTHOR]

Abstract: One dimensional theory of the electro-diffusion of ions in activated carbon porous electrodes is applied to describe the dynamic cycle of the capacitive mixing (CAPMIX) based on the double layer expansion (CDLE) technique to harvest renewable energy from salinity gradients. The model combines the electro-diffusion of ions with adsorption and desorption of charge and neutral salt into the double layers at the solid liquid interface, providing a comprehensive and accurate description of the full CAPMIX cycle experimentally measured in prototype cells. A careful analysis of the simulated cycles identifies key parameters for the optimization of the extracted power, like the appropriate thickness and micro-structure of the electrodes, best materials and operation conditions of the electrochemical cell. These directions will be fundamental in the development of this technique as an economically competitive renewable energy source. [Copyright &y& Elsevier]

[Display omitted] • A machine learning strategy is proposed for optimizing rapid charging protocols. • The strategy explicitly includes constraints that limit battery degradation. • The approach converges more quickly than other published optimization strategies. • The performance is quantified for varying number of current steps in the protocol. • Three CC-step protocol charged 126.6 s and 14.0 s faster than the one and two CC-step protocols. Lithium-ion batteries are one of the most commonly used energy storage device for electric vehicles. As battery chemistries continue to advance, an important question concerns how to efficiently determine charging protocols that best balance the desire for fast charging while limiting battery degradation mechanisms which shorten battery lifetime. Challenges in this optimization are the high dimensionality of the space of possible charging protocols, significant variability between batteries, and limited quantitative information on battery degradation mechanisms. Current approaches to addressing these challenges are model-based optimization and grid search. Optimization based on electrochemical models is limited by uncertainty in the underlying battery degradation mechanisms and grid search methods are expensive in terms of time, testing equipment, and cells. This article proposes a fast-charging Bayesian optimization strategy that explicitly includes constraints that limit degradation. The proposed BO-based charging approaches are sample-efficient and do not require first-principles models. Three different types of acquisition function (i.e., expected improvement, probability of improvement, and lower confidence bound) are evaluated. Their efficacies are compared for exploring and exploiting the parameter space of charging protocols for minimizing the charging time for lithium-ion batteries described by porous electrode theory. The probability-of-improvement acquisition function has lower mean and best minimum charging times than the lower-confidence-bound and expected-improvement acquisition functions. We quantify the decrease in the minimum charging time and increase in its uncertainty with increasing number of current steps used in charging protocols. Understanding ways to increase the convergence rate of Bayesian optimization, and how the convergence scales with the number of degrees of freedom in the optimization, serves as a baseline for extensions of the optimization to include battery design parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] Although lithium-metal anodes are being extensively examined in research projects aiming at pushing the energy density of lithium batteries to its limit, the knowledge about the mechanical properties of pure lithium is insufficient in two aspects. First, most of the available data focuses either on nano- and micro-scale single-crystalline lithium or on macro-scale bulk material. Second, those tests were commonly performed via uniaxial tests in which the stress states were simple or nanoindentation. This work aims at bridging these gaps by performing a systematic experimental program under various stress states on small-sized specimens and by developing a plasticity model that can capture the important characteristics. Based on these experimental and computational findings, the added value on the understanding of the deformation and failure mechanisms of lithium under various stress states and a first quantitative description on the plasticity anisotropy on lithium is provided. In order to manufacture the required complex-shaped specimens for the five different stress states (uniaxial tension, notched tension with two different radii, central hole tension, and simple shear), a method which allows safe laser cutting of thick lithium foil in argon atmosphere is developed. The tensile tests are conducted in pure argon as well as in air to quantify the effect of oxidation on the strength of lithium. By means of post-mortem microstructural examinations, two active slip systems and cross-slip are observed. Lithium fractures in a perfectly ductile manner when the specimen thickness is reduced to zero due to localized necking. Digital image correlation analysis shows that the lithium foil is highly anisotropic in the through-thickness direction although it is in-plane isotropic. By using a rate-dependent transverse isotropic model, a satisfactory prediction of the five experiments is provided. [ABSTRACT FROM AUTHOR]

The microscopic theory of chemical reactions is based on transition state theory, where atoms or ions transfer classically over an energy barrier, as electrons maintain their ground state. Electron transfer is fundamentally different and occurs by tunneling in response to solvent fluctuations. Here, we develop the theory of coupled ion-electron transfer, in which ions and solvent molecules fluctuate cooperatively to facilitate non-adiabatic electron transfer. We derive a general formula of the reaction rate that depends on the overpotential, solvent properties, the electronic structure of the electron donor/acceptor, and the excess chemical potential of ions in the transition state. For Faradaic reactions, the theory predicts curved Tafel plots with a concentration-dependent reaction-limited current. For moderate overpotentials, our formula reduces to the Butler–Volmer equation and explains its relevance, not only in the well-known limit of large electron-transfer (solvent reorganization) energy, but also in the opposite limit of large ion-transfer energy. The rate formula is applied to Li-ion batteries, where reduction of the electrode host material couples with ion insertion. In the case of lithium iron phosphate, the theory accurately predicts the concentration dependence of the exchange current measured by in operando X-Ray microscopy without any adjustable parameters. These results pave the way for interfacial engineering to enhance ion intercalation rates, not only for batteries, but also for ionic separations and neuromorphic computing. [ABSTRACT FROM AUTHOR]