Engineering topics
Publications and source records attributed to Bazant, Martin Z..
We have developed a theory for the electrical double layer of salt-in-ionic liquids accounting for the thermoreversible association of ions into Cayley tree aggregates. We find that the solution becomes more aggregated at moderate positive voltages.
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.
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Electrochemical methods are known to have attractive features and capabilities when used for ion separations and water purification. In this study, we developed a new process called shock ion extraction (shock IX) for selective and chemical-free removal of toxic heavy metals from water. Shock IX is a hybrid process that combines shock electrodialysis (shock ED) and ion exchange using an ion exchange resin wafer (IERW), and this method can be thought of functionally as an electrochemically assisted variation of traditional ion exchange. In particular, shock IX exhibits greater ion removal and selectivity for longer periods of time, compared to the use of ion exchange alone. Here, the use of an IERW in shock ED also increases multivalent ion selectivity, reduces energy consumption, and improves the hydrodynamics and scalability of the system.
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The structure of polar liquids and electrolytic solutions, such as water and aqueous electrolytes, at interfaces underlies numerous phenomena in physics, chemistry, biology, and engineering. In this work, we develop a continuum theory that captures the essential features of dielectric screening by polar liquids at charged interfaces, including decaying spatial oscillations in charge and mass, starting from the molecular properties of the solvent. The theory predicts an anisotropic dielectric tensor of interfacial polar liquids previously studied in molecular dynamics simulations. We explore the effect of the interfacial polar liquid properties on the capacitance of the electrode/electrolyte interface and on hydration forces between two plane-parallel polarized surfaces. In the linear response approximation, we obtain simple formulas for the characteristic decay lengths of molecular and ionic profiles at the interface.
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We report constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on Li X FePO 4 , a technologically relevant battery positive electrode material. We uncovered the functional form of the composition–eigenstrain relation in this two-phase binary solid across the entire composition range (0 ≤ X ≤ 1), including inside the thermodynamically unstable miscibility gap. The learned relation directly validates Vegard’s law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.
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Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.
Ultrathin membranes with nanoporous conduits show promise for ionic separations and desalination applications, but the mechanisms underlying the nonlinear ionic transport observed in these systems are not well understood. Here, in this work, we demonstrate how induced charge at membrane interfaces can lead to nonlinear ionic transport and voltage-dependent conductance through such channels. The application of an electric field on a polarizable membrane leads to induced charges at the membrane interfaces. The induced charges in turn are screened by diffuse charges in the electrolyte, which are acted upon by the electric field. For extremely thin membranes, the induced charge effect can be significant even for moderate applied voltages commonly used in experiments. We apply a continuum Poisson-Nernst-Planck model to characterize the current-voltage behavior of ultrathin membranes over a wide parameter space. The predictions of the model are compared to recent experiments on graphene and MoS 2 membranes in an electric field. We expect the role of induced charge to be especially pronounced in the limit of atomically thin membranes.
Simultaneous multilayer coating techniques are widely known, but their industrial application remains limited to narrow market sectors. One barrier to adoption may be the mismatch between industries that are familiar with such processes but have no need, and industries that have need but are not familiar. Moreover, there are application-specific technical challenges to developing multilayer-coating processes. In this article, we describe our resolution of two specific issues in simultaneous multilayer coating of all aqueous highly loaded slurries for new and emerging energy applications. The first issue is particle jamming (associated with Shear-thickening) of the highly loaded slurries in the die internals, which we alleviated by adding small amounts of viscosity modifiers without reducing the solid loading. The second issue is a Marangoni-driven surface instability that resembles top layer de-wetting, which we solved by carefully selecting surfactants to tune the dynamic surface tensions of each slurry. Both issues were resolved early in a step-wise development, saving significant development cost which in our case was driven by expensive materials.
Electrochemical ion insertion involves coupled ion–electron transfer reactions, transport of guest species and redox of the host. The hosts are typically anisotropic solids with 2D conduction planes but can also be materials with 1D or isotropic transport pathways. These insertion compounds have traditionally been studied in the context of energy storage but also find extensive applications in electrocatalysis, optoelectronics and computing. Recent developments in operando, ultrafast and high-resolution characterization methods, as well as accurate theoretical simulation methods, have led to a renaissance in the understanding of ion-insertion compounds. In this Review, we present a unified framework for understanding insertion compounds across timescales and length scales ranging from atomic to device levels. Using graphite, transition metal dichalcogenides, layered oxides, oxyhydroxides and olivines as examples, we explore commonalities in these materials in terms of point defects, interfacial reactions and phase transformations. We illustrate similarities in the operating principles of various ion-insertion devices, ranging from batteries and electrocatalysts to electrochromics and thermal transistors, with the goal of unifying research across disciplinary boundaries.
Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.
Layered oxides widely used as lithium-ion battery electrodes are designed to be cycled under conditions that avoid phase transitions. Although the desired single-phase composition ranges are well established near equilibrium, operando diffraction studies on many-particle porous electrodes have suggested phase separation during delithiation. Notably, the separation is not always observed, and never during lithiation. These anomalies have been attributed to irreversible processes during the first delithiation or reversible concentration-dependent diffusion. However, these explanations are not consistent with all experimental observations such as rate and path dependencies and particle-by-particle lithium concentration changes. Here, we show that the apparent phase separation is a dynamical artefact occurring in a many-particle system driven by autocatalytic electrochemical reactions, that is, an interfacial exchange current that increases with the extent of delithiation. We experimentally validate this population-dynamics model using the single-phase material L ix (Ni 1/3 Mn 1/3 Co 1/3 )O 2 (0.5 < x < 1) and demonstrate generality with other transition-metal compositions. Operando diffraction and nanoscale oxidation-state mapping unambiguously prove that this fictitious phase separation is a repeatable non-equilibrium effect. We quantitatively confirm the theory with multiple-datastream-driven model extraction. More generally, our study experimentally demonstrates the control of ensemble stability by electro-autocatalysis, highlighting the importance of population dynamics in battery electrodes (even non-phase-separating ones).
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The individual and collective behavior of ions near electrically charged interfaces is foundational to a variety of electrochemical phenomena encountered in biology, energy, and the environment. While many theories have been developed to predict the interfacial arrangements of counterions, direct experimental observations and validations have remained elusive. Utilizing cryo-electron microscopy, here we directly visualize individual counterions and reveal their discrete interfacial layering. Comparison with simulations suggests the strong effects of finite ionic size and electrostatic interactions. We also uncover correlated ionic structures under extreme confinement, with the channel widths approaching the ionic diameter (~1 nm). Finally, our work reveals the roles of ionic size, valency, and confinement in determining the structures of liquid–solid interfaces and opens up new opportunities to study such systems at the single-ion level.