arXiv subjects

William C. Chueh

Publications and source records attributed to William C. Chueh.

At least 19 recordsLinked to original sources

arXiv2026

Electrochemical and thermal control of continuous phase transitions in P2-NaxNi1/3Mn2/3O2

Sodium layered oxides often undergo phase transformations involving ordering or disordering of Na+ upon desodiation, i.e., when cycled as a battery electrode. Accurately characterizing these phases is crucial for understanding functional properties, such as chemical diffusivity. In this work, we reveal that Na+-vacancy (dis)ordering in a layered oxide is intrinsically coupled to symmetry-changing phase transformations of the host structure. We examine the low-symmetry orthorhombic unit cell of P2-NaxNi1/3Mn2/3O2 (NNM) using both neutron and X-ray diffraction. Specifically, special sodium stoichiometries (x = 2/3 and 1/2) exhibit concomitant Na+-vacancy ordering and an orthorhombic distortion from the parent hexagonal unit cell. We then demonstrate that electrochemical desodiation drives symmetry-changing transformations in NNM that are linked to Na+-vacancy (dis)ordering, with evidence of second-order behavior observed near x = 2/3. Variable-temperature synchrotron X-ray diffraction further clarifies the coupling between Na+-vacancy disordering and orthorhombic-to-hexagonal phase transitions in NNM. Surprisingly, the temperature-driven phase transitions at x = 2/3 and 1/2 differ in character, appearing second-order and first-order, respectively. Our analysis of the phase transitions in NNM has fundamental consequences for sodium chemical diffusivity in the vicinity of the ordered phases and leads to design principles for modifying phase transition behavior in the broader class of intercalation electrodes.

cond-mat.mtrl-sci↗

arXiv2026

Visualizing Degradation in Anode-Free High-Utilization Aqueous Batteries Across Cell Lifetime

Operando microscopy has unveiled key mechanistic insights in battery materials during early cycling, but long-term characterization to unveil material evolution, degradation, and failure remain limited. To address this gap, we develop a custom operando optical microscope that captures images across hundreds of cycles and hours using optically accessible, anode-free pouch cells. We image through-plane, bulk-representative electrodeposition behavior of aqueous tin metal anodes, which are promising due to their high energy density but whose reactivity limits practical cycle life. We show that substrate governs the morphology and stability of plated tin, particularly at high plated capacities. Specifically, copper substrates exhibit a multi-stage tin growth mode, which results in high overpotentials and irreversible active material loss at high plated capacities. In contrast, graphite substrates display a single-stage growth mode with slower kinetics. Using this insight, we balance performance and stability to demonstrate a high-utilization (70%, 630 mAh g$^{-1}_{Sn}$) porous graphite substrate Sn anode with high efficiency and long lifetime. Our results underscore the importance of material and device optimization guided by operando characterization across device lifetime with broad applicability to electrochemical systems.

cond-mat.mtrl-sci↗

arXiv2025

Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles

The Doyle-Fuller-Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity within the DFN model are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode's diffusion coefficients and reaction rate constants of 95 Tesla Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide--graphite (LiC$_\text{6}$--SiO$_{\text{x}}$) anode. The parameters are estimated at intermittent diagnostic cycles over the lifetime of each cell. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for uncertainty quantification (UQ) for a total of 7776 cycles at discharge C-rates of C/5, 1C, and 2C. While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the parameters contributing to cell aging. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions.

eess.SY↗

arXiv2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery's current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery's degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. In this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning. We demonstrate the versatility of this model framework by applying it to three battery-cycling datasets consisting of 422 cells under different operating conditions, highlighting the utility of an interpretable diagnostic-free, onboard battery diagnosis and prognosis model.

eess.SY↗

arXiv2024

How quickly can sodium-ion learn? Assessing scenarios for techno-economic competitiveness against lithium-ion batteries

Sodium-ion batteries have garnered significant attention as a potentially low-cost alternative to lithium-ion batteries, which have experienced supply shortages and pricing volatility of key minerals. Here we assess their techno-economic competitiveness against incumbent lithium-ion batteries using a modeling framework incorporating componential learning curves constrained by minerals prices and engineering design floors. We compare projected sodium-ion and lithium-ion price trends across over 6,000 scenarios while varying Na-ion technology development roadmaps, supply chain scenarios, market penetration, and learning rates. Assuming substantial progress can be made along technology roadmaps via targeted R&D, we identify many sodium-ion pathways that might reach cost-competitiveness with low-cost lithium-ion variants in the early 2030s. Additionally, we show timelines are highly sensitive to movements in critical minerals supply chains -- namely that of lithium, graphite, and nickel. Modeled outcomes suggest increasing sodium-ion energy densities to decrease materials intensity to be among the most impactful ways to improve competitiveness.

physics.chem-ph↗

arXiv2024

Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

cs.LG↗

arXiv2023

Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data

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 $\mathbf{Xw}=\mathbf{0}$, 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.

stat.ML↗

arXiv2021

Correlative image learning of chemo-mechanics in phase-transforming solids

Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (e.g., due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. In this work, 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 composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 $\leq$ X $\leq$ 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.

cond-mat.mtrl-sci↗

arXiv2021

Correlative analysis of structure and chemistry of LixFePO4 platelets using 4D-STEM and X-ray ptychography

Lithium iron phosphate (LixFePO4), a cathode material used in rechargeable Li-ion batteries, phase separates upon de/lithiation under equilibrium. The interfacial structure and chemistry within these cathode materials affects Li-ion transport, and therefore battery performance. Correlative imaging of LixFePO4 was performed using four-dimensional scanning transmission electron microscopy (4D-STEM), scanning transmission X-ray microscopy (STXM), and X-ray ptychography in order to analyze the local structure and chemistry of the same particle set. Over 50,000 diffraction patterns from 10 particles provided measurements of both structure and chemistry at a nanoscale spatial resolution (16.6-49.5 nm) over wide (several micron) fields-of-view with statistical robustness.LixFePO4 particles at varying stages of delithiation were measured to examine the evolution of structure and chemistry as a function of delithiation. In lithiated and delithiated particles, local variations were observed in the degree of lithiation even while local lattice structures remained comparatively constant, and calculation of linear coefficients of chemical expansion suggest pinning of the lattice structures in these populations. Partially delithiated particles displayed broadly core-shell-like structures, however, with highly variable behavior both locally and per individual particle that exhibited distinctive intermediate regions at the interface between phases, and pockets within the lithiated core that correspond to FePO4 in structure and chemistry.The results provide insight into the LixFePO4 system, subtleties in the scope and applicability of Vegards law (linear lattice parameter-composition behavior) under local versus global measurements, and demonstrate a powerful new combination of experimental and analytical modalities for bridging the crucial gap between local and statistical characterization.

cond-mat.mtrl-sci↗

arXiv2021

Layer-Resolved Many-Electron Interactions in Delafossite PdCoO2 from Standing-Wave Photoemission Spectroscopy

When a three-dimensional material is constructed by stacking different two-dimensional layers into an ordered structure, new and unique physical properties can emerge. An example is the delafossite PdCoO2, which consists of alternating layers of metallic Pd and Mott-insulating CoO2 sheets. To understand the nature of the electronic coupling between the layers that gives rise to the unique properties of PdCoO2, we revealed its layer-resolved electronic structure combining standing-wave X-ray photoemission spectroscopy and ab initio many-body calculations. Experimentally, we have decomposed the measured valence band spectrum into contributions from Pd and CoO2 layers. Computationally, we find that many-body interactions in Pd and CoO2 layers are highly different. Holes in the CoO2 layer interact strongly with charge-transfer excitons in the same layer, whereas holes in the Pd layer couple to plasmons in the Pd layer. Interestingly, we find that holes in states hybridized across both layers couple to both types of excitations (charge-transfer excitons or plasmons), with the intensity of photoemission satellites being proportional to the projection of the state onto a given layer. This establishes satellites as a sensitive probe for inter-layer hybridization. These findings pave the way towards a better understanding of complex many-electron interactions in layered quantum materials.

cond-mat.mtrl-sci↗

arXiv2021

Universal phase dynamics in VO2 switches revealed by ultrafast operando diffraction

Strongly correlated materials that exhibit an insulator-metal transition are key candidates in the search for new computing platforms. Understanding the pathways and timescales underlying the electrically-driven insulator-metal transition is crucial for uncovering the fundamental limits of device operation. Using stroboscopic electron diffraction, we perform synchronized time-resolved measurements of atomic motions and electronic transport in operating vanadium dioxide switches. We discover an electrically-triggered, isostructural state that forms transiently on microsecond timescales, stabilized by local heterogeneities and interfacial interactions between the equilibrium phases. This metastable phase bears striking similarity to that formed under photoexcitation within picoseconds, suggesting a universal transformation pathway across eight orders of magnitude of timescale. Our results establish a new route for uncovering non-equilibrium and metastable phases in correlated materials, and open avenues for engineering novel dynamical behavior in nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2020

Electrochemical Ion Insertion: From Atoms to Devices

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 two-dimensional conduction planes, but can also be materials with one-dimensional 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 time 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.

cond-mat.mtrl-sci↗

arXiv2020

Theory of coupled ion-electron transfer kinetics

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 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 {\it 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.

physics.chem-ph↗

arXiv2020

Coulombically-stabilized oxygen hole polarons enable fully reversible oxygen redox

Stabilizing high-valent redox couples and exotic electronic states necessitate an understanding of the stabilization mechanism. In oxides, whether they are being considered for energy storage or computing, highly oxidized oxide-anion species rehybridize to form short covalent bonds and are related to significant local structural distortions. In intercalation oxide electrodes for batteries, while such reorganization partially stabilizes oxygen redox, it also gives rise to substantial hysteresis. In this work, we investigate oxygen redox in layered Na2-XMn3O7, a positive electrode material with ordered Mn vacancies. We show that coulombic interactions between oxidized oxide-anions and the interlayer Na vacancies can disfavor rehybridization and stabilize hole polarons on oxygen at 4.2 V vs. Na/Na+. These coulombic interactions provide thermodynamic energy saving as large as O-O covalent bonding and enable ~ 40 mV voltage hysteresis over multiple electrochemical cycles with negligible voltage fade. Our results establish a complete picture of redox energetics by highlighting the role of coulombic interactions across several atomic distances and suggest avenues to stabilize highly oxidized oxygen for applications in energy storage and beyond.

cond-mat.mtrl-sci↗

arXiv2019

Electrochemical kinetics of SEI growth on carbon black, I: Experiments

Growth of the solid electrolyte interphase (SEI) is a primary driver of capacity fade in lithium-ion batteries. Despite its importance to this device and intense research interest, the fundamental mechanisms underpinning SEI growth remain unclear. In Part I of this work, we present an electroanalytical method to measure the dependence of SEI growth on potential, current magnitude, and current direction during galvanostatic cycling of carbon black/Li half cells. We find that SEI growth strongly depends on all three parameters; most notably, we find SEI growth rates increase with nominal C rate and are significantly higher on lithiation than on delithiation. We observe this directional effect in both galvanostatic and potentiostatic experiments and discuss hypotheses that could explain this observation. This work identifies a strong coupling between SEI growth and charge storage (e.g., intercalation and capacitance) in carbon negative electrodes.

cond-mat.mtrl-sci↗

arXiv2019

Electrochemical kinetics of SEI growth on carbon black, II: Modeling

Mathematical models of capacity fade can reduce the time and cost of lithium-ion battery development and deployment, and growth of the solid-electrolyte interphase (SEI) is a major source of capacity fade. Experiments in Part I reveal nonlinear voltage dependence and strong charge-discharge asymmetry in SEI growth on carbon black negative electrodes, which is not captured by previous models. Here, we present a theoretical model for the electrochemical kinetics of SEI growth coupled to lithium intercalation, which accurately predicts experimental results with few adjustable parameters. The key hypothesis is that the initial SEI is a mixed ion-electron conductor, and its electronic conductivity varies approximately with the square of the local lithium concentration, consistent with hopping conduction of electrons along percolating networks. By including a lithium-ion concentration dependence for the electronic conductivity in the SEI, the bulk SEI thus modulates the overpotential and exchange current of the electrolyte reduction reaction. As a result, SEI growth is promoted during lithiation but suppressed during delithiation. This new insight establishes the fundamental electrochemistry of SEI growth kinetics. Our model improves upon existing models by introducing the effects of electrochemical SEI growth and its dependence on potential, current magnitude, and current direction in predicting capacity fade.

physics.chem-ph↗

arXiv2018

Fingerprint oxygen redox reactions in batteries through high-efficiency mapping of resonant inelastic X-ray scattering (mRIXS)

Realizing reversible reduction-oxidation (Redox) reactions of lattice oxygen in batteries is a promising way to improve the energy and power density. However, conventional oxygen absorption spectroscopy fails to distinguish the critical oxygen chemistry in oxide-based battery electrodes. Therefore, high-efficiency full-range mapping of resonant inelastic X-ray scattering (mRIXS) has been developed as a reliable probe of oxygen redox reactions. Here, based on mRIXS results collected from a series of Li1.17Ni0.21Co0.08Mn0.54O2 electrodes at different electrochemical states and its comparison with peroxides, we provide a comprehensive analysis of five components observed in the mRIXS results. While almost all the components change upon electrochemical cycling, comparison with peroxide species show that only two evolving features correspond to the critical oxidized oxygen states. One is a specific feature at 531.0eV excitation and 523.7eV emission energy, the other is a low-energy loss feature. We show that both features evolve with electrochemical cycling of Li1.17Ni0.21Co0.08Mn0.54O2 electrodes, and could be used for characterizing oxygen redox states in battery electrodes. This work is the first benchmark for a complete assignment of all the important mRIXS features collected from battery materials, which provides guidelines for future studies in characterization, analysis and theoretical calculation for probing and understanding oxygen redox reactions.

physics.chem-ph↗

arXiv2018

High Reversibility of Lattice Oxygen Redox in Na-ion and Li-ion Batteries Quantified by Direct Bulk Probes of both Anionic and Cationic Redox Reactions

The reversibility and cyclability of anionic redox in battery electrodes hold the key to its practical employments. Here, through mapping of resonant inelastic X-ray scattering (mRIXS), we have independently quantified the evolving redox states of both cations and anions in Na2/3Mg1/3Mn2/3O2. The bulk-Mn redox emerges from initial discharge and is quantified by inverse-partial fluorescence yield (iPFY) from Mn-L mRIXS. Bulk and surface Mn activities likely lead to the voltage fade. O-K super-partial fluorescence yield (sPFY) analysis of mRIXS shows 79% lattice oxygen-redox reversibility during initial cycle, with 87% capacity sustained after 100 cycles. In Li1.17Ni0.21Co0.08Mn0.54O2, lattice-oxygen redox is 76% initial-cycle reversible but with only 44% capacity retention after 500 cycles. These results unambiguously show the high reversibility of lattice-oxygen redox in both Li-ion and Na-ion systems. The contrast between Na2/3Mg1/3Mn2/3O2 and Li1.17Ni0.21Co0.08Mn0.54O2 systems suggests the importance of distinguishing lattice-oxygen redox from other oxygen activities for clarifying its intrinsic properties.

physics.chem-ph↗