Publications

Li(Ni,Mn,Co)O2 (NMC) is one of the most widely used cathode materials for lithium-ion batteries. Most commercial cathodes utilize polycrystalline particle morphologies, which have a characteristic “meatball” shape. Recently, there has been interest in replacing polycrystalline particles with single-crystal particles, which are believed to have improved cycle life due to the absence of intergranular cracking. However, the effects of single-crystal particles on rate capability show conflicting results. In this work, we conduct single-particle electrochemistry using a microelectrode array to test the kinetics of 40 polycrystalline (3-12 µm) and 13 single-crystal (2-4 µm) NMC cathode particles. Our results show that single-crystal particles have much higher reaction overpotentials than polycrystalline ones. Moreover, larger single-crystal particles show higher overpotentials than smaller ones, while the polycrystalline particles display no size effects despite the greater variability. We attribute these findings to intergranular cracking and the penetration of the liquid electrolyte, which enables much faster reaction kinetics. In characterizing the electrochemistry of individual particles, our results confirm that these single-crystal particles would have much poorer rate kinetics and more challenges with fast charge and discharge compared to polycrystalline particles of the same composition.

Electronic switches based on the migration of high-density point defects, or memristors, are poised to revolutionize post-digital electronics. Despite significant research, key mechanisms for filament formation and oxygen transport remain unresolved, thus hindering our ability to predict and design crucial device properties. For example, predicted retention times based on current models can be 10 orders of magnitude lower than ones experimentally realized. Here, using electrical measurements, chemical spectroscopy, and first-principles calculations on tantalum oxide memristors, we reveal that the formation and stability of conductive filaments crucially depend on the stability of the amorphous oxygen-rich and oxygen-poor compounds, which undergo composition phase separation. Including the previously neglected effects of this amorphous phase separation reconciles unexplained discrepancies and enables predictive design of key performance indicators such as retention stability. This result emphasizes non-ideal thermodynamic interactions as key design criteria in post-digital devices with defect densities substantially exceeding those of today’s covalent semiconductors.

The oxygen diffusion rate in hafnia (HfO2)-based resistive memory plays a pivotal role in enabling nonvolatile data retention. However, the information retention times obtained in HfO2 resistive memory devices are many times higher than the expected values obtained from oxygen diffusion measurements in HfO2 materials. In this study, we resolve this discrepancy by conducting oxygen isotope tracer diffusion measurements in amorphous hafnia (a-HfO2) thin films. Our results show that the oxygen tracer diffusion in amorphous HfO2 films is orders of magnitude lower than that of previous measurements on monoclinic hafnia (m-HfO2) pellets. Moreover, oxygen tracer diffusion is much lower in denser a-HfO2 films deposited by atomic layer deposition (ALD) than in less dense a-HfO2 films deposited by sputtering. The ALD films yield similar oxygen diffusion times as experimentally measured device retention times, reconciling this discrepancy between oxygen diffusion and retention time measurements. More broadly, our work shows how processing conditions can be used to control oxygen transport characteristics in amorphous materials without long-range crystal order.

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.

Polycrystalline Li(Ni,Mn,Co)O2 (NMC) secondary particles are the most common cathode materials for Li-ion batteries. During electrochemical (dis)charge, lithium is believed to diffuse through the bulk and enter (leave) the secondary particle at the surface. Based on this model, smaller particles would cycle faster due to shorter diffusion lengths and larger surface-area-to-volume ratios. In this work, we evaluate this widespread assumption by developing a new high-throughput single-particle electrochemistry platform using the multi-electrode array from neuroscience. By measuring the reaction and diffusion times for 21 individual particles in liquid electrolytes, we find no correlation between the particle size and either the reaction or diffusion times, which is in stark contrast to the prevailing lithium transport model. We propose that electrochemical reactions occur inside secondary particles, possibly due to electrolyte penetration into cracks. Our high-throughput, single-particle electrochemical platform further opens new frontiers for robust, statistical quantification of individual particles in electrochemical systems.

Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This short retention has precluded ECRAM from being considered for inference classification in deep neural networks, which is likely the largest opportunity for in-memory computing. In this work, an ECRAM cell with orders of magnitude longer retention than previously achieved is developed, and which is anticipated to exceed ten years at 85 °C. This study hypothesizes that the origin of this exceptional retention is phase separation, which enables the formation of multiple effectively equilibrium resistance states. This work highlights the promises and opportunities to use phase separation to yield ECRAM cells with exceptionally long, and potentially permanent, retention times.

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

On-chip dynamic strain engineering requires efficient micro-actuators that can generate large in-plane strains. Inorganic electrochemical actuators are unique in that they are driven by low voltages (≈1 V) and produce considerable strains (≈1%). However, actuation speed and efficiency are limited by mass transport of ions. Minimizing the number of ions required to actuate is thus key to enabling useful “straintronic” devices. Here, it is shown that the electrochemical intercalation of exceptionally few lithium ions into WTe2 causes large anisotropic in-plane strain: 5% in one in-plane direction and 0.1% in the other. This efficient stretching of the 2D WTe2 layers contrasts to intercalation-induced strains in related materials which are predominantly in the out-of-plane direction. The unusual actuation of LixWTe2 is linked to the formation of a newly discovered crystallographic phase, referred to as Td', with an exotic atomic arrangement. On-chip low-voltage (<0.2 V) control is demonstrated over the transition to the novel phase and its composition. Within the Td'-Li0.5−δWTe2 phase, a uniaxial in-plane strain of 1.4% is achieved with a change of δ of only 0.075. This makes the in-plane chemical expansion coefficient of Td'-Li0.5−δWTe2 far greater than of any other single-phase material, enabling fast and efficient planar electrochemical actuation. 

Neuromorphic computing has the great potential to enable faster and more energy-efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)-based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three-terminal, LixWO3-based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time-dependent synaptic functions such as paired-pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike-encoded timing information extracted from the short-term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO-STP synapses.

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analogue memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analogue states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher speed and energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3×3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with an outer product update. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance towards developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks. 

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.  

Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LiXTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LiXTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime. 

Plasmonic antennas and metasurfaces can effectively control light–matter interactions, and this facilitates a deterministic design of optical materials properties, including structural color. However, these optical properties are generally fixed after synthesis and fabrication, while many modern-day optics applications require active, low-power, and nonvolatile tuning. These needs have spurred broad research activities aimed at identifying materials and resonant structures capable of achieving large, dynamic changes in optical properties, especially in the challenging visible spectral range. In this work, we demonstrate dynamic tuning of polarization-dependent gap plasmon resonators that contain the electrochromic oxide WO3. Its refractive index in the visible changes continuously from n = 2.1 to 1.9 upon electrochemical lithium insertion and removal in a solid-state device. By incorporating WO3 into a gap plasmon resonator, the resonant wavelength can be shifted continuously and reversibly by up to 58 nm with less than 2 V electrochemical bias voltage. The resonator can remain in a tuned state for tens of minutes under open circuit conditions.  

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.

Phase transformations driven by compositional change require mass flux across a phase boundary. In some anisotropic solids, however, the phase boundary moves along a non-conductive crystallographic direction. One such material is LiXFePO4, an electrode for lithium-ion batteries. With poor bulk ionic transport along the direction of phase separation, it is unclear how lithium migrates during phase transformations. Here, we show that lithium migrates along the solid/liquid interface without leaving the particle, whereby charge carriers do not cross the double layer. X-ray diffraction and microscopy experiments as well as ab initio molecular dynamics simulations show that organic solvent and water molecules promote this surface ion diffusion, effectively rendering LiXFePO4 a three-dimensional lithium-ion conductor. Phase-field simulations capture the effects of surface diffusion on phase transformation. Lowering surface diffusivity is crucial towards supressing phase separation. This work establishes fluid-enhanced surface diffusion as a key dial for tuning phase transformation in anisotropic solids.

Insertion is a widely utilized process for reversibly changing the stoichiometry of a solid through a chemical or electrochemical stimulus. Insertion is instrumental to many energy technologies, including batteries, fuel cells, and hydrogen storage, and has been the subject of extensive investigations. More recently, solid-state switching devices utilizing insertion have drawn significant interest; such devices dynamically switch a material's chemical stoichiometry, changing it from one state to another. This review illustrates the fundamental properties and mechanisms of insertion, including reaction, diffusion, and phase transformation, and discusses recent developments in characterization in these fields. We also review new classes of recently demonstrated insertion devices, which reversibly switch mechanical and electronic properties, and show how the fundamental mechanisms of insertion can be used to design improved switching devices.

The kinetics and uniformity of ion insertion reactions at the solid-liquid interface govern the rate capability and lifetime, respectively, of electrochemical devices such as Li-ion batteries. Using an operando x-ray microscopy platform that maps the dynamics of the Li composition and insertion rate in LixFePO4, we found that nanoscale spatial variations in rate and in composition control the lithiation pathway at the subparticle length scale. Specifically, spatial variations in the insertion rate constant lead to the formation of nonuniform domains, and the composition dependence of the rate constant amplifies nonuniformities during delithiation but suppresses them during lithiation, and moreover stabilizes the solid solution during lithiation. This coupling of lithium composition and surface reaction rates controls the kinetics and uniformity during electrochemical ion insertion.

High-resolution X-ray microscopy is used to investigate the sequence of lithiation in LiFePO4 porous electrodes. For electrodes with homogeneous interparticle electronic connectivity via the carbon black network, the smaller particles lithiate first. For electrodes with heterogeneous connectivity, the better-connected particles preferentially lithiate. Correlative electron and X-ray microscopy also reveal the presence of incoherent nanodomains that lithiate as if they are separate particles.

Many battery electrodes contain ensembles of nanoparticles that phase-separate on (de)intercalation. In such electrodes, the fraction of actively intercalating particles directly impacts cycle life: a vanishing population concentrates the current in a small number of particles, leading to current hotspots. Reports of the active particle population in the phase-separating electrode lithium iron phosphate (LiFePO4; LFP) vary widely, ranging from near 0% (particle-by-particle) to 100% (concurrent intercalation). Using synchrotron-based X-ray microscopy, we probed the individual state-of-charge for over 3,000 LFP particles. We observed that the active population depends strongly on the cycling current, exhibiting particle-by-particle-like behaviour at low rates and increasingly concurrent behaviour at high rates, consistent with our phase-field porous electrode simulations. Contrary to intuition, the current density, or current per active internal surface area, is nearly invariant with the global electrode cycling rate. Rather, the electrode accommodates higher current by increasing the active particle population. This behaviour results from thermodynamic transformation barriers in LFP, and such a phenomenon probably extends to other phase-separating battery materials. We propose that modifying the transformation barrier and exchange current density can increase the active population and thus the current homogeneity. This could introduce new paradigms to enhance the cycle life of phase-separating battery electrodes.