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Twisted 2D-flat band materials host exotic quantum phenomena and novel moiré patterns, showing immense promise for advanced spintronic and quantum applications. Here, we evaluate the nanostructure-activity relationship in twisted bilayer graphene by modeling it under the scanning electrochemical cell microscopy setup to resolve its spatial moiré domains. We solve the steady state ion transport inside a 3D nanopipette to isolate the current response at AA and AB domains. Interfacial reaction rates are obtained from a modified Marcus-Hush-Chidsey theory combining input from a tight binding model that describes the electronic structure of bilayer graphene. High rates of redox exchange are observed at the AA domains, an effect that reduces with diminished flat bands or a larger cross-sectional area of the nanopipette. Using voltammograms, we identify an optimal voltage that maximizes the current difference between the domains. Our study lays down the framework to electrochemically capture prominent features of the band structure that arise from spatial domains and deformations in 2D flat-band materials.
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In this work, we develop a twist-dependent electrochemical activity map, combining a low-energy continuum electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists in the system geometry. We find a broad activity peak with a ruthenium hexamine redox couple in regions corresponding to both magic angles and incommensurate angles, a result qualitatively distinct from the twisted bilayer case. Flat bands and incommensurability offer new avenues for reaction rate enhancements in electrochemical transformations.
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The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties, and ab initio calculations are too costly. In this work, we train a machine learning interaction potential on density functional theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants, and various surface properties inaccessible using DFT. We establish that there exists a weak Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.
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Open-circuit voltage (OCV) models as a function of state-of-charge (SOC) are fundamental to modeling the performance of batteries. The second law of thermodynamics enforces that the OCV should be monotonic with respect to the SOC. In this Perspective, we first review some of the currently popular empirical OCV fitting methods which compromise thermodynamics for the flexibility of empirical fitting. We propose a simple thermodynamically consistent OCV model enabled by differentiable thermodynamic modeling, which obeys the second law of thermodynamics. We cast the common-tangent condition derived from the second law of thermodynamics as a fixed-point solving problem and use implicit function theorem to enable efficient gradient-based parameters optimization. We demonstrate this on the OCV of 12 popular electrode materials, and this is integrated with open-source battery modeling software PyBaMM. We perform pseudo-2D discharge simulations to show the seamless integration of the above OCV models with battery modeling software. Given the simplicity of integration and implementation, we believe that thermodynamically consistent OCV models should be a requirement for future Li-ion battery models.
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Enabling widespread electric vehicle (EV) adoption requires a substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the USA at a county-level resolution. We find that gasoline and EV charging stations scale sub-linearly with their respective vehicle registrations, recovering the sub-linear scaling typical of infrastructure. Surprisingly, we find that EV charging stations scale super-linearly with population size within counties, deviating from the sub-linear scaling of gasoline stations. We discuss how this demonstrates the infancy of both EVs and EV infrastructure while providing a framework for estimating future EV infrastructure demands. By considering the power delivery of existing gasoline stations, and appropriate EV efficiencies, we estimate the EV infrastructure gap at the county level, providing a road map for future EV infrastructure expansion.
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Accurate models of electrochemical kinetics at electrode-electrolyte interfaces are crucial to understanding the high-rate behavior of energy storage devices. Phase transformation of electrodes is typically treated under equilibrium thermodynamic conditions, while realistic operation is at finite rates. Analyzing phase transformations under nonequilibrium conditions requires integrating nonlinear electrochemical kinetic models with thermodynamic models. This had only previously been demonstrated for Butler-Volmer kinetics, where it can be done analytically. In this work, we develop a software package capable of the efficient numerical inversion of rate relationships for general kinetic models. We demonstrate building nonequilibrium phase maps, including for models such as Marcus-Hush-Chidsey that require computation of an integral, and also discuss the impact of a variety of assumptions and model parameters, particularly on high-rate phase behavior. Even for a fixed set of parameters, the magnitude of the critical current can vary by more than a factor of 2 among kinetic models.
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Interfacial electron-transfer (ET) reactions underpin the interconversion of electrical and chemical energy. It is known that the electronic state of electrodes strongly influences ET rates because of differences in the electronic density of states (DOS) across metals, semimetals, and semiconductors. Here, by controlling interlayer twists in well-defined trilayer graphene moirés, we show that ET rates are strikingly dependent on electronic localization in each atomic layer and not the overall DOS. The large degree of tunability inherent to moiré electrodes leads to local ET kinetics that range over 3 orders of magnitude across different constructions of only three atomic layers, even exceeding rates at bulk metals. Our results demonstrate that beyond the ensemble DOS, electronic localization is critical in facilitating interfacial ET, with implications for understanding the origin of high interfacial reactivity typically exhibited by defects at electrode-electrolyte interfaces.
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Electric vertical takeoff and landing aircraft have a unique duty cycle characterized by high discharge currents at the beginning and end of the mission (corresponding to takeoff and landing of the aircraft) and a moderate power requirement between them with no rest periods during the mission. Here, we generated a dataset of battery duty profiles for an electric vertical takeoff and landing aircraft using a cell typical for that application. The dataset features 22 cells, comprising a total of 21,392 charge and discharge cycles. 3 of the cells use the baseline cycle while each of the other cells vary either charge current, discharge power, discharge duration, ambient cooling conditions, or end of charge voltage. While it was designed to mimic the expected duty cycle of an electric aircraft, this dataset is relevant for training machine learning models on battery life, fitting physical or empirical models for battery performance and/or degradation, and countless other applications.
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The past decade has witnessed the development of layered-hydroxide-based self-supporting electrodes, but the low active mass ratio impedes its all-around energy-storage applications. Herein, the intrinsic limit of layered hydroxides is broken by engineering F-substituted ß-Ni(OH)2 (Ni-F-OH) plates with a sub-micrometer thickness (over 700 nm), producing a superhigh mass loading of 29.8 mg cm-2 on the carbon substrate. Theoretical calculation and X-ray absorption spectroscopy analysis demonstrate that Ni-F-OH shares the ß-Ni(OH)2 -like structure with slightly tuned lattice parameters. More interestingly, the synergy modulation of NH4 + and F- is found to serve as the key enabler to tailor these sub-micrometer-thickness 2D plates thanks to the modification effects on the (001) plane surface energy and local OH- concentration. Guided by this mechanism, the superstructures of bimetallic hydroxides and their derivatives are further developed, revealing they are a versatile family with great promise. The tailored ultrathick phosphide superstructure achieves a superhigh specific capacity of 7144 mC cm-2 and a superior rate capability (79% at 50 mA cm-2 ). This work highlights a multiscale understanding of how exceptional structure modulation happens in low-dimensional layered materials. The as-built unique methodology and mechanisms will boost the development of advanced materials to better meet future energy demands.
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The lithium-metal anode is one of the most promising candidates for "beyond-lithium-ion" batteries thanks to its high specific capacity and low negative electrochemical potential. However, the electrode-electrolyte interface instability hinders its commercialization in rechargeable batteries. During cycles of charging and discharging, the lithium-metal anode is electrochemically plated and stripped along with the morphological evolution, which determines the cycling performance. In this work, with a phase-field model, we computationally characterize the morphological evolution dynamics during the plating and stripping steps at the lithium-metal-electrolyte interface. Our model is valid in a wide range of lithium concentrations in liquid electrolytes by incorporating nonidealities of electrolyte solutions into the interfacial reaction kinetics. Intriguingly, at fast stripping, i.e., high discharging overpotential, we observe an unexpected localized recrystallization phenomenon in high-lithium-ion-concentration valley regions. This recrystallization phenomenon mitigates the overall reaction rate heterogeneity and provides a potential approach to improving the morphological stability. Furthermore, we systematically investigate the correlation between the recrystallization phenomenon and lithium-ion activity and draw a simplified phase diagram for the overpotential-dependent recrystallization.
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Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphiteâ£â£LiNi0.5Mn0.3Co0.2O2 pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.
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Solid-state Li-ion batteries with lithium anodes offer higher energy densities and are safer than conventional liquid electrolyte-based Li-ion batteries. However, the growth of lithium dendrites across the solid-state electrolyte layer leads to the premature shorting of cells and limits their practical viability. Here, using solid-state Li half-cells with metallic interlayers between a garnet-based lithium-ion conductor and lithium, we show that interfacial void growth precedes dendrite nucleation and growth. Specifically, void growth was observed at a current density of around two-thirds of the critical current density for dendrite growth. Computational calculations reveal that interlayer materials with higher critical current densities for dendrite growth also have the largest thermodynamic and kinetic barriers for lithium vacancy accumulation at their interfaces with lithium. Our results suggest that interfacial modification with suitable metallic interlayers decreases the tendency for void growth and improves dendrite growth tolerance in solid-state electrolytes, even in the absence of high stack pressures.
Assuntos
Eletrólitos , Lítio , Dendritos , Fontes de Energia Elétrica , EletrodosRESUMO
There is great current interest in multicomponent superhydrides due to their unique quantum properties under pressure. A remarkable example is the ternary superhydride Li_{2}MgH_{16} computationally identified to have an unprecedented high superconducting critical temperature T_{c} of â¼470 K at 250 GPa. However, the very high synthesis pressures required remains a significant hurdle for detailed study and potential applications. In this Letter, we evaluate the feasibility of synthesizing ternary Li-Mg superhydrides by the recently proposed pressure-potential (P^{2}) method that uniquely combines electrochemistry and applied pressure to control synthesis and stability. The results indicate that it is possible to synthesize Li-Mg superhydrides at modest pressures by applying suitable electrode potentials. Using pressure alone, no Li-Mg ternary hydrides are predicted to be thermodynamically stable, but in the presence of electrode potentials, both Li_{2}MgH_{16} and Li_{4}MgH_{24} can be stabilized at modest pressures. Three polymorphs are predicted as ground states of Li_{2}MgH_{16} below 300 GPa, with transitions at 33 and 160 GPa. The highest pressure phase is superconducting, while the two at lower pressures are not. Our findings point out the potentially important role of the P^{2} method in controlling phase stability of complex multicomponent superhydrides.
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Tailoring electron transfer dynamics across solid-liquid interfaces is fundamental to the interconversion of electrical and chemical energy. Stacking atomically thin layers with a small azimuthal misorientation to produce moiré superlattices enables the controlled engineering of electronic band structures and the formation of extremely flat electronic bands. Here, we report a strong twist-angle dependence of heterogeneous charge transfer kinetics at twisted bilayer graphene electrodes with the greatest enhancement observed near the 'magic angle' (~1.1°). This effect is driven by the angle-dependent tuning of moiré-derived flat bands that modulate electron transfer processes with the solution-phase redox couple. Combined experimental and computational analysis reveals that the variation in electrochemical activity with moiré angle is controlled by a structural relaxation of the moiré superlattice at twist angles of <2°, and 'topological defect' AA stacking regions, where flat bands are localized, produce a large anomalous local electrochemical enhancement that cannot be accounted for by the elevated local density of states alone.
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Aircraft, and the aviation ecosystem in which they operate, are shaped by complex trades among technical requirements, economics and environmental concerns, all built on a foundation of safety. This Perspective explores the requirements of battery-powered aircraft and the chemistries that hold promise to enable them. The difference between flight and terrestrial needs and chemistries are highlighted. Safe, usable specific energy rather than cost is the major constraint for aviation. We conclude that battery packs suitable for flight with specific energy approaching 600 kilowatt hours per kilogram may be achievable in the next decade given sufficient investment targeted at aeronautical applications.
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Novel methods for producing ammonia, a large-scale industrial chemical, are necessary for reducing the environmental impact of its production. Lithium-mediated electrochemical nitrogen reduction is one attractive alternative method for producing ammonia. In this work, we experimentally tested several classes of proton donors for activity in the lithium-mediated approach. From these data, an interpretable data-driven classification model is constructed to distinguish between active and inactive proton donors; solvatochromic Kamlet-Taft parameters emerged to be the key descriptors for predicting nitrogen reduction activity. A deep learning model is trained to predict these parameters using experimental data from the literature. The combination of the classification and deep learning models provides a predictive mapping from proton donor structure to activity for nitrogen reduction. We demonstrate that the two-model approach is superior to a purely mechanistic or a data-driven approach in accuracy and experimental data efficiency.
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Recently, superhydrides have been computationally identified and subsequently synthesized with a variety of metals at very high pressures. In this work, we evaluate the possibility of synthesizing superhydrides by uniquely combining electrochemistry and applied pressure. We perform computational searches using density functional theory and particle swarm optimization calculations over a broad range of pressures and electrode potentials. Using a thermodynamic analysis, we construct pressure-potential phase diagrams and provide an alternate synthesis concept, pressure-potential ([Formula: see text]), to access phases having high hydrogen content. Palladium-hydrogen is a widely studied material system with the highest hydride phase being Pd3H4 Most strikingly for this system, at potentials above hydrogen evolution and â¼ 300 MPa pressure, we find the possibility to make palladium superhydrides (e.g., PdH10). We predict the generalizability of this approach for La-H, Y-H, and Mg-H with 10- to 100-fold reduction in required pressure for stabilizing phases. In addition, the [Formula: see text] strategy allows stabilizing additional phases that cannot be done purely by either pressure or potential and is a general approach that is likely to work for synthesizing other hydrides at modest pressures.
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Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 mi of range with payload equivalents of up to seven passengers. Novel UAM aircraft consume between 130 Wh/passenger-mi and â¼ 1,200 Wh/passenger-mi depending on the design and utilization, compared to an expected consumption of over 220 Wh/passenger-mi and 1,000 Wh/passenger-mi for terrestrial electric vehicles and combustion engine vehicles, respectively. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power and energy, while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.