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Register a new userStatistical Kinetics of Phase-Transforming Nanoparticles in LiFePO4 Porous Electrodes
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Using a simple mathematical model, we demonstrate that statistical kinetics of phase-transforming nanoparticles in porous electrodes results in macroscopic non-monotonic transient currents, which could be misinterpreted as the nucleation and growth mechanism by the Kolmogorov-Johnson-Mehl-Avrami (KJMA) theory. Our model decouples the roles of nucleation and surface reaction in the electrochemically driven phase-transformation process by a special activation rate and the mean particle-filling speed of active nanoparticles, which can be extracted from the responses of porous electrodes to identify the dynamics in single composing nanoparticles.
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Interfacial charge transfer is widely assumed to obey Butler-Volmer kinetics. For certain liquid-solid interfaces, Marcus-Hush-Chidsey theory is more accurate and predictive, but it has not been applied to porous electrodes. Here we report a simple method to extract the charge transfer rates in carbon-coated LiFePO4 porous electrodes from chronoamperometry experiments, obtaining curved Tafel plots that contradict the Butler-Volmer equation but fit the Marcus-Hush-Chidsey prediction over a range of temperatures. The fitted reorganization energy matches the Born solvation energy for electron transfer from carbon to the iron redox site. The kinetics are thus limited by electron transfer at the solid-solid (carbon-LixFePO4) interface, rather than by ion transfer at the liquid-solid interface, as previously assumed. The proposed experimental method generalizes Chidseys method for phase-transforming particles and porous electrodes, and the results show the need to incorporate Marcus kinetics in modeling batteries and other electrochemical systems.
Electrochemical energy systems rely on particulate porous electrodes to store or convert energies. While the three-dimensional porous structures were introduced to maximize the interfacial area for better overall performance of the system, spatiotemporal heterogeneities arose from materials thermodynamics localize the charge transfer processes onto a limited portion of the available interfaces. Here, we demonstrate a simple but precision method that can directly track and analyze the operando (i.e. local and reacting) interfaces at the mesoscale in a practical graphite porous electrode to obtain the true local current density, which turned out to be two orders of magnitude higher than the globally averaged current density adopted by existing studies. Our results resolve the long-standing discrepancies between kinetics parameters derived from electroanalytical measurements and from first principles predictions. Contradictory to prevailing beliefs, the electrochemical dynamics is not controlled by the solid-state diffusion process once the spatiotemporal reaction heterogeneities emerge in porous electrodes.
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 Vegards 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.
Atomistic simulations are performed to study the statistical mechanical property of gold nanoparticles. It is demonstrated that the yielding behavior of gold nanoparticles is governed by dislocation nucleation around surface steps. Since the nucleation of dislocations is an activated process with the aid of thermal fluctuation, the yield stress at a specific temperature should exhibit a statistical distribution rather than a definite constant value. Molecular dynamics simulations reveal that the yield stress follows a Gaussian distribution at a specific temperature. As the temperature increases, the mean value of yield stress decreases while the width of distribution becomes larger. Based on numerical analysis, the dependence of the mean yield stress on temperature can be well described by a parabolic function. Present study illuminates the statistical features of the yielding behavior of nanostructured elements.
Todays supercapacitor energy storages are typically discrete devices aimed for printed boards and power applications. The development of autonomous sensor networks and wearable electronics and the miniaturisation of mobile devices would benefit substantially from solutions in which the energy storage is integrated with the active device. Nanostructures based on porous silicon (PS) provide a route towards integration due to the very high inherent surface area to volume ratio and compatibility with microelectronics fabrication processes. Unfortunately, pristine PS has limited wettability and poor chemical stability in electrolytes and the high resistance of the PS matrix severely limits the power efficiency. In this work, we demonstrate that excellent wettability and electro-chemical properties in aqueous and organic electrolytes can be obtained by coating the PS matrix with an ultra-thin layer of titanium nitride by atomic layer deposition. Our approach leads to very high specific capacitance (15 F/cm$^3$), energy density (1.3 mWh/cm$^3$), power density (up to 214 W/cm$^3$) and excellent stability (more than 13,000 cycles). Furthermore, we show that the PS-TiN nanomaterial can be integrated inside a silicon chip monolithically by combining MEMS and nanofabrication techniques. This leads to realisation of in-chip supercapacitor, i.e., it opens a new way to exploit the otherwise inactive volume of a silicon chip to store energy.
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