RESUMEN
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.
RESUMEN
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.
RESUMEN
A variety of autonomous oscillations in nature such as heartbeats and some biochemical reactions have been widely studied and utilized for applications in the fields of bioscience and engineering. Here, we report a unique phenomenon of moisture-induced electrical potential oscillations on polymers, poly([2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide-co-acrylic acid), during the diffusion of water molecules. Chemical reactions are modeled by kinetic simulations while system dynamic equations and the stability matrix are analyzed to show the chaotic nature of the system which oscillates with hidden attractors to induce the autonomous surface potential oscillation. Using moisture in the ambient environment as the activation source, this self-excited chemoelectrical reaction could have broad influences and usages in surface-reaction based devices and systems. As a proof-of-concept demonstration, an energy harvester is constructed and achieved the continuous energy production for more than 15,000 seconds with an energy density of 16.8 mJ/cm2. A 2-Volts output voltage has been produced to power a liquid crystal display toward practical applications with five energy harvesters connected in series.
