Ezequiel Pedro Marcos Leiva

Carbon‐based anodes remain central to lithium‐ion battery development, yet the relationship between oxidation state, structural disorder, and lithium‐ion transport kinetics is still incompletely understood. Here, we systematically compare commercial graphite (GPH), graphene oxide (GO), and exfoliated graphene (EGPN) as anode materials, combining morphological (scanning electron microscopy [SEM]), structural (X‐ray diffraction [XRD]), and chemical (X‐ray photoelectron spectroscopy [XPS]) characterization with an evaluation of electrochemical performance. Our results reveal that oxidation degree governs a fundamental trade‐off: insufficient oxidation, as in GPH, limits interlayer spacing and constrains Li ⁺ transport, while excessive oxidation, as in GO, introduces sp ³ ‐rich domains and oxygen‐containing functional groups that impede both electronic conductivity and ionic mobility. EGPN occupies a productive middle ground; its moderate surface functionalization and expanded interlayer spacing collectively enhance solid‐state and interfacial Li ⁺ diffusivity by up to two orders of magnitude relative to GO, enabling a specific capacity of 165 mAh g ⁻¹ at 1C with lower polarization losses. These findings demonstrate that electrochemical performance in carbon anodes is not simply a function of surface area or defect density, but of achieving an optimal balance between sp ² conductivity and controlled functionalization, a design principle with direct implications for next‐generation, high‐rate carbon anode materials.

In the present work, we address the theory of the lattice-gas model to the study of intercalation materials by using a novel kinetic Monte Carlo (kMC) algorithm for the simulation of an electrochemical method of everyday use in R&D laboratories: constant-current chrono-potentiometric measurements. The main aim of the present approach is to show how to use these atomistic simulations to study intercalation materials used as electrodes in alkali-ion batteries under galvanostatic conditions. The framework can be applied to related areas. To accomplish this, we explain the electrochemical background, linking the continuum scale with the microscopic events of discrete simulations. A comprehensive theoretical approach developed in a previous work is used as a reference for this aim. The galvanostatic kMC algorithm proposed is explained in detail and is subject to validation tests. The present work may serve as a basis for future implementations of kMC under galvanostatic conditions to study phenomena beyond the applicability of simulations on the continuum scale.

In the present work, we address the theory and application of the lattice-gas model to the study of intercalation materials by using a novel kinetic Monte Carlo (kMC) algorithm for the simulation of an electrochemical method of everyday use in R&D laboratories: potentiometric measurements at a constant current. The main aim of the present approach is to show how to use these atomistic simulations to study intercalation materials used as electrodes in alkali-ion batteries. The framework can be applied to related areas. To do this, we explain the electrochemical background, linking the continuum scale with the microscopic events of discrete simulations. A comprehensive theoretical approach developed in previous work is used for this aim. The galvanostatic kMC algorithm proposed is explained in detail and is subject to validation tests.

Herein, a novel open-source software for predicting the performance of single particles of electrode materials under galvanostatic charging conditions is presented. The model improves previous work by incorporating different thermodynamic approaches to describe the interaction between intercalated ions, and the software provides tools for fitting different kinetic parameters and generating potential/capacity profiles or concentration/distance profiles to be analyzed. These features allow for detailed studies of the factors limiting the charging rate of active materials in which ions are intercalated. The diagnostic diagram is constructed through simulations of the capacity reached for a given potential cut-off, represented in the domain of two dimensionless parameters. These parameters represent kinetic and particle-size limitations for Li-ion storage. The present tool aims to facilitate the analysis of single-particle experiments, taking advantage of two different computational languages, popular in scientific computing: Python and C++. The software integrates a Python interface, for user-friendly interaction, with a C++ computational core allowing parallelization via OpenMP, for high computational performance. The software supports different thermodynamic approaches: Langmuir-Frumkin intercalation model and equilibrium potentials derived from experimental data. The inclusion of experimental insertion isotherms allows for the construction of realistic capacity diagrams. The open-source software is available at https://github.com/fernandezfran/galpynostatic/.

In this work, we developed a computational model of lithium deposition on planar electrodes. With this model, we simulated several chronoamperometry experiments considering different deposition probabilities, i.e. cell overpotentials. Since the planar geometry induces an ion concentration gradient profile that continues to evolve toward the bulk electrolyte, we studied the influence of the lithium reservoir and rationalized its role in the real system. The solid electrolyte interface (SEI) is conceptualized as a thin layer whose thickness determines the ion diffusion profile. Additionally, the impact of dendrite formations on electrode roughness and current density profiles was analyzed by comparison with test simulations assuming inhibited dendrite growth. By presenting current density profiles in quantitatively meaningful units, our work narrows the gap for further comparison with experimental measurements. The util- ity of our simulations for determining the electrode area in lithium metal batteries is addressed.

Li–S batteries are promising alternatives due to their proven increased gravimetric capacity compared to Li–ion batteries. However, their development is hindered by many technical issues, one of the most challenging being the dissolution and shuttle of polysulfide species, which causes irreversible loss of cathode material leading to rapid capacity fading. Among the possible strategies to mitigate this effect, the choice of suitable solvents is easy to implement and has large room for improvement. To guide this quest, computationally-aided optimization is a powerful tool, provided that suitable descriptors are used to screen possible solvents. In this work, molecular dynamics simulations were performed for a typical lithium polysulfide Li₂S₆ dissolved in different solvents. Diffusion coefficients and their related activation energies were calculated, and thermodynamic properties like solvation energies and entropies were also evaluated. Additionally, a theoretical framework for computing the relative solubilities of lithium polysulfide is provided. For the set of solvents considered, we found that the system's viscosity appears as an important descriptor to correlate with different system properties. The donor number of the solvent also appears as a valid descriptor, for low-viscosity solvents. In general, it was found that higher viscosity solvents lead to lower diffusion rates and higher polysulfide solubility. These results suggest that the optimal choice to reduce the shuttle is a trade-off between high-viscosity solvents to reduce polysulfide diffusion and low-viscosity solvents to reduce its solubility, which could be further improved by properly tuning the donor number.