Electrochemical electrodes comprise multiple phenomena at different scales. Several works have tried to model such phenomena using statistical techniques. This paper proposes a novel process to work with reduced size images to reconstruct microstructures with the Simulated Annealing method. Later, using the Finite Volume Method, it is verified the effect of the image resolution on the effective transport coefficient (ETC). The method can be applied to synthetic images or images from the Scanning Electron Microscope. The first stage consists of obtaining the image of minimum size, which contains at least 98% of the statistical information of the original image, allowing an equivalent statistical study. The image size reduction was made by applying an iterative decimation over the image using the normalized coarseness to compare the amount of information contained at each step. Representative improvements, especially in processing time, are achieved by reducing the size of the reconstructed microstructures without affecting their statistical behavior. The process ends computing the conduction efficiency from the microstructures. The simulation results, obtained from two kinds of images from different materials, demonstrate the effectivity of the proposed approach. It is important to remark that the controlled decimation allows a reduction of the processor and memory use during the reconstruction and ETC computation of electrodes.

FDTD method opened a fertile research area on the numerical analysis of electromagnetic phenomena under a wide range of media and propagation conditions, providing an extensive analysis of electromagnetic behaviour like propagation, reflection, refraction, and multitrajectory phenomena. In this paper, we present an optimised FDTD-CPML algorithm, focused in saving memory while increasing the performance of the algorithm. We particularly implement FDTD-CPML method at high frequency bands, used in several telecommunications applications as well as in nanoelectromagnetism. We show an analysis of the performance of the algorithm in single and double precision, as well as a stability of the algorithm analysis, from where we conclude that the implemented CPML ABC constitutes a robust choice in terms of precision and accuracy for the high frequencies herein considered. It is important to recall that the CPML ABC parameters provided in this paper are fixed for the tested range of frequencies, that is, from MHz to THz.

The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).

Solar photovoltaic (PV) expansion in fast-growing tropical regions requires engineering-based frameworks that compare deployment pathways in terms of electricity generation, spatial footprint, and economic performance. This study develops a techno-spatial and economic assessment of rooftop versus land-based PV deployment in a biodiversity-sensitive region of the Mexican Caribbean. The novelty lies in linking the technical, spatial, ecological, and financial dimensions of rooftop and land-based PV deployment within a single place-based framework. The framework uses a common annual electricity-output basis and integrates three modules: rooftop PV technical potential estimated from housing-census data and conservative performance assumptions; an equivalent-generation land-based PV counterfactual to estimate spatial footprint and conditional ecological exposure; and a household-scale discounted cash-flow assessment under Mexico’s subsidized residential tariff category 1C and high-consumption residential tariff (DAC, Doméstica de Alto Consumo). Under baseline assumptions, rooftop PV could provide approximately 227 megawatt-peak ($\mathrm{M}{\mathrm{W}}_{\mathrm{p}}$) of installed capacity and 330 gigawatt-hours per year ($\mathrm{G}\mathrm{W}\mathrm{h} {\mathrm{y}\mathrm{r}}^{-1}$) without additional land occupation. Producing the same output through land-based PV would require about 486 hectares ($\mathrm{h}\mathrm{a}$) and, under a forest-overlap scenario, could imply 89,600–95,300 tonnes of carbon dioxide (${\mathrm{t} \mathrm{C}\mathrm{O}}_2$) in potential conversion-related emissions. Rooftop PV showed positive economic performance under both tariffs, with stronger returns under DAC conditions. The study provides a bounded engineering-oriented comparison of PV deployment pathways rather than predictions of siting, land conversion, or adoption.