Speaker
Description
Promising approaches to address the long‐term depletion of fossil resources and the increase in greenhouse gas emissions, photo‐reactive processes enable the conversion of light energy into storable chemical energy carriers through the implementation of artificial photosynthetic reactions. The design and optimization of these processes, constrained by radiation and highly sensitive to geometrical configurations, aim to achieve efficiencies compatible with large‐scale solar industrialization and require, for that purpose, the development of knowledge models and their computational simulations. In artificial photosynthesis, the modeling of the primary photoelectrocatalytic mechanisms of this conversion reveals a common phenomenological pattern: the drift-diffusion of electrical charges in complex environments such as porous photoanodes, where nanoscale structuring emerges as a major lever for optimization. This descriptive attractor constitutes a distinct class of nonlinear couplings: the drift-diffusive transport of concentrations nonlinearly coupled to electromagnetism. Besides insightful physical representations of these transport phenomena, the demand for robust reference solutions and efficient computations is huge. In this regard, providing both conceptual clarity and computational tools, building structures that bridge physical interpretation and computational feasibility is today a challenge.
From Einstein’s Brownian motion to Feynman’s path-integral picture, the dual interplay between probabilistic perspective and macroscopic deterministic continuous fields continually reshaped how physicists build intuition about transport and propagation. This dual deterministic-probabilistic interpretation, fundamentally based on superposition and linearity, has disseminated in most fields of linear physics as for instance heat conduction, radiative transfer, or electromagnetism mainly because it produces flexible intuitions. In the present work, we have advanced new probabilistic approaches based on branching stochastic processes to the nonlinear drift-diffusion transport of charges in confined domains and complex geometries. Our formulation shows how expectations over a single, well-defined branching path-space recover deterministic concentration maps and opens new routes for statistical estimations of charge carrier concentrations by use of new Branching Backward Monte Carlo algorithms.
In regard to solar fuels production devices using artificial photosynthesis, we implemented these path-space sampling algorithms to estimate electrons concentration inside a semiconducting porous photoanode. Interactions with the computer graphics community have allowed us to advance a numerical implementation which not only behaves well, but also takes advantage of the most advanced techniques handling complexity, and is thus of major interest for computational physicists communities. This work immediately unfolds along two crucial dimensions. On the interpretative front, it fundamentally reshapes our understanding of nonlinear drift-diffusion transport coupled to a model of the electric field in terms of nonlinear propagators. On the computational side, it opens the door to harnessing recent breakthroughs in image synthesis, yielding algorithms whose costs are remarkably insensitive to the geometric complexity. Wherever geometric sophistication and the demand for robust reference solutions impose stringent limits, the presented framework delivers a promising perspective. By decoupling computational effort from the system’s inherent complexity while maintaining rigorous probabilistic foundations, it lays the groundwork for tackling numerous challenges, fundamentally redefining standards of predictive power for nanoscale morphologic optimization by inverse design and scientific interpretation of nonlinear charge carriers transport within porous materials.
| Country | france |
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