Invited Presentation: Theory and Simulation of Multiscale Interplays Between Mechanical and Electrochemical Mechanisms in Fuel Cells and Rechargeable Lithium Batteries
Bibliographic record
Abstract
Thanks to the growing progress in scientific computational techniques, multiscale modeling and numerical simulation combining atomistic, molecular and continuum approaches, have been shownto be powerful tools to bridge the gaps between the chemical/structural properties of materials and the efficiency of electrochemical power generators, such as rechargeable lithium batteries [1-2], fuel cells [3-5] and electrolyzers [6-7]. Getting a deep understanding of operation principles in these devices through theory can help on achieving significant advances on their controlled design and optimization. A new multiscale modeling framework is presented here describing the interplays between electrochemical and mechanical processes induced by materials structural changes during the operation of low temperature PEM Fuel Cells (PEMFCs), conversion lithium ion batteries (CLIBs) and lithium air batteries (LABs). The model is a hierarchical cell level model and aims to couple on the fly: - hybrid Mean Field/Kinetic Monte Carlo sub-models describing elementary kinetic reactions (e.g. Oxygen Reduction Reaction -ORR- on the catalyst in the PEMFC cathode and ORR and LixOy growth kinetics on the carbon substrate in the LAB positive electrode) and the dynamical structure of the electrochemical double layer at the vicinity of the catalyst (PEMFC), conversion particles (CLIB) and carbon substrate (LAB); - Cahn-Hilliard phase field sub-models describing conversion reactions and induced morphological changes in MO particles to form M° and Li2O during discharge in CLIBs; - continuum sub-models describing ionic and O2 transport (PEMFC and LAB) within the composite electrode volume (carbon, polymer, catalyst) with structure-dependent diffusion coefficients that can be calculated from Metropolis Monte Carlo and/or Coarse Grain Molecular Dynamics (CGMD) simulations [5]; - continuum sub-models describing the ionic transport in the separator (CLIB and LAB) and in the polymer membrane (PEMFC). In particular, an application example to PEMFCs is deeply illustrated here, where the model is used to describe the morphological changes of the membrane (evolution of the porosity/tortuosity of hydrophilic channels calculated by CGMD) induced by its chemical degradation (triggered by H2O2 production in the electrodes [8]), and conversely, to describe how these morphological changes affect its effective transport properties (water, proton), the electrode/membrane delamination and the cell performance decay [9]. Relationships between the expected durability of the cell and the type of applied operation mode (constant vs. cycled current, humidification level) are calculated and discussed. Finally, main similitudes and differences between fuel cells and batteries on the interplays between electrochemical and mechanical processes are highlighted, and some remaining challenges to tackle these interplays from physical modeling are underlined. Acknowledgements. Close collaborations with Dr. Marie-Liesse Doublet (ICG, France) on CLIBs and with Dr. Kourosh Malek (SFU, Canada) on CGMD calculations are gratefully acknowledged. References [1] A.A. Franco, RSC Advances, 3 (32) (2013) 130 [2]A.A. Franco, K.H. Xue, ECS J. Solid State Sc. Tech., 2 (10) (2013) M3084 [3]A. A. Franco (Ed.), Polymer Electrolyte Fuel Cells: Science, Applications and Challenges, Taylor and Francis Group, FL, USA (2013); [4] A.A. Franco et al., Electrochim. Acta (2011) 56 (28) (2011) 10842 [5] K. Malek, A.A. Franco, J. Phys. Chem. B, 115 (25) (2011) 8088. [6] L. F. Lopes Oliveira, S. Laref, E. Mayousse, C. Jallut, A.A. Franco, PCCP, 14(2012)10215. [7] L.F. Lopes Oliveira, C. Jallut, A.A. Franco, Electrochimica Acta, 110 (2013) 363. [8]A.A. Franco, PEMFC degradation modeling and analysis, book chapter in: Polymer electrolyte membrane and direct methanol fuel cell technology (PEMFCs and DMFCs) - Volume 1: Fundamentals and performance, edited by C. Hartnig and C. Roth (publisher: Woodhead, Cambridge, UK) (2012). [9] A.A. Franco, K. Malek, A microstructure-based model of membrane degradation in PEMFCs, in preparation (2013). Figure. Schematics of our multiscale model for conversion lithium ion batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".