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Record W2268426784 · doi:10.1149/ma2014-01/3/343

Invited Presentation: Theory and Simulation of Multiscale Interplays Between Mechanical and Electrochemical Mechanisms in Fuel Cells and Rechargeable Lithium Batteries

2014· article· en· W2268426784 on OpenAlexaboutno aff
Alejandro A. Franco, María Alejandra Quiroga, Trong-Khoa Nguyen, Kan‐Hao Xue

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMultiscale modelingElectrochemistryLithium (medication)Materials scienceElectrochemical kineticsProton exchange membrane fuel cellCathodeNanotechnologyKinetic Monte CarloElectrodeChemical engineeringChemistryFuel cellsMonte Carlo methodPhysical chemistryComputational chemistryEngineering

Abstract

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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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.008
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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