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Record W2259855982 · doi:10.1149/ma2015-02/37/1500

Accelerated Stress Tests on Fuel Cell Cathode Catalysts: A Material Balance Approach Combining Modeling and Experiment

2015· article· en· W2259855982 on OpenAlexaff
Cynthia A. Rice, Patrick Urchaga, Jingwei Hu, Thomas Kadyk, Michael Eikerling

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser UniversityAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsCatalysisProton exchange membrane fuel cellCathodeMaterials scienceElectrolyteDegradation (telecommunications)DurabilityChemical engineeringDissolutionBattery (electricity)ChemistryPower (physics)Composite materialElectrical engineeringEngineeringElectrodeOrganic chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Proton Exchange Fuel Cells (PEFCs) represent an environmentally benign power sources for automotive transportation. The current major hurdles for the mass commercialization of automotive PEFC technology are cost and durability of the cathode catalyst layer. Catalyst degradation occurs due to voltage transients experienced by the cathode catalyst during normal drive cycles and start-up/shut-downs (SUDS). Urban drive cycles involve rapid stop-go transients (idle-to-peak power) that take the cathode catalyst potential from >0.9 V (idle) to around 0.6 V (peak power). Frequent potential changes with high peak potentials (> 1.2 V) drastically accelerate the loss of active catalyst particles and the decay of the of electrochemically active surface area (ECSA). The main degradation mechanisms affecting the carbon-supported Pt-based catalyst are dissolution/redeposition, coagulation and detachment. Accelerated stress tests (ASTs), performed to decouple and quantify contributions of different catalyst degradation mechanisms are typically performed ex situ to eliminate degradation effects caused by the other materials within the cell, such as the proton conducting membrane and the gas diffusion media. The AST results presented herein evaluated Pt/C degradation under operationally relevant conditions temperature (22°C and 70°C), upper potential limit (UPL, 0.9 V and 1.2 V) and potential wave form (triangle vs. square waves). A comprehensive material balance analysis was performed to track changes in the state of Pt during the AST. This analysis included the determination of ECSA, particle size distribution and amount of dissolved Pt in the electrolyte solution. These results were analyzed with a dynamic model that couples the three catalyst degradation mechanisms to quantify their relative contributions. The model relates the kinetic rates of the degradation processes to the evolution of the particle size distribution. The loss of ECSA during 25,000 AST cycles at 70°C by periodically performing cyclic voltammetry (0.02 V « 0.6 V at 10 mV sec -1 ) and integrating the hydrogen desorption charge (210 µC cm 2 ). Generally, ECSA loss was found to be accelerated by the SW profile and by the high UPL of 1.2V. The Pt particle size distribution was measured by Transmission Electron Microscopy (TEM). The beginning of life (BOL) particle median size was 2 nm. Figure 1 shows the change in the particle size distribution after 25,000 AST cycles at 70°C. The tests with higher UPL had the most significant increase in mean particle size and width of the distribution. The combined modeling and material balance preliminary results demonstrate a significant role of the effective surface tension of the catalyst dominating the kinetics of the dissolution/Redeposition mechanism at 1.2 V, suggesting that Pt dissolution is strongly coupled to Pt oxide formation and reduction. The analysis fit the enhanced degradation due to the square wave acceleration of dissolution/Redeposition compared to the triangular wave. Figure 1. Histograms from TEM particle size analysis for 25k AST cycles at 70°C. Upper potential limit of (A) 0.9V and (B) 1.2V. Lines are simulated particle size distributions from dynamic model analysis. Figure 1

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.238
Teacher spread0.206 · 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 teacher head, not a consensus.

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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