MétaCan
Menu
← Back to cohort
Record W3024637027 · doi:10.1149/ma2020-01381677mtgabs

A Numerical Study on the Impact of Cathode Catalyst Layer Loading on the Open Circuit Voltage

2020· article· en· W3024637027 on OpenAlexaff
M. S. Moore, Shantanu Shukla, Kunal Karan, Adam Z. Weber, Iryna V. Zenyuk, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsOverpotentialCathodeOpen-circuit voltageElectrolyteAnodePlatinumChemistryVoltage dropShort circuitHydrogenCatalysisElectrode potentialThermodynamicsChemical engineeringInorganic chemistryElectrodeMaterials scienceVoltageCurrent (fluid)ElectrochemistryPhysical chemistryElectrical engineeringEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The open-circuit voltage (OCV) is the measured potential when no current is drawn from a cell. Theoretically this potential is given by the equilibrium potential, which is determined from thermodynamics based on the operating conditions. In practice, the actual potential at zero current is measured to be approximately 300 mV lower than the theoretical value. There are a number of possible causes for the lower OCV [1,2], however the most commonly accepted is that hydrogen crossing from the anode to the cathode will react in the presence of the platinum catalyst, thereby developing a mixed potential [2,3,4]. The OCV will therefore depend on the relative kinetics of the hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) in the cathode such that when the ORR is equal to the HOR in the cathode, the OCV is reached, see Figure 1A. The overpotential required to achieve the required ORR will determine the potential at which OCV occurs and the overpotential required will be impacted by the platinum loading in the cathode. This effect is shown experimentally, where the OCV is seen to drop significantly at loadings less than 0.05mg/cm2 , as shown in Figure 1B. The two-dimensional, macro-homogeneous model in OpenFCST [5] is extended to allow multiple reactions in an electrode and used to study the impact of platinum loading on OCV. The model accounts for the transport of oxygen, hydrogen, water vapour and solved water, as well as the potentials of both the solid and electrolyte phases. Both the ORR and HOR are considered in the cathode, and as the hydrogen crossover rate is only of the order of 1 mA/cm2, the accuracy of the kinetic models used for the ORR and HOR are studied. Furthermore, the impact of the HOR kinetics is examined by considering recent experimental studies [7,8] that have shown the HOR is severely limited at high overpotentials by anion adsorption and oxide formation. This work shows that the double-trap kinetic model [6] for the ORR is able to match the polarization data in the kinetic region as well as correctly predict the OCVs for platinum loadings of 0.014-0.113 mg/cm2 . The commonly used Tafel equation, in contrast, cannot simultaneously fit both the kinetic region of the polarisation curve and the OCVs. Using a HOR model that reproduces the kinetic data showing limited activity at high overpotentials does not accurately capture the OCV and predicts that hydrogen would escape through the cathode. The impact of microscale transport is studied using the ionomer covered carbon particle (ICCP) model. A parametric study on the parameters controlling the dissolution of hydrogen into the ionomer thin film and its diffusion to the reaction site indicate that hydrogen transport is not limiting at these scales for the cell structural parameters used in this study, but may be important at higher ionomer film thicknesses. Changing the operating oxygen partial pressure shows that the double trap model in ref. [6] does not accurately capture the change in reaction rate with the change in oxygen concentration. This work demonstrates that modeling the OCV is helpful in fitting the kinetic region of the polarisation curve, as an accurate fit here and at OCV means that the kinetic model is accurate over several orders of magnitude in current and approximately 100mV of potential. References: [1] J. J. Giner-Sanz et al., Int. J. Hydrogen Energ., 39 (2014) 13206-13216 [2] S. A. Vilekar et al., J. Power Sources, 195 (2010) 2241–2247 [3] V. M. Ehlinger et al., J. Electrochem. Soc., 166, (7) F3255-F3267 (2019) [4] A. A. Kulikovsky, Electrochimica Acta, 247 (2017) 730–735 [5] M. Secanell et al., Electrochem. Soc. Trans., 64 (3) 655-680 (2014) [6] M. Moore et al., J. Electrochem. Soc., 160 (6) F670-F681 (2013) [7] J. Durst et al., J. Electrochem. Soc., 162 (1) F190-F203 (2015) [8] C. M. Zalitis et al., Phys. Chem. Chem. Phys., 2013, 15, 4329 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 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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.277
Teacher spread0.224 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→