MétaCan
Menu
Back to cohort
Record W4391639148 · doi:10.1149/ma2023-02371719mtgabs

Fuel-Cell Performance and Stability during Liquid-Water Removal Cycles

2023· article· en· W4391639148 on OpenAlexaffabout
Aslan Kosakian, Fei Wei, Jeremy Zhou, Seongyeop Jung, Jonathan Sharman

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFuel cellsEnvironmental scienceWaste managementMaterials scienceChromatographyChemistryChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Water management is crucial for achieving high-performance proton-exchange-membrane fuel cells (PEMFCs), as it helps keep the electrolyte hydrated while avoiding performance degradation and cell shutdown due to liquid-water accumulation. According to ex-situ measurements [1-3], accumulation of liquid water in gas-diffusion layers (GDLs) of PEMFCs is a transient process that can be accompanied with oscillations in capillary pressure and saturation. To understand how liquid-water accumulation and drainage impact PEMFC performance hysteresis and stability, a transient cell-level model that accounts for electrode structure and composition and is computationally efficient is needed. A number of volume-averaged models that describe the electrode structure through pore-size distribution have been developed in the past [4-7], but they are steady-state and thus cannot predict dynamic PEMFC performance. Existing transient models have also not been used to analyze cyclic liquid-water accumulation [8-10]. In this work, a transient two-phase 2D PEMFC model is developed in the open-source fuel-cell modeling software OpenFCST [11] and applied to analyze PEMFC performance hysteresis and stability during liquid-water accumulation and drainage cycles. The model accounts for the electrode structure through a mixed-wettability pore-size-distribution framework and incorporates a novel dynamic boundary condition to describe the experimentally observed cyclic liquid-water accumulation. Results of the numerical simulations are compared to transient current-density and resistance data at two polarization scan rates and during voltage steps measured with an in-house single-channel cell at multiple operating conditions. This work demonstrates how high breakthrough pressure and rapid liquid-water removal from GDLs may cause highly unstable fuel-cell operation with strong hysteresis and oscillations in the polarization curve (such as those in the figure below) that closely resemble experimental reports [12]. Numerical simulations also reveal the existence of a scan rate that maximizes polarization hysteresis due to a match between the time scale of GDL flooding and of a complete voltage sweep. Fast-scan polarization sweeps are shown most suitable for detecting catalyst-layer flooding that depends on its wettability and occurs within single seconds in contrast to GDL flooding that takes hundreds of seconds. Overall, this work brings more attention to the transient analysis of fuel-cell performance under wet conditions. Figure: Polarization-curve oscillations caused by cyclic liquid-water removal from the cathode GDL. Similar fluctuations have been experimentally observed in [12]. Transient graphs show the dynamics of current density and cathode GDL saturation. Operating conditions are 60 °C, 90% RH, 1.5 atm; scan rate is 0.5 mV/s. References J. T. Gostick et al., J. Electrochem. Soc. 157.4 (2010) C. Quesnel et al., J. Phys. Chem. C 119.40 (2015) D. Ziegler, B.Sc. thesis, Hochschule Mannheim / University of Alberta (2020) A. Z. Weber et al., J. Electrochem. Soc. 151.10 (2004) M. Eikerling, J. Electrochem. Soc. 153.3 (2006) V. Mulone and K. Karan, Int. J. Hydrog. Energy 38.1 (2013) Zhou et al., J. Electrochem. Soc. 164.6 (2017) R. J. Balliet and J. Newman, J. Electrochem. Soc. 158.8 (2011) I. V. Zenyuk et al., J. Electrochem. Soc. 163.7 (2016) A. Goshtasbi et al., J. Electrochem. Soc. 166.7 (2019) M. Secanell et al. ECS Transactions 64.3 (2014) C. Ziegler and D. Gerteisen, J. Power Sources 188.1 (2009) 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicFuel Cells and Related MaterialsFrench-language works237,207