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Record W4392542499 · doi:10.1149/2754-2734/ad3121

Experimental Design of High-Performing Open-Cathode Polymer Electrolyte Membrane Fuel Cells

2024· article· en· W4392542499 on OpenAlexafffund
Anand Sagar, Sachin Chugh, Erik Kjeang

Bibliographic record

VenueECS Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersSimon Fraser UniversityCanada Research Chairs
KeywordsElectrolyteCathodeMembraneMaterials sciencePolymerFuel cellsChemical engineeringComposite materialChemistryEngineeringElectrical engineeringElectrode

Abstract

fetched live from OpenAlex

Open-cathode polymer electrolyte membrane fuel cells (PEMFCs) utilize a unique air-cooled system design to eliminate the humidifiers, air compressor, and liquid cooling loop of conventional, liquid-cooled PEMFC systems, thereby greatly reducing system cost. However, the open-cathode PEMFC performance is restricted by poor humidification, high membrane and charge transfer resistances, and overheating due to inefficient thermal and water management. This work aims to strategically modify the membrane electrode assembly (MEA) design to overcome these issues and achieve high open-cathode PEMFC performance that approaches that of liquid-cooled systems. The use of thinner membrane along with short side chain ionomer is found to elevate the cell performance due to increased water retention at the cathode catalyst layer (CCL) and decreased ohmic losses. Thinner gas diffusion layers with high porosity enable additional cell performance increment by improving oxygen availability at the CCL. An overall current density rise of 88% at 0.6 V and 53% at 0.4 V is achieved by the strategically designed MEA for open-cathode cells. The enhanced power density enabled by the custom MEA can both reduce the stack cost and expand the power range of open-cathode PEMFCs, thus expanding their potential use for low-cost fuel cell system applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.564

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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.

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

Citations5
Published2024
Admission routes2
Has abstractyes

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