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Impact of cell design and conditioning on polymer electrolyte membrane water electrolyzer operation

2024· article· en· W4404580439 on OpenAlexafffund
Patrick K. Giesbrecht, Michael S. Freund

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsElectrolyteElectrolysisPolymer electrolyte membrane electrolysisConditioningMembraneElectrolysis of waterChemical engineeringCell voltageEnvironmental scienceMaterials scienceChemistryElectrodeEngineering

Abstract

fetched live from OpenAlex

Integration of polymer electrolyte membrane water electrolyzers (PEMWEs) for clean hydrogen generation requires a robust understanding of the impact cell designs and conditioning protocols have on operation and stability. Here, catalyst-coated electrode and catalyst-coated membrane cells employing Pt/C cathode catalyst layer , an IrO 2 anode catalyst layer, with a platinized titanium mesh or a carbon paper with a microporous layer as the porous transport layer were developed. The impact of cell conditioning above and below 0.25 A cm −2 was investigated using advanced electrochemical impedance spectroscopy analyses and microscopic imaging, with the electrochemical response related to physicochemical processes. Operation below 0.25 A cm −2 prior to operation above 0.25 A cm −2 resulted in anode corrosion and titanium cation contamination, increasing the cell voltage at 1 A cm −2 by 200 mV compared to uncontaminated cells. Conditioning above 0.25 A cm −2 led to non-negligible hydrogen transport resistances due to cathode flooding that resulted in a ca. 50 mV contribution at 1 A cm −2 and convoluted with the anode impedance response. The presence of a microporous layer increased catalyst utilization but increased the cell voltage by 300 mV at 1 A cm −2 due to increased anodic mass transport resistances. These results yield critical insights into the impact of PEMWE cell design and operation on corresponding cell performance and stability while highlighting the need for application dependent standardized operating protocols and operational windows.

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: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.246
Teacher spread0.239 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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