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Record W4323307304 · doi:10.1149/1945-7111/acc1a5

Degradation Effects at the Porous Transport Layer/Catalyst Layer Interface in Polymer Electrolyte Membrane Water Electrolyzer

2023· article· en· W4323307304 on OpenAlexaff
Chang Liu, Meital Shviro, Guido Bender, Aldo Saul Gago, Tobias Morawietz, Michael J. Dzara, Indro Biswas, Pawel Gazdzicki, Zhenye Kang, Sarah Zaccarine, Svitlana Pylypenko, K. Andreas Friedrich, Marcelo Carmo, Werner Lehnert

Bibliographic record

VenueJournal of The Electrochemical Society · 2023
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsQueen's University
FundersHydrogen and Fuel Cell Technologies OfficeOffice of Energy EfficiencyForschungszentrum JülichColorado School of MinesU.S. Department of EnergyOffice of Energy Efficiency and Renewable EnergyNational Science Foundation
KeywordsElectrolyteChemical engineeringCoatingIonomerLayer (electronics)Materials scienceCatalysisMembraneDegradation (telecommunications)PolymerAnodeElectrochemistryPlatinumPorosityChemistryElectrodeComposite materialCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

The porous transport layer (PTL)/catalyst layer (CL) interface plays a crucial role in the achievement of high performance and efficiency in polymer electrolyte membrane water electrolyzers (PEMWEs). This study investigated the effects of the PTL/CL interface on the degradation of membrane electrode assemblies (MEAs) during a 4000 h test, comparing the MEAs assembled with uncoated and Ir-coated Ti PTLs. Our results show that compared to an uncoated PTL/CL interface, an optimized interface formed when using a platinum group metal (PGM) coating, i.e., an iridium layer at the PTL/CL interface, and reduced the degradation of the MEA. The agglomeration and formation of voids and cracks could be found for both MEAs after the long-term test, but the incorporation of an Ir coating on the PTL did not affect the morphology change or oxidation of IrO x in the catalyst layer. In addition, our studies suggest that the ionomer loss and restructuring of the anodic MEA can also be reduced by Ir coating of the PTL/CL interface. Optimization of the PTL/CL interface improves the performance and durability of a PEMWE.

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)0.001
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 score0.009
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations60
Published2023
Admission routes1
Has abstractyes

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