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Record W2498384956 · doi:10.1149/2.0141611jes

Coated Stainless Steel Bipolar Plates for Proton Exchange Membrane Electrolyzers

2016· article· en· W2498384956 on OpenAlexaff
Philipp Lettenmeier, R. Wang, Rami Abouatallah, Fabian Burggraf, Aldo Saul Gago, K. Andreas Friedrich

Bibliographic record

VenueJournal of The Electrochemical Society · 2016
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsHydrogenics (Canada)
Fundersnot available
KeywordsMaterials scienceProton exchange membrane fuel cellStack (abstract data type)ElectrolysisPhysical vapor depositionMetallurgyTitaniumCathodeSputter depositionHydrogenSurface modificationComposite materialSputteringElectrodeMembraneCoatingChemical engineeringNanotechnologyElectrolyteChemistryThin film

Abstract

fetched live from OpenAlex

Given its rapid response to fluctuating currents and wide operation range, proton exchange membrane (PEM) water electrolysis is utmost suitable for generation of hydrogen from renewable power. However, it is still hindered by the high cost of the stack components compared to those used in the alkaline technology. In particular, the titanium bipolar plates (BPP) are an issue and the replacement of this metal by stainless steel is a challenge, due to the highly corrosive environment inside PEM electrolyzer stack. Herein, we coat stainless steel BPPs with 50–60 μm Ti and 1.5 μm Pt coatings by vacuum plasma spraying (VPS) and magnetron sputtering physical vapor deposition (PVD), respectively. The BPPs are evaluated at constant 1 A cm−2 for more than 1000 h. The thermally sprayed Ti coatings fully protect the stainless steel substrate during this period of time, and the Pt surface modification allows achieving a cell performance comparable to the baseline.

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 score0.179
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.224
Teacher spread0.214 · 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

Citations98
Published2016
Admission routes1
Has abstractyes

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