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Record W4281612043 · doi:10.1002/aesr.202200024

Long‐Term Operation of Nb‐Coated Stainless Steel Bipolar Plates for Proton Exchange Membrane Water Electrolyzers

2022· article· en· W4281612043 on OpenAlexaff
Svenja Stiber, Michael Hehemann, Marcelo Carmo, Martin Müller, Katherine E. Ayers, Christopher Capuano, Nemanja Danilovic, Tobias Morawietz, Indro Biswas, Pawel Gazdzicki, Jan‐Frederik Heger, Aldo Saul Gago, K. Andreas Friedrich

Bibliographic record

VenueAdvanced Energy and Sustainability Research · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsQueen's University
FundersHelmholtz-Gemeinschaft
KeywordsMaterials scienceProton exchange membrane fuel cellCorrosionX-ray photoelectron spectroscopyElectrolysisElectrolysis of waterHydrogenChemical engineeringHydrogen productionAmorphous solidContact angleMetallurgyElectrodeComposite materialFuel cellsChemistryElectrolyteCrystallography

Abstract

fetched live from OpenAlex

Proton exchange membrane water electrolysis (PEMWE) is the most promising technology for green hydrogen production using renewable electricity, but it is expensive due to the Ti bipolar plates (BPPs). Herein, a PEMWE stack with coated stainless steel (ss) BPPs (Nb/Ti/ss‐BPP and Nb/ss‐BPP) is reported, which operates for about 14 000 h at 1.63 ± 0.12 A cm−2 and 65 °C. The average degradation rate is as low as 1.2% or 5.5 μV h−1. Scanning electrode microcopy reveals no signs of corrosion of the ss beneath the coatings. The interfacial contact resistance increases due to the formation of poorly conductive amorphous Nb oxides, as shown by atomic force microscopy and X‐Ray photoelectron spectroscopy, although it does not affect the cell performance. The results prove that Ti is not needed anymore as base material for manufacturing the BPPs, thus the cost of PEMWE can be significantly reduced.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.020
GPT teacher head0.313
Teacher spread0.293 · 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

Citations51
Published2022
Admission routes1
Has abstractyes

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