MétaCan
Menu
Back to cohort
Record W2547752795 · doi:10.1002/anie.201608601

The Stability Challenges of Oxygen Evolving Catalysts: Towards a Common Fundamental Understanding and Mitigation of Catalyst Degradation

2016· review· en· W2547752795 on OpenAlexaff
Camillo Spöri, Jason Tai Hong Kwan, Arman Bonakdarpour, David P. Wilkinson, Peter Strasser

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDegradation (telecommunications)CatalysisBiochemical engineeringOxygen evolutionElectrolysisMaterials scienceProcess engineeringAnodeNanotechnologyChemical engineeringEnvironmental scienceComputer scienceChemistryEngineeringElectrochemistryElectrodeTelecommunications

Abstract

fetched live from OpenAlex

This Review addresses the technical challenges, scientific basis, recent progress, and outlook with respect to the stability and degradation of catalysts for the oxygen evolution reaction (OER) operating at electrolyzer anodes in acidic environments with an emphasis on ion exchange membrane applications. First, the term "catalyst stability" is clarified, as well as current performance targets, major catalyst degradation mechanisms, and their mitigation strategies. Suitable in situ experimental methods are then evaluated to give insight into catalyst degradation and possible pathways to tune OER catalyst stability. Finally, the importance of identifying universal figures of merit for stability is highlighted, leading to a comprehensive accelerated lifetime test that could yield comparable performance data across different laboratories and catalyst types. The aim of this Review is to help disseminate and stress the important relationships between structure, composition, and stability of OER catalysts under different operating conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
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 head0.053
GPT teacher head0.295
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations899
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicElectrocatalysts for Energy ConversionFrench-language works237,207