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Record W4376618814 · doi:10.1021/acsami.3c04573

Electronically Conductive, Multifunctional Polymer Binder for Highly Active, Stable, and Abundant Composite Electrodes for Oxygen Evolution

2023· article· en· W4376618814 on OpenAlexafffund
J. Ping Liu, Steven H. Bergens

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsMaterials scienceElectrical conductorComposite numberElectrodePolymerOxygen evolutionConductive polymerOxygenElectrically conductiveChemical engineeringComposite materialNanotechnologyElectrochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The electrolysis of water to form hydrogen and oxygen is a promising method to store renewable energy. This method requires electrodes that convert water into protons, electrons, and oxygen. We report a multifunctional polymer that conducts electrons and ions and may coreact with the electrocatalyst in the oxygen evolution reaction (OER). The electrodes were prepared in two steps from off-the-shelf reagents. They operate with low loadings of abundant catalysts and are among the most active (100 mA cm –2 at 1.43 V vs RHE (1.41 V, iR -corrected)) and stable electrodes, reported to date under harsh conditions (85 °C, 6 M KOH, 120 h (0.69% loss over the first 14.5 h and then 0.61% loss over 105.5 h)). Control experiments on glassy carbon electrodes showed that the polycarbazole system significantly outperformed a Nafion system of the same catalyst loading. This simple strategy can be applied to other types of electrodes.

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 score0.009

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.239
Teacher spread0.227 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Materials & InterfacesSame topicElectrocatalysts for Energy ConversionFrench-language works237,207