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Record W2999856169 · doi:10.1021/acssuschemeng.9b06767

Hierarchical Chestnut-Burr Like Structure of Copper Cobalt Oxide Electrocatalyst Directly Grown on Ni Foam for Anion Exchange Membrane Water Electrolysis

2020· article· en· W2999856169 on OpenAlexaff
Yoo Sei Park, Myeong Je Jang, Jaehoon Jeong, Sung‐Min Park, Xiaolei Wang, Min Ho Seo, Sung Mook Choi, Juchan Yang

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersKorea Institute of Materials Science
KeywordsElectrocatalystElectrolysisMaterials scienceChemical engineeringOxygen evolutionCurrent collectorElectrolyteCobalt oxideNanoporeNanotechnologyElectrodeIon exchangeCopperOxideElectrochemistryChemistryMetallurgyIonOrganic chemistry

Abstract

fetched live from OpenAlex

Uniquely nanostructured CuCo2O4 is presented as an electrocatalyst for oxygen evolution reactions (OER). CuCo2O4 particles in a chestnut-burr-like shape (CCO*, where ∗ = chestnut burr) were hydrothermally synthesized around fibers of Ni foam substrates as current collectors. Chestnut burrs 4 μm on average had thorns consisting of less than five threads. Each thread was made of a consecutive array of nanobeads less than 10 nm. Nanovoids or nanopores were found between nanobeads. The chestnut-burr structure of CCO* allowed IrO2-overwhelming OER activity. By using the hierarchically nanostructured electrocatalyst directly grown on current collectors without binders and conducting agents, high performances of anion exchange membrane (AEM) electrolysis was demonstrated. Three merits of the electrode architecture were emphasized. First, mass transfer pathways for reactants and products were secured in a microscale between thorns and in a nanoscale between nanobeads. Second, more active sites were exposed to electrolytes in the hierarchical structure. Third, direct growth of active materials on conductive substrates improved adhesion and electrical conduction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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.

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

Citations76
Published2020
Admission routes1
Has abstractyes

Explore more

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