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Record W2601267014 · doi:10.1002/cssc.201700369

Self‐Assembly of Spinel Nanocrystals into Mesoporous Spheres as Bifunctionally Active Oxygen Reduction and Evolution Electrocatalysts

2017· article· en· W2601267014 on OpenAlexafffund
Dong Un Lee, Jingde Li, Moon Gyu Park, Min Ho Seo, Wook Ahn, Ian Stadelmann, Luis Ricardez‐Sandoval, Zhongwei Chen

Bibliographic record

VenueChemSusChem · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooKorea Institute of Energy ResearchCompute Canada
KeywordsOxygen evolutionSpinelNanocrystalMesoporous materialOxygenElectrochemistryElectrocatalystChemistryOxideInorganic chemistryChemical engineeringCatalysisMaterials scienceElectrodeNanotechnologyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The present work introduces spinel oxide nanocrystals self‐assembled into mesoporous spheres that are bifunctionally active towards catalyzing both the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). The electrochemical evaluation reveals that (Ni,Co) 3 O 4 demonstrates a significantly positive‐shifted ORR onset and half‐wave potentials [−0.127 and −0.292 V vs. saturated calomel electrode (SCE), respectively], whereas Co 3 O 4 results in a negative‐shifted OER potential (0.65 V vs. SCE) measured at 10 mA cm −2 . Based on the DFT analysis, the potential at which all oxygen intermediate reactions proceed spontaneously is the highest for (Ni,Co) 3 O 4 ( U =0.66 eV) during ORR, whereas it is the lowest for Co 3 O 4 ( U =2.09 eV) during OER. The high ORR activity of (Ni,Co) 3 O 4 is attributed to the enhanced electrical conductivity of the spinel lattice, and the high OER activity of Co 3 O 4 is attributed to relatively weak adsorption energy promoting rapid release of evolved oxygen.

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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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

Citations32
Published2017
Admission routes2
Has abstractyes

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