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Record W2793158096 · doi:10.1149/08511.0091ecst

Effect of Co Addition in Amorphous Ni-Based Alloys for the Alkaline Oxygen Evolution Reaction

2018· article· en· W2793158096 on OpenAlexaff
Kevin M. Cole, Donald W. Kirk, Steven J. Thorpe

Bibliographic record

VenueECS Transactions · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTafel equationOxygen evolutionAmorphous solidMaterials scienceAmorphous metalCyclic voltammetryAlloyElectrochemistryChemical engineeringRaman spectroscopyInorganic chemistryMetallurgyChemistryElectrodeCrystallographyPhysical chemistry

Abstract

fetched live from OpenAlex

An amorphous alloy Ni74.2Co5Nb12.5Y8.3 was synthesized using cryogenic mechanical alloying and evaluated as a catalyst for the oxygen evolution reaction (OER) in alkaline media using cyclic voltammetry and Tafel measurements. Electrochemical testing showed that the amorphous alloy possessed a lower Tafel slope and enhanced kinetics for the OER compared to crystalline Ni and Ni95Co5. Anodic cycling of the amorphous alloy resulted in a lower onset potential for the OER and decreased Tafel values while no changes were observed for crystalline Ni95Co5. Pairing of in situ confocal Raman spectroscopy with anodic cycling showed that the amorphous alloy formed reversible hydrous Co oxy/hydroxides instead of irreversible CoO2 typically seen on crystalline NiCo alloys in KOH. The formation of hydrous Co oxy/hydroxides upon cycling was also accompanied by increased formation of β-NiOOH leading to enhanced catalytic performance of the amorphous alloy over the crystalline counterparts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.359
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.239
Teacher spread0.232 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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