MétaCan
Menu
Back to cohort
Record W2729138459 · doi:10.20381/ruor-5055

Electrochemical Promotion of Gold Nanoparticles Supported on Yttria-Stabilized Zirconia

2011· article· en· W2729138459 on OpenAlexvenueno aff
Jong Min Kim

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsCubic zirconiaYttria-stabilized zirconiaNanoparticlePromotion (chess)ElectrochemistryNanotechnologyBusinessMaterials scienceChemistryMetallurgyElectrodePolitical science

Abstract

fetched live from OpenAlex

The feasibility of highly dispersed gold nanocatalyst supported on yttria-stabilized zirconia (YSZ) for the model reactions of C2H4 and CO oxidation is demonstrated for the first time. Gold nanoparticles are synthesized on YSZ powder by chemical reduction of the precursor salt in the mixture of ethanol, water and polyvinylpyrrolidone (PVP). Resulting metal loading of the catalysts are 1 wt.% with average particle sizes ranging from 6 to 9 nm. Results of CO and C2H4 oxidation display catalytic activity at 65 0C and 25 0C for CO and C2H4 oxidation, respectively. The catalytic properties of the catalysts are different due to their average particle size. Electrochemical Promotion of Catalysis (EPOC) of C2H4 oxidation is demonstrated. Application of constant potential difference between two electrodes in the bipolar electrochemical cell led to increase in C2H4 conversion. A proposed mechanism explains the bipolar EPOC phenomenon through formation of O2- flux across the electrochemical cell, resulting in the change of Work Function of gold nanoparticles placed in between the electrodes and is electronically isolated.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.161
Teacher spread0.153 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicCatalytic Processes in Materials ScienceFrench-language works237,207