MétaCan
Menu
Back to cohort
Record W2060109581 · doi:10.1021/la0497301

Parametric Study on Electrochemical Deposition of Copper Nanoparticles on an Ultrathin Polypyrrole Film Deposited on a Gold Film Electrode

2004· article· en· W2060109581 on OpenAlexaff
Xianxian Zhou, Andrea J. Harmer, Nina F. Heinig, K. T. Leung

Bibliographic record

VenueLangmuir · 2004
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceCopperElectrodeNanocrystalElectrochemistryElectrolyteChemical engineeringDeposition (geology)Colloidal goldNanoparticleThin filmPolypyrroleNanotechnologySubstrate (aquarium)MetallurgyChemistry

Abstract

fetched live from OpenAlex

Monoshaped and monosized copper nanostructured particles have been prepared by potentiostatic electrochemical deposition on an ultrathin polypyrrole (PPY) film, electrochemically grown on a Si(100) substrate sputter-coated with a thin gold film or gold-film electrode (GFE). The crystal size and the number density of the copper nanocrystals have been examined by varying several deposition parameters, including the thickness of the gold film, the PPY film thickness, the applied potential, and the Cu2+ and the electrolyte concentrations for copper deposition. Optimal conditions for uniform growth ofnanocrystals well-dispersed on the GFE have been determined, along with insight into the mechanism of crystal growth. A minimum gold film thickness of 80 nm is required to eliminate the effects of the gold-silicon interface. The PPY film thickness and homogeneity principally affect the shape uniformity of the nanocrystals, while the copper deposition potential could be used to regulate the size and number density of the nanocrystals. Both the Cu2+ and electrolyte concentrations are also found to play important roles in controlling the electrodeposition of nanocrystal growth.

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 score0.007
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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

Citations106
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueLangmuirSame topicElectrodeposition and Electroless CoatingsFrench-language works237,207