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Record W2165324275 · doi:10.1149/1.3551487

Electrodeposition and Electroless Deposition of Metallic Powders: A Comparison

2011· article· en· W2165324275 on OpenAlexfundno aff
Stojan S. Djokić, Nebojša D. Nikolić, Predrag Živković, Konstantin Popov, Nada Djokić

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsOverpotentialMaterials scienceMetalCopperGalvanic cellDeposition (geology)Chemical engineeringPrecipitationMetallurgyReducing agentInorganic chemistrySingle displacement reactionElectrochemistryElectrodeChemistry

Abstract

fetched live from OpenAlex

Electrodeposition and electroless deposition of metallic powders were comparatively investigated. Electrodeposition of copper is accompanied with the simultaneous hydrogen evolution, which significantly influences the morphology of Cu powder. At lower overpotentials, branched dendrites were produced. At higher overpotentials honeycomb-like deposits of copper were obtained. Formation of silver powders was characterized by the comparison of the exchange and limiting current densities. Instantaneous growth of dendrites starts at low overpotential due to large exchange current density in silver nitrate solution. Formation of powders such as Ni, Co, Ag, Pd and Au from homogenous solutions using an appropriate reducing agent or via galvanic displacement reaction was demonstrated. The hydrolysis of metallic ions is crucial in the deposition metallic powders via electroless deposition from homogenous solutions. Oxides, such as Ag2O, Cu2O and CuO, suspended in water can successfully be reduced with an appropriate reducing agent, leading to the precipitation of metallic powders.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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

Citations30
Published2011
Admission routes1
Has abstractyes

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