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Record W2998877191 · doi:10.1063/1.5129647

Synthesis of copper and copper oxide nanomaterials by electrical discharges in water with various electrical conductivities

2020· article· en· W2998877191 on OpenAlexaff
X. Glad, Jacopo Profili, Min Suk, Ahmad Hamdan

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsUniversité de Montréal
FundersKing Abdullah University of Science and Technology
KeywordsCopperNanomaterialsElectrical resistivity and conductivityCupriteConductivityMaterials scienceCopper oxideNanoparticleTransmission electron microscopyChemical engineeringAnalytical Chemistry (journal)Inorganic chemistryNanotechnologyChemistryMetallurgyEnvironmental chemistry

Abstract

fetched live from OpenAlex

In the present study, Cu-based nanomaterials are synthesized by initiating spark discharges between two copper electrodes immersed in de-ionized water. The electrical conductivity of water is varied by adding HCl at very low concentrations. The discharge and water properties are determined by measuring the current-voltage waveforms and monitoring water acidity and conductivity, respectively. Scanning electron and transmission electron microscopy analyses of the produced nanoparticles show that increasing water conductivity, by adding HCl, promotes the synthesis of Cu-based nanomaterials. Depending on the initial water conductivity, various nanostructures are observed, including nanoflakes of cuprite (Cu2O), dendrites with high Cu content, and unordered micrometric aggregates with a mixed Cu/Cu2O content. The initial water conductivity also affects the chemical structure, mainly the relative Cu/Cu2O crystalline content.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.011
GPT teacher head0.218
Teacher spread0.206 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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