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Record W3041027690 · doi:10.3390/nano10071347

Synthesis of Copper and Copper Oxide Nanomaterials by Pulsed Electric Field in Water with Various Electrical Conductivities

2020· article· en· W3041027690 on OpenAlexaff
Ahmad Hamdan, X. Glad, Min Suk

Bibliographic record

VenueNanomaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de Montréal
FundersKing Abdullah University of Science and Technology
KeywordsNanomaterialsCopperMaterials scienceNanoparticleNanotechnologyCopper oxideElectrodePlasmaOxideElectrical resistivity and conductivityNanostructureElectric fieldNanowireChemical engineeringChemistryMetallurgy

Abstract

fetched live from OpenAlex

Nanomaterial synthesis is a hot research subject that has been extensively studied in the last two decades. Recently, plasmas in liquid systems have been proposed as an efficient means of synthesizing various types of nanomaterials. The formation processes implicate many physical and chemical phenomena that take place at the electrode surface, as well as in the plasma volume, which renders it difficult to fully understand the underlying mechanisms. In this study, we assess the effect of electric field on nanomaterial synthesis in a system composed of two copper electrodes immersed in water, in the absence of an electrical discharge. The obtained results indicate that various nanostructures, including copper nanoparticles, copper oxide nanowires, and/or hollow nanoparticles, may be produced, depending on the electrical conductivity of the solution (adjusted by adding highly diluted HCl to deionized water). The materials synthesized herein are collected and characterized, and a formation mechanism is proposed. Overall, our results provide insight into the physical and chemical phenomena underlying nanomaterial synthesis in plasmas in liquid.

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 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 score0.007
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.178
Teacher spread0.174 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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