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Record W3127392526 · doi:10.1063/5.0040171

Synthesis of nickel and cobalt oxide nanoparticles by pulsed underwater spark discharges

2021· article· en· W3127392526 on OpenAlexafffund
Thomas Merciris, Flavien Valensi, Ahmad Hamdan

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesKing Abdullah University of Science and TechnologyCanada Foundation for Innovation
KeywordsNanoparticleMaterials scienceCobaltNickelNanomaterialsElectrodeAnodeChemical engineeringTransmission electron microscopyDistilled waterCobalt oxideNanotechnologyAnalytical Chemistry (journal)MetallurgyChemistry

Abstract

fetched live from OpenAlex

Electrical discharges in liquids are considered an efficient and ecological technique of nanoparticle synthesis via controlled erosion of electrodes. Herein, we use spark discharges between Co–Co, Ni–Ni, Co–Ni, or Ni–Co electrodes immersed in distilled water to synthesize Co and/or Ni nanoparticles, as well as their oxides. When mixed electrodes are used (Co–Ni or Ni–Co), both Co and Ni nanoparticles are produced, and the major species is dictated by the nature of the anode pin. The characteristics of nanoparticles synthesized under varying conditions of pulse width (100 and 500 ns) and voltage amplitude (5 and 20 kV) are analyzed by transmission electron microscopy. Within the investigated discharge conditions, it is not possible to produce Co–Ni nanoalloys; however, core–shell nanoparticles are observed among the Ni and Co nanoparticles. Finally, the direct optical bandgaps of the nanomaterials are determined using UV-visible absorption spectroscopy.

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 score0.002

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations33
Published2021
Admission routes2
Has abstractyes

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