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Record W4306399009 · doi:10.3390/nano12203603

A Versatile Route for Synthesis of Metal Nanoalloys by Discharges at the Interface of Two Immiscible Liquids

2022· article· en· W4306399009 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCourtois Foundation
KeywordsCyclohexaneMaterials scienceCatalysisNanoparticleElectrodeChemical engineeringMetalNitrateDecompositionNanomaterialsNanotechnologyInorganic chemistryChemistryMetallurgyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Discharge in liquid is a promising technique to produce nanomaterials by electrode erosion. Although its feasibility was demonstrated in many conditions, the production of nanoalloys by in-liquid discharges remains a challenge. Here, we show that spark discharge in liquid cyclohexane that is in contact with conductive solution, made of a combination of Ni-nitrate and/or Fe-nitrate and/or Co-nitrate, is suitable to produce nanoalloys (<10 nm) of Ni-Fe, Ni-Co, Co-Fe, and Ni-Co-Fe. The nanoparticles are synthesized by the reduction of metal ions during discharge, and they are individually embedded in C-matrix; this latter originates from the decomposition of cyclohexane. The results open novel ways to produce a wide spectrum of nanoalloys; they are needed for many applications, such as in catalysis, plasmonic, and energy conversion.

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.

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 score0.027
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.234
Teacher spread0.229 · 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