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Formation of Sn/Zn alloy or core-shell nanoparticles via pulsed nanosecond discharges in liquid toluene

2022· article· en· W4303646995 on OpenAlexaff
Marta Agati, Ahmad Hamdan, Simona Boninelli

Bibliographic record

VenueMaterials Chemistry and Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de Montréal
FundersHorizon 2020
KeywordsNanoparticleMaterials scienceChemical engineeringAnodeParticle (ecology)NanotechnologyAlloyNanostructureTolueneCathodeOxideElectrodeComposite materialMetallurgyChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In the exploration of new techniques for the synthesis of metallic nanoparticles, the possibility to exploit electrical discharges in liquid has arisen as an easy, high throughput and low-cost method. This technique of synthesis offers an extensive playground to produce a wide range of nanostructures with composition highly dependent on that of the electrode and of the liquid medium. Here, we demonstrate the formation of a Sn–Zn nanoalloy (particle diameter <10 nm) using electrical discharge between a Sn anode and a Zn cathode immersed in liquid toluene. Core/shell nanoparticles, with diameter between 12 and 20 nm, are also produced. These particles are composed of a Sn crystalline core and a shell made of Zn, Sn and O. A third class of particles was also found, although being rarer, constituted of large (hundreds of nm) Sn particles, with a thin Sn oxide shell. Detailed structural and chemical characterizations were accomplished via TEM and STEM imaging, as well as STEM-EDX analyses on the single nanoparticles and, considering the complex variety of phenomena taking place in in-liquid plasma, a plausible mechanism of synthesis is proposed.

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 score0.093
Threshold uncertainty score0.505

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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