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Pulsed nanosecond discharge in heptane in contact with Ag solution: Feasibility of nanoparticles synthesis

2021· article· en· W4207063624 on OpenAlexaff
Kyana Mohammadi, Ahmad Hamdan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMaterials scienceHeptaneNanoparticleNanomaterialsNanocompositeSilver nanoparticleAnalytical Chemistry (journal)IonChemical engineeringNanotechnologyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. Plasma-liquid systems have a high potential of producing numerous nanomaterials due to their easy substructure, environmentally friendly, high efficiency, and safety. In this paper, we build a novel Plasma-liquid technique in which spark discharges at 22 kV of amplitude and 500 ns of pulse width are ignited in heptane. This hydrocarbon liquid is in contact with the conductive solution containing silver nitrate solution. The main reason of producing ag nanoparticles is the reducing silver ions in the solution which is happened by the interaction between these ions and the high-pressure spark discharge which has a high density of species. After 30 minutes treatment with discharges, liquid samples from both heptane and solution side were collected to analysis by different techniques like FTIR, XRD, EDS, TEM, etc. It is determined that in heptane side, nanocomposites of silver nanoparticles in two ranges; less than 10 nm and between 10 to 45 nm are embedded in carbon matrix. And silver nanoparticles from 10 to 159 nm are synthesized in the solution sample. To study the effect of different pulse width on produced nanomaterials, the experiments repeat at shorter pulse width (100 ns). The results show the same materials in both liquids but, the size distribution of nanoparticles was smaller than large pulse width.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.956

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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