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Record W2804451318 · doi:10.1088/1361-6463/aac46f

Carbon-based nanomaterial synthesis using nanosecond electrical discharges in immiscible layered liquids: n-heptane and water

2018· article· en· W2804451318 on OpenAlexaff
Ahmad Hamdan, Min Suk

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de Montréal
FundersKing Abdullah University of Science and Technology
KeywordsNanosecondHeptaneNanomaterialsMaterials scienceCarbon fibersChemical engineeringNanotechnologyChemistryOrganic chemistryComposite materialPhysicsEngineeringOptics

Abstract

fetched live from OpenAlex

Abstract Plasmas in- or in-contact with liquids have been extensively investigated due to their high potential for a wide range of applications including, but not limited to, water treatment, material synthesis and functionalization, bio-medical applications, and liquid fuel reformation. Recently, we successfully developed a discharge using two immiscible liquids, having very different electrical permittivities, which could significantly intensify the electric field intensity. Here, we establish nanosecond discharges at the interface n -heptane-water (with respective relative dielectric permittivities of 2 and 80) to enable the synthesis of carbon-based nanomaterials. A characterization of the as-synthesized material and the annealed (500 °C) material, using various techniques (Fourier-transform, infra-red, scanning and transmission electron microscopes, etc), shows that the as-synthesized material is a mixture of two carbon-based phases: a crystalline phase (graphite like) embedded into a phase of hydrogenated amorphous carbon. The existence of two-phases may be explained by the non-homogeneity of the discharge that induces various chemical reactions in the plasma channel.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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.

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

Citations14
Published2018
Admission routes1
Has abstractyes

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