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Record W3125393252 · doi:10.1063/5.0036041

Transformation of <i>n</i>-heptane using an in-liquid submerged microwave plasma jet of argon

2021· article· en· W3125393252 on OpenAlexaff
Ahmad Hamdan, Jinglin Liu, Min Suk

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de Montréal
FundersKing Abdullah University of Science and Technology
KeywordsArgonChemistryHeptanePlasmaAnalytical Chemistry (journal)SyngasCombustionJet (fluid)AcetylenePlasmatronMicrowaveHydrogenChemical engineeringOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

The reforming of hydrocarbons has gained much interest as a means to upgrade low-grade fuels and to produce value-added chemicals. Plasmas have been considered one of the potential ways to reform fuels to achieve more effective and cleaner combustion, particularly by producing various hydrocarbons, hydrogen carriers, and oxygenates as well as syngas. Here, we employed a submerged microwave plasma jet of argon to investigate its potential to transform n-heptane. We found that the product selectivities were mainly governed by the effective gas temperature, which we adjusted by changing the energy density of the argon stream. The transformation of n-heptane by this method mostly produced ethylene and acetylene, which is different than the products produced by pyrolysis or a chemical equilibrium composition. Such unique selectivities could be attributed to the rapid quenching of the microwave plasma jet upon direct contact with the colder liquid. The transformation of n-heptane was significantly affected by the interactions between the microwave plasma jet and the liquid n-heptane. To support our results, we include a detailed chemical analysis and discussion of the physical characterization of the microwave plasma jet using optical emission 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 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 score0.007
Threshold uncertainty score0.376

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 head0.025
GPT teacher head0.269
Teacher spread0.244 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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