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Record W4399126216 · doi:10.21203/rs.3.rs-4414994/v1

Direct Hydrocarbon Upgrade from n-Hexane, n-Octane, and n-Decane Using a Microsecond Pulsed Dielectric Barrier Discharge Non-Thermal Plasma

2024· preprint· en· W4399126216 on OpenAlexaff
Saif Marji, Gabriela Baez Zaldivar, Pierre‐Luc Girard‐Lauriault

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrosecondDecaneHexaneDielectric barrier dischargeHydrocarbonPlasmaNonthermal plasmaOctaneUpgradeThermalMaterials scienceDielectricAnalytical Chemistry (journal)ChemistryOrganic chemistryThermodynamicsPhysicsOptoelectronicsNuclear physics

Abstract

fetched live from OpenAlex

<title>Abstract</title> Conventional chemical processing methods, employed for transforming hydrocarbon mixtures into more valuable forms, are known to consume high amounts of energy and produce a substantial amount of greenhouse gas emissions. This paper investigates an alternative approach employing non-thermal plasma, in a controlled temperature environment, to synthesize higher-order hydrocarbons. The method examined in this paper, has the potential to reduce energy requirements and mitigate CO2 emissions. Effects of temperature and hydrocarbon chain length on liquid and gas production efficiency are studied. A comparative analysis of the different hydrocarbons as reactants underscores the promising attributes of n-octane in this application. With the proposed reactor configuration, the highest average liquid production efficiency was found in n-octane at 20ºC. Organic compounds with carbon chain lengths as large as 20 carbons where successfully synthesized in the reactor configuration when using decane as the reactant. The observed trends alluded to different chemical reaction pathways being prevalent in different temperature conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.462
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.035
GPT teacher head0.355
Teacher spread0.320 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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