Direct Hydrocarbon Upgrade from n-Hexane, n-Octane, and n-Decane Using a Microsecond Pulsed Dielectric Barrier Discharge Non-Thermal Plasma
Bibliographic record
Abstract
Abstract 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".