Microwave-assisted 1-decene oligomerization: In-depth analysis, kinetic insights, and thermodynamic perspectives with HY zeolite catalyst
Why this work is in the frame
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Bibliographic record
Abstract
Oligomerization of 1-olefins is a promising method for creating high-quality synthetic fuels and base oils. The optimization of 1-decene oligomerization was conducted in a micro-wave-assisted batch reactor using HY-zeolite as a catalyst. The Box-Behnken design, a response surface methodology, was employed to optimize the reaction by varying catalyst dose (0.02–0.15 g), reaction time (5–60 min), and temperature (100–250 °C). A quadratic regression model with an R 2 value above 91 % indicated a strong correlation between experimental and predicted data. The study revealed that higher temperatures and catalyst doses enhanced conversion, while extended reaction time initially boosted conversion but later caused a decline. Temperature and catalyst dose were identified as the most significant factors, while time had a statistically insignificant effect. The maximum conversion was obtained at optimal conditions of 175 °C, 30 min, and 0.11 g of catalyst. Product analysis using Gas Chromatography Mass Spectrometry, Fourier Transform Infrared Spectroscopy (FTIR), and Gel Permeation Chromatography (GPC) confirmed the formation of oligomers, with FTIR showing the disappearance of monomeric double bonds and GPC confirming oligomers with a molecular weight of approximately 700 g/mol. The kinetic study revealed an activation energy of 13.4 kJ/mol, and a reaction order of 1. The oligomerization process was determined to be endothermic, with positive adsorption enthalpy values for both dimerization and trimerization reactions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it