Influence of <i>n</i> ‐butanol and isomers on the combustion mechanisms of isooctane and coke formation based on <scp>ReaxFF</scp> simulation
Bibliographic record
Abstract
Abstract Fuel additives play a significant role in enhancing the thermal stability of fuel combustion. The effect of additives on the combustion of hydrocarbon fuels and the combustion performances of isooctane with three different isomer additives, ( n ‐butanol (1‐BuOH), diethyl ether (DEE), and 2‐butanol(2‐BuOH)), and additive‐free isooctane were explored by ReaxFF simulation in this work. The simulation system was heated to 3000 K at a heating rate of 10 K/ps and kept stable at 3000 K. A variety of combustion products (e.g., small gas molecules and C1‐C8 hydrocarbon compounds) in each system were analyzed, and the reaction paths were speculated based on the computed trajectory. The simulation results showed that the CC bond scission reaction dominated the combustion process of the three additives. All three additives promote the formation of toxic carbonyl compounds such as formaldehyde, while the pure DEE additive has the best inhibition effect on the formation of the coke precursor, C 2 H 2 , C 3 H 4 , and C 3 H 6 . The pure 1‐BuOH additive can shorten the initial reaction time of the reactants. The effects of DEE/1‐BuOH additive on the combustion of isooctane were investigated to obtain a desirable additive mixture with good performance. The expansion of the DEE proportion (80%/20% DEF/1‐BuOH) shows a slightly better coke (C 3 H 4 and C 3 H 6 ) reduction effect, while the inhibition effect is not as obvious as that of a pure DEE additive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".