Subsequent R + O<sub>2</sub> Chemistry of Intermediates Formed in Low-Temperature R + O<sub>2</sub> Reactions: Potential Importance in Modeling Autoignition Behavior
Bibliographic record
Abstract
Comprehensive chemical kinetics models used in the simulation of hydrocarbon and biofuel oxidation rely on accurate prescription of the underlying reaction mechanisms and rate parameters of associated elementary reactions. For practical transportation fuels, such models contain thousands of elementary reactions, which collectively define chain-initiation, -propagation, -branching, and -inhibition pathways. In the low-temperature regime, below approximately 1000 K where R + O2 reactions dominate, primary oxidation intermediates including cyclic ethers, carbonyls, and conjugate alkenes are formed in abundance via unimolecular decomposition of either chemically activated or thermalized radicals, specifically organic peroxy (ROO) or hydroperoxyalkyl species (QOOH). Experimental results from multiplexed photoionization mass spectrometry (MPIMS) experiments are detailed herein for several intermediates, derived initially from R + O2 reactions of hydrocarbons and biofuels, and show that intermediate species formed in the initial steps of oxidation undergo similar reactions to those of the parent molecule, including through QOOH-mediated pathways. Products from QOOH decomposition via chain-inhibition and chain-propagation pathways, namely conjugate alkenes, carbonyls, and cyclic ethers, are detected directly. Despite such rich chemistry involving QOOH radicals, most comprehensive chemical kinetics models neglect the complete description of primary oxidation intermediates, and rather consider a restricted number of reaction pathways. It is suggested that exclusion of the details of the oxidation of these intermediate products may affect the interpretation of combustion simulations using such models.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".