Droplet Combustion of Chlorinated Benzenes, Alkanes, and Their Mixtures in a Dry Atmosphere
Bibliographic record
Abstract
Combustion of three chlorobenzenes (monochlorobenzene, dichlorobenzene, trichlorobenzene) and five alkanes (octane, decane, dodecane, tetradecane, and hexadecane) were investigated. Using high-speed video photography, the size and velocity of the burning drops at various points during their lifetime were measured. From such data, the time-variation of a droplet's burning rate was deduced. It was found that the variations of the burning rates for the mixtures were qualitatively similar when viewed as functions of the chlorine to hydrogen atom ratio. Starting from a pure alkane, as Cl/H increased, the burning rate first decreased, then slightly increased, and then fell sharply near Cl/H = 0.5 to approximately the vaporization rate of the pure chlorobenzene. It was also found that prior droplet combustion studies (performed in the H2O-rich atmosphere of combustion-heated reactors) exhibited burning rates of the same liquid blends that were nearly twice the present dry atmosphere results. We have demonstrated that when the hydrogen in the surrounding atmospheric H2O is included in the ratio Cl/H, these previous results yield curves of burning rate vs. Cl/H that are similar to those of the present study. This implies that the enhanced burning rate observed when an alkane is blended into a pure chlorobenzene is partly due to the hydrogen provided by the alkane, and that a similar effect appears achievable by adding water vapor to the gas phase.
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".