In Situ Measurement of Liquid Phase Oxygen during Oxidation
Bibliographic record
Abstract
In liquid phase autoxidation of hydrocarbons, oxygen availability in the liquid phase affects reaction rate and product selectivity. Instead of relying on engineering predictions, this work set out to measure in situ oxygen availability in the liquid phase under oxidation conditions. This was achieved by employing an oxygen sensitive material submersed in the liquid and measuring the change in fluorescence decay with a fluorometer. Mass transfer coefficients ( k L ) of oxygen in benzene and in indan were 3.0 × 10 –6 and 1.3 × 10 –6 ± 0.1 × 10 –6 m/s, respectively, at 50 °C and 19.2 kPa O 2 partial pressure. This enabled the calculation of the maximum oxygen transfer rate and it matched the experimental observations well. Liquid phase oxygen content increased until the oxygen transfer rate and the oxygen consumption rate were balanced, i.e. reached dynamic equilibrium. At high oxygen consumption rate, the oxygen in the liquid phase was consumed and the oxygen consumption rate matched the maximum oxygen transfer rate. These changes could be monitored in situ over time during oxidation. It was further found that liquid phase oxidation violated key assumptions underlying engineering predictions based on the Hatta number. Notably, the kinetic constant for oxygen consumption changed with time, oxidation did not reach “steady state”, and the relationship between oxygen consumption and oxidation rate varied over time.
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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 | 0.000 |
| 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 | 0.001 |
| 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".