Measurement and modeling of peroxides half‐life: A thermo‐kinetic approach
Bibliographic record
Abstract
Abstract Half‐life values of organic peroxides at elevated temperature conditions are important in characterizing the reactivity and are often available in literature or through vendors. However, there is often lack of details/accuracy on methods used to obtain these values, as well as differences in methods across vendors and publications, thus resulting in discrepant reactivity profile. To address this, a method involving calorimetric experiment and thermo‐kinetic modeling was developed. The current approach was applied on five peroxides samples to obtain kinetic parameters and estimate their half‐life in the temperature range of interest. The measurements were performed by DSC under non‐isothermal conditions on the dilute peroxide solutions (∼0.12 M in mineral oil) and the data were kinetically treated according to three model‐based and one model‐free kinetic equations. A very good agreement was found between the half‐life calculated by all kinetic methods, but significant differences were noticed with the kinetic parameters reported in literature. Additionally, the obtained half‐life results, based on non‐isothermal measurements developed kinetic models, were validated through isothermal calorimetric testing. Given the accuracy and robustness of our results, the current method can be applied to estimate half‐life of organic peroxides at elevated temperature conditions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".