Test of Reaction Kinetics Using Both Differential Scanning and Accelerating Rate Calorimetries As Applied to the Reaction of Li<i><sub>x</sub></i>CoO<sub>2</sub> in Non-aqueous Electrolyte
Bibliographic record
Abstract
Extracting reliable reaction kinetics from nonisothermal calorimetric results can be difficult. The reaction model, activation energy, and frequency factor make up a “kinetic triplet” for a particular reaction and define the reaction kinetics. One expects a good correlation between data and the predictions of the reaction model for a variety of experiments, provided the reaction triplet has been well determined. Such a correlation is expected for the results of accelerating rate calorimeter (ARC) and differential scanning calorimeter (DSC) experiments. As an example, the reaction of Li x CoO 2 in nonaqueous electrolyte (as is important in Li-ion battery safety) has been studied with both DSC and ARC. Comparing the shape of ARC profiles to those predicted theoretically limits the choice of reaction model. The activation energy is determined from the shift of the DSC profile with heating rate or from the change in the initial self-heating rate of ARC samples as a function of temperature. The frequency factor is then chosen to give the correct DSC peak temperature and correct self-heating rate. Calculated DSC and ARC curves fit experiment well for several related reaction models.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".