Why some carbons may or may not graphitize? The point of view of thermodynamics
Bibliographic record
Abstract
Not all carbons graphitize in equal measure. Some will develop a structure which approaches the one of perfect graphite (graphitizable carbons) upon heat treatment, while others will not (non-graphitizable carbons). The present work develops a phenomenological model for the conceptual understanding of graphitizability (capacity to graphitize). To support this model, a mathematical formalism, inspired from thermodynamics, is proposed to calculate the Ultimate Graphitizability (ηg) of some graphitizable and non-graphitizable carbon materials. ηg is the average interlayer spacing (d002) of a graphenic carbon following graphitization at ∼3400K. ηg can be estimated assuming a topological graphitization mechanism operating between 1700K and 3400K. Two independent variables define ηg: d002(Tα) and d002(Tβ). Tα and Tβ are arbitrarily selected temperatures between 1700K and 2550K (the graphitization threshold). In order to better understand the parameters affecting d002(Tα) and d002(Tβ), new carbonization/graphitization experimental results are presented. These suggest that d002(Tα) and d002(Tβ) are correlated to the oxygen/hydrogen composition ratio and the relative mesoscale crystallite orientation of some graphitizable carbons following the end of primary carbonization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".