Enhancing Specific Capacity of Hard Carbon through Optimized Liquid-Phase CrossLinking and Solid-Phase Oxidation Strategy
Bibliographic record
Abstract
Abstract Pre-oxidation is recognized as an effective strategy for preparing pitch-based carbon materials. However, medium and low-temperature coal tar (MLCT) with a low softening point tends to form a protective film on its surface during oxidation, inhibiting oxidative cross-linking and hindering the development of effective cross-linked structures. To address this limitation, the molecular structure of MLCT is modified through liquid phase cross-linking. Small molecular aromatic hydrocarbons create a methylene bridge structure or an oxygen-containing functional group cross-linking structure under the influence of oxygen free radicals, resulting in the formation of cross-linked molecular structures. This process consequently increases the softening point of coal tar. Subsequently, the cross-linked pitch undergoes solid-phase oxidation, introducing oxygen-containing groups that facilitate the formation of a disordered carbon skeleton. This process transforms the material from thermoplastic to thermosetting, thereby suppressing the ordered arrangement of microcrystals during carbonization. Through the integration of liquid-phase cross-linking and solid-phase oxidation, hard carbon with ultra-microporous and closed-pore structures has been synthesized. The optimized hard carbon exhibits a specific capacity of 306.9 mAh/g after 200 cycles at a current density of 100 mA/g, with the plateau region contributing 204 mAh/g. This study provides a novel pathway for the preparation of high-performance hard carbon from low-softening-point pitch.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".