Study on the effect of asphalt pretreatment on the structural and electrochemical properties of porous carbon based on finite element analysis
Bibliographic record
Abstract
Bitumen is a high-quality raw material for the preparation of carbon materials due to its high carbon and low ash characteristics, and its use in the preparation of supercapacitor electrode materials plays a significant role in the enhancement of the economic benefits of the entire coal chemical process.In this paper, the raw materials and experimental equipment required for this study were selected to prepare porous carbon samples under the guidance of the raw material pretreatment process.After completing the preparation of porous carbon samples, the finite element analysis software ANSYS was used to investigate the effect of bitumen pretreatment on the structure and electrochemical properties of porous carbon.With the rising air oxidation time, the peak ratio of porous carbon showed a trend of decreasing and then increasing, with specific values of 2.627, 1.958, 2.083, and 2.486, which was the same trend as that of the XRD test results, suggesting that the asphalt pretreatment has a moderating effect on the structure of porous carbon.The study in this paper further recognizes the effect of asphalt pretreatment on the structure and electrochemical properties of porous carbon, which provides a reference for research and development and innovation in materials chemistry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".