Coke coating of natural graphite for Li-ion batteries
Bibliographic record
Abstract
Natural graphite can cause irreversible reactions during the initial charging periods of cell operation in lithium-ion batteries due to the formation of a solid electrolyte interface (SEI) on the graphite particles that prevents further electrolyte decomposition. In practical lithium secondary cells, this irreversible charge consumes lithium that must be brought into the cell in the form of a heavy lithium metal oxide such as LiCoO{sub 2}. However, irreversible capacity can be suppressed by preconditioning of the graphitic materials. In this study, a simple process to prepare coke-coated graphite was both proposed and tested using pyrolysis of Span80. Carbon-coated natural graphite (NG) particles were prepared by heat treatment at 700 degrees C. The Brunauer-Emmett-Teller (BET) surface areas of the materials were measured by the nitrogen adsorption method. The charge and discharge characteristics of the graphite materials were examined in a coin test cell, where a lithium metal was used as a counterpart electrode. Results of the study showed that the surface modification of NG by Span80 was effective in preparing an anode material for the lithium-ion batteries. It was observed that the Span80 carbon coating significantly reduced the specific BET surface area and the irreversible capacity due to SEI film formation and exfoliation of graphite during the first cycle. It was concluded that the coated NG exhibited better electrochemical performance than the untreated graphite. 19 refs., 1 tab., 6 figs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".