Effects of Nano Carbon Conductive Additives on the Electrochemical Performance of LiCoO2 Cathode for Lithium Ion Batteries
Bibliographic record
Abstract
Carbon black(CB), multi-walled carbon nanotubes(CNTs) and graphene nanosheets(GNs) were employed as carbon conductive additives for LiCoO2(LCO). X-ray diffraction, transmission electron microscopy and scanning electron microscopy were used to characterize the crystal structure and morphology of samples. And the specific surface area and porosity structure of the three kinds of carbon conductive additives were measured by N2 adsorption-desorption. To investigate the effect on the electrochemical reaction activity, galvanostatic discharge-charge experiments showed that the composite of LCO-GNs exhibited the highest specific capacity of 167mAh/g at 0.1C and 123 mAh/g at 1 C rate. The flexible wrapping of GNs and bridging nearby LCO particles together were found to enhance electrical conductivity most effectively.
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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 | 0.000 | 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".