Chemical Coupling of Carbon Nanotubes and Silicon Nanoparticles for Improved Negative Electrode Performance in Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Multi‐walled carbon nanotube (MWCNT)/silicon nanocomposites obtained by a grafting technique using the diazonium chemistry are used to prepare silicon negative electrodes for lithium‐ion batteries. The covalent bonding of the two compounds is obtained via mono‐ and multi‐layers of phenyl bridges, leading to an ideal dispersion of MWCNTs and silicon nanoparticles that are bound together. The presence of MWCNTs close to silicon nanoparticles enhances the electronic pathway to the active material particles and probably helps to prevent silicon decrepitation upon repeated lithium insertion/extraction by improving the mechanical stability of the electrode at a nanoscale level. This effect results in the enhancement of cycling ability and capacity, which are demonstrated by comparing the nanocomposite electrode to a simple mixture of the two compounds. This technique can be applied to other carbon conductive additives together with silicon or other nanosized active compounds.
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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".