Graphene Nanoribbons Derived from the Unzipping of Carbon Nanotubes: Controlled Synthesis and Superior Lithium Storage Performance
Bibliographic record
Abstract
Graphene nanoribbons (GNRs) from chemical unzipping of carbon nanotubes (CNTs) have been reported to be a suitable candidate for lithium ion battery materials, but very few of them focused on controlling GNRs with different unzipping levels. Here we present a study of GNRs with controlled unzipping level and the prevailing factors that affect the lithium storage performance at early and final unzipping level; besides, the effect of thermal reduction has been investigated. On the basis of Raman and BET surface area tests, we found that the unzipping of CNTs starts with surface etching and then proceeds to partial and full unzipping and finally fragmentation and aggregation. Galvanostatic charge–discharge reveals that defect increase is mainly responsible for the capacity enhancement at the early unzipping level; surface area drop is associated with the capacity fade at the final unzipping level. Surface functional groups can result in low electrical conductivity and therefore cause capacity drop within several cycles. The GNRs with controlled unzipping level display different electrochemical behaviors and thus can provide rational choices for researchers who are searching for desired functions using GNRs as additives in lithium ion batteries.
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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) | 0.000 | 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".