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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".