Bibliographic record
Abstract
This paper presents a surface science perspective on sidewall functionalisation of carbon nanotubes. Carbon nanotubes are varyingly described by physicists as extended solids or by organic chemists as molecules. As prototypical nanostructures, they are indeed a little bit of both. Their chemistry and resulting potential applications can only truly be appreciated by combining both perspectives. The emphasis of this review is on the interdependence of topography and electronic structure on one side and chemistry and reactivity of carbon nanotube sidewalls on the other side. A brief introduction to the structure of single-walled carbon nanotubes and related materials (graphite, multi-wall carbon nanotubes and fullerenes) is followed by a review of possible deviations from perfect order in nanotube sidewalls, such as defects, functional groups or non-covalent interactions with nearby entities. We then proceed to review the implications of these deviations on the sidewall local electronic structure and chemical reactivity. Site-dependent functionalisation and induced reactivity on carbon nanotube sidewalls have been proposed theoretically and we have found some initial experimental evidence that this is indeed the case. However, this is a young field and many theoretical and experimental challenges still lie ahead. Understanding site-selectivity of carbon nanotube sidewall functionalisation will allow us to tailor their properties and utilise them as components of more complex nanostructures in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".