Carbon Nanotubes with Small and Tunable Diameters from Poly(ferrocenylsilane)-<i>b</i><i>lock</i>-polysiloxane Diblock Copolymers
Bibliographic record
Abstract
Iron-containing nanostructures produced from various self-assembled poly(ferrocenylsilane)-block-polysiloxane thin films are catalytically active for the initiation and growth of high density, small diameter carbon nanotubes (CNTs). Moreover, the tube diameter and density can be tuned by adjusting the chain lengths of the block copolymer. Iron-containing nanostructures from poly(ferrocenylmethylethylsilane)-b-poly(methylvinylsiloxane) polymer with 25 repeat units of an iron-containing segment and 265 repeat units of a non-iron-containing segment are able to produce CNTs with diameters around or less than 1 nm. Lithographically selective growth of CNTs across a large surface area has been demonstrated using this polymer system. Under the same growth condition, it has been found that the yield of defect-free CNTs varies with the size of the catalytically active nanostructures, which are dictated by the chain lengths of the two blocks. This result indicates that, for a specific-sized catalyst nanocluster, a unique set of growth conditions is required for synthesizing high yield, defect-free CNTs. This finding further addresses the importance of using uniform-sized catalyst-containing nanostructures for consistently achieving high-yield and high-quality CNTs with a minimum number of defects and amount of amorphous carbon.
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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".