Purification, Functionalization, and Bioconjugation of Carbon Nanotubes
Bibliographic record
Abstract
Bioconjugation of carbon nanotubes (CNTs) with biomolecules promises exciting applications such as biosensing, nanobiocomposite formulation, design of drug vector systems, and probing protein interactions. Pristine CNTs, however, are virtually water-insoluble and difficult to evenly disperse in a liquid matrix. Therefore, it is necessary to attach molecules or functional groups to their sidewalls to enable bioconjugation. Both noncovalent and covalent procedures can be used to conjugate CNTs with a target biomolecule for a specific bioapplication. This chapter presents a few selected protocols that can be performed at any wet chemistry laboratory to purify and biofunctionalize CNTs. The preparation of CNTs modified with metallic nanoparticles, especially gold, is also described since biomolecules can bind and self-organize on the surfaces of such metal-decorated CNTs.
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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) | 0.001 | 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 | 0.001 |
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".