Dispersion of Multiwalled Carbon Nanotubes in Water Using Ionic-Complementary Peptides
Bibliographic record
Abstract
We demonstrate the noncovalent modification of multiwalled carbon nanotubes (MWNTs) immersed in aqueous solution using the ionic-complementary peptide EFK16-II. This modification presumably arises through the interaction between the hydrophobic side of the EFK16-II and MWNT sidewalls and orients hydrophilic functional groups toward the solution phase and enables them to form highly stable dispersions in water. This stability can be attributed to the electrostatic repulsion between self-assembled peptides on the MWNTs. This repulsion as determined by ζ potential measurements increases as the pH diverges from the isoelectric point of ~6.7 for EFK16-II. This trend is confirmed by dynamic light scattering measurements of the suspensions showing a decrease in their particle size as the ζ potential increases. These EFK16-II-MWNT suspensions have been used to modify mica surfaces. Atomic force microscopy and scanning electron microscopy images show that this leads to a uniform distribution of individual modified MWNTs on the mica surfaces. Transmission electron microscopy reveals images of well-dispersed fibers with dimensions similar to those of individual MWNTs. Tissue culture plates previously contacted with EFK16-II-modified MWNTs have been shown to have enough biocompatibility for growth and attachment of cells. The biocompatibility and enhanced electrical conductivity that should result from the modification with these EFK16-II-MWNT suspensions opens up their use in a number of potential biomedical applications such as the design of bioelectrode interfaces and fabrication of biosensors with high sensitivity.
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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 | 0.001 | 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 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".