Atomistic Study of Several Peptides Covering Single‐walled Carbon Nanotube by Noncovalent Adsorption
Bibliographic record
Abstract
Single‐walled Carbon Nanotubes (SWNTs) have shown outstanding physicochemical, mechanical, electronic, and optical properties that enable a broad range of biomedical applications, including drug delivery (carrier for anti‐cancer drugs), imaging (carrier for contrast agents), tissue engineering (bone, nerve, and cardiac), gene delivery, and biosensors. However, their carbon hexagonal‐lattice nanostructure causes a sizeable surface‐to‐volume ratio with a potent van der Waals surface that leads to limiting characteristics for bioapplications (e.g., low solubility, agglomeration, and potential toxicity). In this perspective, the biomolecule‐adsorption strategy that exhibits advantageous characteristics over covalent functionalization could not only help overcome these SWNT limitations but also preserve its pristine sp2 hybridization wall by noncovalently binding amphiphilic peptides. The objective of this computational study is to investigate the fundamental interactions and properties of ten different segments (lower than 40 amino acids) on (16,0) SWNT system using Molecular Dynamics (MD) simulations that are essential and effective factors on SWNT applications, particularly for drug delivery. This proposed coverage of natural‐sequence peptides can potentially be easier recognized by biological processes as well as increase dispersibility and decrease toxicity of SWNT.
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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) | 0.000 | 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".