Surface-assisted assembly of a histidine-rich lipidated peptide for simultaneous exfoliation of graphite and functionalization of graphene nanosheets
Bibliographic record
Abstract
Biological molecules have promising potential to exfoliate graphite and produce biocompatible graphene nano-materials for biomedical applications. Here, a systematic design of a histidine-rich lipidated peptide sequence is presented that simultaneously exfoliates graphite flakes and functionalizes the resulting graphene nanosheets (∼150 nm lateral size) with long-term dispersion stability in aqueous solution (>8 months). The details of peptide/peptide and peptide/graphite interactions are probed using various microscopy, spectroscopy and molecular dynamics simulation methods. The results show that histidine and stearic acid interact with the graphite surface through π-π stacking and hydrophobic forces, respectively. Surface-assisted assembly of peptide molecules is then initiated via hydrogen bonds between deprotonated histidine segments, and a textured peptide nano-structure is formed. The work of adhesion between the peptide and graphite is found to be high enough to promote exfoliation of graphite flakes through layer-by-layer peeling of graphene nanosheets. The positively charged arginine in the peptide is exposed outward, and is responsible for the stable dispersion. The peptide molecules are sufficiently small, presenting the possibility to insert into and increase the spacing between the graphitic layers for enhanced exfoliation. The peptide-functionalized graphene nanosheets not only show great biocompatibility with cells in vitro, but also enhance cancer drug uptake by the cells.
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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 | 0.000 | 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) | 0.000 | 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".