Favorable adsorption of capped amino acids on graphene substrate driven by desolvation effect
Bibliographic record
Abstract
The use of graphene-based nanomaterials is being explored in the context of various biomedical applications. Here, we performed a molecular dynamics simulation of individual amino acids on graphene utilizing an empirical force field potential (Amber03). The accuracy of our force field method was verified by modeling the adsorption of amino acids on graphene in vacuum. These results are in excellent agreement with those calculated using ab initio methods. Our study shows that graphene exhibits bioactive properties in spite of the fact that the interaction between graphene and amino acids in a water environment is significantly weaker as compared to that in vacuum. Furthermore, the adsorption characteristics of capped and uncapped amino acids are significantly different from each other due to the desolvation effect. Finally, we conclude that when assessing protein-surface interactions based on adsorption of single amino acids, the minimum requirement is to use capped amino acids as they mimic residues as part of a peptide chain.
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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".