Molecular simulation of curcumin loading on graphene and graphene oxide for drug delivery applications
Bibliographic record
Abstract
Curcumin loading capacity of polyethylene glycol (PEG) functionalized graphene and graphene oxides are investigated using molecular dynamics and Monte Carlo (MC) adsorption locator simulations. These simulation methods were performed as a function of oxidation extent to study the effect of functional groups on curcumin loading and release properties. Adsorption locator energy calculations suggest that the curcumin drug molecule prefers to adsorb at the less oxidized sites of graphene oxide. One of the phenolic rings of curcumin drug prefers to have a planar interaction with graphene and graphene oxide framework due to the pi-pi interaction. Molecular dynamic studies are conducted in aqueous medium under neutral pH. Mean square displacement and radial distribution functions are obtained to determine the nature of the curcumin attachment and release in aqueous medium. The molecular simulations show that separation distance of the curcumin molecule from GO sheet is 4.4 Å. The molecular simulations presented in this work will help to design new synthetic methods of nanocarriers for curcumin delivery applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".