Efficient skin interactions of graphene derivatives: challenge, opportunity or both?
Bibliographic record
Abstract
Interactions between graphene, with its wide deployment in consumer products, and skin, the body's largest organ and first barrier, are highly relevant with respect to toxicology and dermal delivery. In this work, interaction of polyglycerol-functionalized graphene sheets, with 200 nm average lateral size and different surface charges, and human skin was studied and their potential as topical delivery systems were investigated. While neutral graphene sheets showed no significant skin interaction, their positively and negatively charged counterparts interacted with the skin, remaining in the stratum corneum. This efficient skin interaction bears a warning but also suggests a new topical drug delivery strategy based on the sheets' high loading capacity and photothermal property. Therefore, the immunosuppressive drug tacrolimus was loaded onto positively and negatively charged graphene sheets, and its release measured with and without laser irradiation using liquid chromatography tandem-mass spectrometry. Laser irradiation accelerated the release of tacrolimus, due to the photothermal property of graphene sheets. In addition, graphene sheets with positive and negative surface charges were loaded with Nile red, and their ability to deliver this cargo through the skin was investigated. Graphene sheets with positive surface charge were more efficient than the negatively charged ones in enhancing Nile red penetration into the skin.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".