Synthesis and characterization of graphene-grafted gelatin nanocomposite hydrogels as emerging drug delivery systems
Bibliographic record
Abstract
Abstract The rate of drug release from the hydrogels can be influenced by various parameters such as swelling, polymer dissolution/erosion, drug dissolution/diffusion characteristics, drug concentration inside the hydrogel structure, the degree of crosslinking and geometry of hydrogel matrix. Hence, using mathematical modeling coupled with experimental procedures can be useful in recognizing the factors affecting the mechanism of drug release. Here, gelatin/graphene nanoplatelets (GNPs) composite was prepared and characterized by structural analyses. The swelling ratio of the nanocomposite hydrogel was determined at different concentrations of GNPs and crosslinker. The influence of the GNPs ratio on drug loading and encapsulation efficiencies of hydrogels were also investigated. After in vitro drug release from drug-loaded hydrogels into PBS solution, the release profile showed an initial burst release during the first 5 h, followed by a relatively slow release until 24 h. Finally, a model-fitting was used to deepen our understanding of the experimental data.
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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 | 0.000 |
| 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) | 0.001 | 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".