Engineering multifunctional adhesive hydrogel patches for biomedical applications
Bibliographic record
Abstract
Abstract Traditional patches, such as sticking plaster or acrylic adhesives used for over a hundred years, lack functionality. To address this issue of poor functionality, adhesive hydrogel patches have emerged as an efficient bioactive multifunctional alternative. Hydrogels are three‐dimensional, water‐swellable, and polymeric materials closely resembling the native tissue architecture. The physicochemical properties of hydrogels can be modified easily, allowing them to be suitable for various biomedical applications. Moreover, adhesive properties can be imparted to hydrogels through physicochemical manipulations, making them ideal candidates for supplementing or replacing traditional sticking plaster. As a result, sticky hydrogel patches are widely used for transdermal drug delivery and have even found commercial purposes. Beyond transdermal delivery, such hydrogel patches have also found applications in cardiac therapy, cancer research, and biosensing, among other applications. In this mini‐review, we critically discuss the challenges of fabricating multifunctional adhesive hydrogel patches. Furthermore, we introduce some of the chemical strategies involved with fabricating the patches. We also review their emerging biomedical applications. Finally, we explore their potential future in the flourishing field of tissue engineering and drug delivery.
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 | 0.000 |
| 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) | 0.002 | 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".