The effects of surface topography modification on hydrogel properties
Bibliographic record
Abstract
Hydrogel has been an attractive biomaterial for tissue engineering, drug delivery, wound healing, and contact lens materials, due to its outstanding properties, including high water content, transparency, biocompatibility, tissue mechanical matching, and low toxicity. As hydrogel commonly possesses high surface hydrophilicity, chemical modifications have been applied to achieve the optimal surface properties to improve the performance of hydrogels for specific applications. Ideally, the effects of surface modifications would be stable, and the modification would not affect the inherent hydrogel properties. In recent years, a new type of surface modification has been discovered to be able to alter hydrogel properties by physically patterning the hydrogel surfaces with topographies. Such physical patterning methods can also affect hydrogel surface chemical properties, such as protein adsorption, microbial adhesion, and cell response. This review will first summarize the works on developing hydrogel surface patterning methods. The influence of surface topography on interfacial energy and the subsequent effects on protein adsorption, microbial, and cell interactions with patterned hydrogel, with specific examples in biomedical applications, will be discussed. Finally, current problems and future challenges on topographical modification of hydrogels will also be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".