Smart hydrogels: Network design and emerging applications
Bibliographic record
Abstract
ABSTRACT Hydrogels are capable of adsorbing large quantities of water due to their hydrophilic three‐dimensional polymeric networks; thus, their physical properties are similar to soft tissues such as cartilage and muscle. Stimuli‐responsive smart hydrogels are particularly interesting because of their ability to respond to various exogenous and/or endogenous stimuli such as pH, thermal, light, magnetism, etc. Because of their versatile and unique properties, smart hydrogels show great potential in controlled drug release, tissue engineering scaffolds, solid/water stabilization, etc., wherein a rapid responsive property and outstanding mechanical properties are highly desired. The degree of responsiveness and mechanical properties of hydrogels are highly dependent on their polymeric network structures. Thus, the network structure design strategies are of great interest and particular importance for various applications. In this paper, we review various network design strategies to build smart hydrogel networks with rapid responsiveness and/or high mechanical properties, as well as the emerging applications of smart hydrogels such as controlled release systems, detection sensors, soft valves, and responsive actuators. The perspectives as well as current challenges facing the applications of such innovative stimuli‐responsive polymeric materials are also addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".