Hydrogels with multiple characteristic pore dimensions: From transport properties to multifunctional materials
Bibliographic record
Abstract
Hydrogels with multiple characteristic pore dimensions (HMPs) have emerged as a powerful class of soft materials inspired by biological systems. By incorporating distinct average pore sizes into a single network, simultaneous control over competing hydrogel transport properties can be achieved, including throughput and selectivity, both of which are important in drug delivery, tissue engineering, catalysis, sensing, and water remediation hydrogel applications. This review highlights recent advances in the design, synthesis, characterization, and applications of HMPs. It highlights the fundamental principles of transport in these hydrogels, including the role of spatial arrangement of regions with different pore dimensions in probe mobility and fluid flow. Experimental and theoretical characterization of distinct pore dimensions in HMPs is followed by the discussion of the contribution of multiple pore dimensions to HMP functionality. The review provides the summary of the strategies for fabricating HMPs and their applications. An outlook highlights key challenges and future opportunities in this field to advance HMPs as the new generation of hydrogel-based materials for diverse applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".