A Review of Design and Fabrication Methods for Nanoparticle Network Hydrogels for Biomedical, Environmental, and Industrial Applications
Bibliographic record
Abstract
Abstract Nanoparticle network hydrogels (NNHs) in which nanoparticles are used as a key building block to build the gel network have attracted significant interest given their potential to leverage the favorable properties of both hydrogels (e.g., hydrophilicity, tunable pore sizes, mechanics, etc.) and a variety of different nanoparticles (e.g., high surface area, chemical activity, independently tunable porosity, mechanics) to create new functional materials. Herein, recent progress in the design and use of NNHs is comprehensively reviewed, with an emphasis on defining the typical gel morphologies/architectures that can be achieved with NNHs, the typical crosslinking approaches used to fabricate NNHs, the fundamental properties and functional benefits of NNHs, and the reported applications of NNHs in electronics (flexible electronics, sensors), environmental (sorbents, separations), agriculture, self‐cleaning‐materials, and biomedical (drug delivery, tissue engineering) applications. In particular, the way in which the NNH structure is applied to improve the performance of the hydrogel in each application is emphasized, with the aim to develop a set of principles that can be used to rationally design NNHs for future uses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".