Nanogels and Microgels: From Model Colloids to Applications, Recent Developments, and Future Trends
Bibliographic record
Abstract
Nanogels and microgels are soft, deformable, and penetrable objects with an internal gel-like structure that is swollen by the dispersing solvent. Their softness and the potential to respond to external stimuli like temperature, pressure, pH, ionic strength, and different analytes make them interesting as soft model systems in fundamental research as well as for a broad range of applications, in particular in the field of biological applications. Recent tremendous developments in their synthesis open access to systems with complex architectures and compositions allowing for tailoring microgels with specific properties. At the same time state-of-the-art theoretical and simulation approaches offer deeper understanding of the behavior and structure of nano- and microgels under external influences and confinement at interfaces or at high volume fractions. Developments in the experimental analysis of nano- and microgels have become particularly important for structural investigations covering a broad range of length scales relevant to the internal structure, the overall size and shape, and interparticle interactions in concentrated samples. Here we provide an overview of the state-of-the-art, recent developments as well as emerging trends in the field of nano- and microgels. The following aspects build the focus of our discussion: tailoring (multi)functionality through synthesis; the role in biological and biomedical applications; the structure and properties as a model system, e.g., for densely packed arrangements in bulk and at interfaces; as well as the theory and computer simulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".