Bibliographic record
Abstract
Magnetic ionogels, a category of hybrid materials consisting of magnetic nanoparticles and ionic liquids, have garnered significant interest owing to their remarkable attributes, including tunability, flexibility, and reactivity to external magnetic fields. These materials provide a distinctive amalgamation of the benefits of both magnetic nanoparticles and ionogels, resulting in improved efficacy across many applications. Magnetic ionogels may be readily controlled using magnetic fields, rendering them suitable for drug administration, biosensing, soft robotics, and actuators. The capacity to incorporate these materials into dynamic systems presents novel opportunities for the development of responsive, intelligent materials capable of real-time environmental adaptation. Nonetheless, despite the promising potential of magnetic ionogels, problems persist, including the optimization of the magnetic particle dispersion, the enhancement of the ionogel mechanical strength, and the improvement of the long-term stability. This review presents a comprehensive examination of the syntheses, characteristics, and uses of magnetic ionogels, emphasizing significant breakthroughs and persistent problems within the domain. We examine recent advancements and prospective research trajectories aimed at enhancing the design and efficacy of magnetic ionogels for practical applications across diverse fields, including biomedical uses, sensors, and next-generation actuators. This review seeks to elucidate the present status of magnetic ionogels and their prospective influence on materials science and engineering.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".