Contact‐Free Remote Manipulation of Hydrogel Properties Using Light‐Triggerable Nanoparticles: A Materials Science Perspective for Biomedical Applications
Bibliographic record
Abstract
Considerable progress has been made in synthesizing "intelligent", biodegradable hydrogels that undergo rapid changes in physicochemical properties once exposed to external stimuli. These advantageous properties of stimulus-triggered materials make them highly appealing to diverse biomedical applications. Of late, research on the incorporation of light-triggered nanoparticles (NPs) into polymeric hydrogel networks has gained momentum due to their ability to remotely tune hydrogel properties using facile, contact-free approaches, such as adjustment of wavelength and intensity of light source. These multi-functional NPs, in combination with tissue-mimicking hydrogels, are increasingly being used for on-demand drug release, preparing diagnostic kits, and fabricating smart scaffolds. Here, the authors discuss the atomic behavior of different NPs in the presence of light, and critically review the mechanisms by which NPs convert light stimuli into heat energy. Then, they explain how these NPs impact the mechanical properties and rheological behavior of NPs-impregnated hydrogels. Understanding the rheological behavior of nanocomposite hydrogels using different sophisticated strategies, including computer-assisted machine learning, is critical for designing the next generation of drug delivery systems. Next, they highlight the salient strategies that have been used to apply light-induced nanocomposites for diverse biomedical applications and provide an outlook for the further improvement of these NPs-driven light-responsive hydrogels.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".