Smart Shear-Thinning Hydrogels as Injectable Drug Delivery Systems
Bibliographic record
Abstract
-Acrylic acid (PNIPAM) particles to the shear-thinning gel to render it pH-responsive. The effects of total solid weight and the percentage of laponite in a solid mass on the rheological behavior and mechanical properties were investigated to obtain the optimum formulation. The nanocomposite gel and particles were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), zeta potential, and dynamic light scattering techniques. Finally, release related experiment including degradability, swelling and Rhodamine B (Rd) release at various pH were performed. The results suggest that incorporation of silicate nanoplatelets in the gelatin led to the formation of the tunable porous composite, with a microstructure that was affected by introducing particles. Besides, the optimum formulation possessed shear-thinning properties with modified rheological and mechanical properties which preserved its mechanical properties while incubated in physiological conditions. The release related experiments showed that the shear-thinning materials offer pH-sensitive behavior so that the highest swelling ratio, degradation rate, and Rd release were obtained at pH 9.18. Therefore, this nanocomposite gel can be potentially used to develop pH-sensitive systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".