Injectable Poly(<i>N</i>-isopropylacrylamide)/Poly(vinylpyrrolidone) Interpenetrating Network Hydrogels Formed via Hydrazone/Disulfide Cross-Linking
Bibliographic record
Abstract
Injectable in situ gelling interpenetrating network hydrogels in which both interpenetrating phases can independently gel upon injection offer significant potential in applications if their internal structure can be rationally controlled. Herein, we report the fabrication and nanostructural characterization of poly( N -isopropylacrylamide) (PNIPAM)/poly(vinylpyrrolidone) (PVP) injectable interpenetrating polymer networks constructed based on a combination of hydrazone (PNIPAM) and disulfide (PVP) cross-linking. Compared to previously reported hydrazone/thiosuccinimide IPN formation, disulfide cross-linking is significantly slower than hydrazone cross-linking, enabling the generation of tunable internal phase morphologies in the injectable IPNs as a function of PVP molecular weight and degree of thiolation. Results from bulk rheology, polarity-sensitive fluorescence measurements, STORM super-resolution fluorescence microscopy, and contrast-matched small-angle neutron scattering measurements suggest that domain formation on the SANS-accessible length scale is driven by the rapid PNIPAM gelation that largely excludes PNIPAM from the PVP-rich domains but results in significant PVP entrapment within the PNIPAM-rich domain. Furthermore, PVP precursor polymers with lower molecular weights (i.e., reduced viscosities) and/or lower degrees of thiolation result in IPNs that undergo significant structural reconfiguration upon heating above the volume phase transition of the PNIPAM phase, a result correlated to the capacity of the thermodynamically favorable PNIPAM phase transition to reconfigure the weaker and dynamic PVP-disulfide phase to maximize the degree of PNIPAM phase collapse. Overall, regulating the relative gelation kinetics of the two interpenetrating in situ gelling phases results in tunable morphologies of relevance to the potential applications of such materials.
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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) | 0.000 | 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".