Nanostructure of Fully Injectable Hydrazone–Thiosuccinimide Interpenetrating Polymer Network Hydrogels Assessed by Small-Angle Neutron Scattering and dSTORM Single-Molecule Fluorescence Microscopy
Bibliographic record
Abstract
Herein, we comprehensively investigate the internal morphology of fully injectable interpenetrating networks (IPNs) prepared via coextrusion of functionalized precursor polymer solutions based on thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) and nonthermoresponsive poly(vinyl pyrrolidone) (PVP) by reactive mixing using kinetically orthogonal hydrazone and thiosuccinimide cross-linking mechanisms. Small-angle neutron scattering, probing both the full IPN as well as the individual constituent networks of the IPN using index-matching, suggests a partially mixed internal structure characterized by PNIPAM-rich domains entrapped in a clustered PVP-rich phase. This interpretation is supported by super-resolution fluorescence microscopy (direct stochastic optical reconstruction microscopy) measurements on the same gels on a different length scale, which show both the overall phase segregation typical of an IPN as well as moderate mixing of PNIPAM into the PVP-rich phase. Such a morphology is consistent with the kinetics of both gelation and phase separation in this in situ gelling system, in which gelation effectively traps a fraction of the PNIPAM in the PVP phase prior to full phase separation; by contrast, such interphase mixing is not observed in semi-IPN control hydrogels. This knowledge has significant potential for the design of an injectable hydrogel with internal morphologies optimized for particular biomedical applications.
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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".