Thermotropic and Lyotropic Phase Behavior of Poly(γ-stearyl-<scp>l</scp>-glutamate)-Functionalized Nanoparticles
Bibliographic record
Abstract
Poly(γ-stearyl- l -glutamate) (PSLG) is a semiflexible synthetic polypeptide that forms both thermotropic and lyotropic liquid crystal (LC) phases. We previously showed that spherical nanoparticles (NPs) decorated with another semiflexible helical polymer, poly(hexyl isocyanate), form lyotropic nematic rather than cubic LC phases. In this work, PSLG ligands for functionalizing 4 nm ZrO 2 NPs were prepared via N-carboxyanhydride ring-opening polymerization. The PSLG-functionalized NPs (PSLG@ZrO 2 NP) were shown to be liquid crystalline NPs (LC-NPs) that form both lyotropic and thermotropic phases without being dispersed in an LC matrix. The structural changes of PSLG upon covalent attachment to NPs were studied by differential scanning calorimetry (DSC), optical microscopy, and small-angle X-ray scattering (SAXS). Surface anchoring of PSLG did not significantly change its lyotropic or thermotropic LC behavior. However, for both free and tethered PSLG, X-ray scattering combined with optical microscopy revealed the coexistence of cholesteric and columnar hexagonal lyotropic phases over a wide concentration range rather than the expected pure cholesteric phase. Although low-molecular-weight (MW) mesogenic ligands have been widely used, polymer ligands have not been previously exploited to produce thermotropic LC-NPs.
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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".