Cholesteric Behavior of Poly(γ-benzyl-<scp>l</scp>-glutamate)-Functionalized Nanoparticles
Bibliographic record
Abstract
Following earlier work showing that nanoparticles (NPs) with semiflexible polymer ligands can form lyotropic nematic liquid crystals (LCs), this strategy was expanded to explore whether other LC phases are accessible by using synthetic polypeptides. The key finding of this work was that ZrO 2 NPs grafted with poly(hexyl-isocyanate) ligands can exhibit liquid crystalline properties independently, without needing to be dispersed in a LC matrix. Following this result, poly(γ-benzyl- l -glutamate (PBLG) was chosen due to its well-understood cholesteric LC properties. Phosphonic acid-functionalized PBLG ligands of different molecular weights above the threshold to form lyotropic phases were synthesized and grafted to 4 nm metal oxide NPs and assessed for LC behavior. Cholesteric lyotropic phases, gels, and fibrils were observed and characterized by optical microscopy and small-angle X-ray scattering. Pinning of PBLG chains to NPs had the largest effect for low molecular weight PBLG with a significant decrease in the critical concentration required to form a lyotropic phase. This trend diminished as the molecular weight of the PBLG ligands increased. Structure wise, the NP contribution enlarges the cholesteric structure with an increased cholesteric pitch owing to the steric requirements of the tethered PBLG ligands. In general, the PBLG-NPs behave like the free polymer, indicating that NPs with synthetic polypeptide ligands, given their relatively simple synthesis, are promising building blocks to construct biocompatible, stimulus-responsive nanocomposites as well as for NP-based 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".