Polymer Functionalized Nanoparticles in Blue Phase LC: Effect of Particle Shape
Bibliographic record
Abstract
Ethylene oxide oligomers and polymers, free and tethered to gold nanoparticles, were dispersed in blue phase liquid crystals (BPLC). Gold nanospheres (AuNPs) and nanorods (AuNRs) were functionalized with thiolated ethylene oxide ligands with molecular weights ranging from 200 to 5000 g/mol. The BPLC mixture (ΔTBP ~6 °C) was based on the mesogenic acid heterodimers, n-hexylbenzoic acid (6BA) and n-trans-butylcyclohexylcarboxylic acid (4-BCHA) with the chiral dopant (R)-2-octyl 4-[4-(hexyloxy)benzoyloxy]benzoate. The lowest molecular weight oligomer lowered and widened the BP range but adding AuNPs functionalized with the same ligand had little effect. Higher concentrations or molecular weights of the ligands, free or tethered to the AuNPs, completely destabilized the BP. Mini-AuNRs functionalized with the same ligands lowered and widened the BP temperature range with longer mini-AuNRs having a larger effect. In contrast to the AuNPs, the mini-AuNRs with the higher molecular weight ligands widened rather than destabilized the BP, though the lowest MW ligand yielded the largest BP range, (ΔTBP > 13 °C). The different effects on the BP may be due to the AuNPs accumulating at singular defect sites whereas the mini-AuNRs, with diameters smaller than that of the disclination lines, can more efficiently fill in the BP defects.
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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".