Hydrogen-Bonded Liquid Crystal Nanocomposites
Bibliographic record
Abstract
Nanoparticle-liquid crystal (NP-LC) composites based on hydrogen bonding were explored using a model system. The ligand shells of 3 nm diameter zirconium dioxide nanoparticles (ZrO2 NPs) were varied to control their interaction with 4-n-hexylbenzoic acid (6BA). The miscibility and effect of the NPs on the nematic order as a function of particle concentration was characterized by polarized optical microscopy (POM), fluorescence microscopy and (2)H NMR spectroscopy. Nonfunctionalized ZrO2 NPs have the lowest miscibility and strongest effect on the LC matrix due to irreversible binding of 6BA to the NPs via a strong zirconium carboxylate bond. The ZrO2 NPs were functionalized with 6-phosphonohexanoic acid (6PHA) or 4-(6-phosphonohexyloxy)benzoic acid (6BPHA) which selectively bind to the ZrO2 NP surface via the phosphonic acid groups. The miscibility was increased by controlling the concentration of the pendant CO2H groups by adding hexylphosphonic acid (HPA) to act as a spacer group. Fluorescence microscopy of lanthanide doped ZrO2 NPs showed no aggregates in the nematic phase below the NP concentration where aggregates are observed in the isotropic phase. The functionalized NPs preferably concentrate into LC defects and any remaining isotropic liquid but are still present throughout the nematic liquid at a lower concentration.
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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".