Multiple Alignment Modes for Nematic Liquid Crystals Doped with Alkylthiol-Capped Gold Nanoparticles
Bibliographic record
Abstract
The ability of alkylthiol capped gold nanoparticles (Au NPs) to tune, alter, and reverse the alignment of nematic liquid crystals (LCs) has been investigated in detail. Adjusting the concentration of the suspended Au NPs in the nematic LC host, optimizing the sample preparation protocol, or providing different sample substrates (untreated glass slides, rubbed polyimide-coated LC test cell, or ITO-coated glass slides) results in several LC alignment scenarios (modes) including vertical alignment, planar alignment, and a thermally controlled alignment switch between these two alignment modes. The latter thermal switch between planar and homeotropic alignment was observed particularly for lower concentrations (i.e., around 1 to 2 wt %) of suspended NPs in the size regime of 1.5-2 nm and was found to be concentration-dependent and thermally reversible. Different scenarios are discussed that could explain these induced alignment modes. In one scenario, the NP-induced alignment is related to the temperature-dependent change of the order parameter, S, of the nematic phase (ordering in the bulk). In the second scenario, a change of the ordering of the nematic molecules around the NPs that reside at the interfaces is described. We also started to test spin coating as an alternative way of preparing nematic thin films with well-separated Au NPs on the substrate and found this to be a possible method for manufacturing of future NP-doped LC devices, as this method produced evenly distributed NPs on glass substrates. Together the presented findings continue to pave the way for LC display-related applications of Au NP-doped nematic LCs and provide insights for N-LC sensor 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) | 0.001 | 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".