Effects of gold nanoparticle film morphology on the alignment of a nematic liquid crystal
Bibliographic record
Abstract
We report the alignment of liquid crystal (LC) 4-cyano-4ʹ-pentylbiphenyl (5CB) to well-defined films of alkanethiol-capped gold nanoparticles, residing at the LC/water interface and in optical sandwich cell configurations. Gold nanoparticles (AuNPs) of two core sizes (2.6 and 4.1 nm) were synthesised with a variety of alkyl chain lengths (CnH2n+1SH, for n = 5–18). Langmuir films of the nanoparticles were compressed to 10 mN/m and introduced to the LC/water interface via Langmuir–Schaefer transfer onto an ~20 μm thick film of LC. The 4.1 nm AuNP films consistently yield homeotropic alignment of the LC while the 2.6 nm AuNP films yield mixed alignments. These observations reflect differences in the AuNP film morphology, surface coverage and relatively weak anchoring strength of 5CB to the nanoparticle films. We determine the anchoring of 5CB to these alkanethiol-capped AuNP films at high coverage. This method can be applied to other types of nanoparticles and ligand shells for determining the LC anchoring to condensed films of water-insoluble nanoparticles with the goal of controlling nanoparticle–LC interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".