Detecting, Visualizing, and Measuring Gold Nanoparticle Chirality Using Helical Pitch Measurements in Nematic Liquid Crystal Phases
Bibliographic record
Abstract
Chirality at the nanoscale, or more precisely, the chirality or chiroptical effects of chiral ligand-capped metal nanoparticles (NPs) is an intriguing and rapidly evolving field in nanomaterial research with promising applications in catalysis, metamaterials, and chiral sensing. The aim of this work was to seek out a system that not only allows the detection and understanding of NP chirality but also permits visualization of the extent of chirality transfer to a surrounding medium. The nematic liquid crystal phase is an ideal candidate, displaying characteristic defect texture changes upon doping with chiral additives. To test this, we synthesized chiral cholesterol-capped gold NPs and prepared well-dispersed mixtures in two nematic liquid crystal hosts. Induced circular dichroism spectropolarimetry and polarized light optical microscopy revealed that all three gold NPs induce chiral nematic phases, and that those synthesized in the presence of a chiral bias (disulfide) are more powerful chiral inducers than those where the NP was formed in the absence of a chiral bias (prepared by conjugation of a chiral silane to preformed NPs). Helical pitch data here visually show a clear dependence on the NP size and the number of chiral ligands bound to the NP surface, thereby supporting earlier experimental and theoretical data that smaller metal NPs made in the presence of a chiral bias are stronger chiral inducers.
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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 | 0.001 |
| 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".