Chemically and thermally stable, emissive carbon dots as viable alternatives to semiconductor quantum dots for emissive nematic liquid crystal–nanoparticle mixtures with lower threshold voltage
Bibliographic record
Abstract
Dispersions of chemically and thermally robust carbon dots (2.5 ± 0.5 nm in core diameter) were prepared and investigated by polarised optical microscopy, electro-optic measurements including dynamic tests and numerical simulations as well as fluorescence confocal microscopy. The carbon dots were prepared by a straightforward thermal decomposition method from citric acid and hexadecylamine, and they show typical excitation wavelength-dependent photoluminescence behaviour. All dispersions, ranging from 0.5 to 5.0 wt.%, showed lower values for isotropic–nematic phase transition temperature and broader isotropic–nematic biphasic temperature intervals with increasing carbon dot content in comparison to the neat material. Doping of the nematic host with the carbon dots resulted in lower values for the apparent threshold voltage and the elastic constants, but higher values for the rotational viscosity. At 2.5 wt.% and higher, carbon dots residing at the confining interfaces in planar cells induce an increasing initial pre-tilt of up to 8° at lower temperatures. Fluorescence confocal microscopy confirmed this, where the luminescence of the carbon dots permitted visualisation of the distribution of the carbon dots in the bulk with a noticeable, in some cases even pattern-like, segregation to the confining interfaces.
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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".