Hydrogen-bonded LC nanocomposites: characterisation of nanoparticle-LC interactions by solid-state NMR and FTIR spectroscopies
Bibliographic record
Abstract
Mahdi Roohnikanab, Kayla Cummings Premacka, Brenda Guzman-Juareza, Violeta Toadera, Alejandro Reyb & Linda Reven*aa Quebec Center for Advanced Materials (QCAM), Department of Chemistry, McGill University, Montreal, QC, Canadab Department of Chemical Engineering, McGill University, Montréal, QC, CanadaCONTACT Linda Reven linda.reven@mcgill.caABSTRACTAlthough the molecular-level nanoparticle-liquid crystal (NP-LC) interactions are key for forming stable NP dispersions in LC, characterisation of these interactions is scarce in the literature. Recently, we have developed hydrogen-bonded LC nanocomposites based on mesogenic carboxylic acids and NPs functionalised with hydrogen-bond acceptor/donor groups. Here, we apply solid-state 1H and 13C NMR and Fourier transform infrared spectroscopies to model systems consisting of mixtures of trans-4-n-butylcyclohexanecarboxylic acid (4-BCHA), 4-hexylbenzoic acid (6BA) and 4,4ʹ-bipyridine. The binary mixture, 4-BCHA:6BA, was also studied. The results were compared to monolayer quantities of 6BA and 4-BCHA adsorbed on pyridine functionalised zirconia NPs (ZrO2-n3-pyridine) to understand the different effects of the functionalised NPs on the LC properties.Graphical Abstract
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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".