Bibliographic record
Abstract
Abstract Applications of nuclear magnetic resonance (NMR) spectroscopy as an analytical tool in liquid crystals (LCs) are surveyed. Proton, deuteron, carbon‐13, and nitrogen‐14 are commonly used as probes in solid‐state NMR of condensed phases. Their usages in LCs are discussed in some details. Complex mathematical expressions needed to interpret NMR observables (both equilibrium and dynamic properties) are kept to a minimum, and the reader can refer to original articles and books for details. As it is impossible to provide an exhaustive coverage of the literature, selective examples are chosen to highlight areas of recent interests in the study of liquid‐crystal materials, particularly thermotropics, in terms of their physics and chemistry. Orientational/positional order parameters are readily obtained in ordered liquid‐crystal phases by means of NMR. They can serve as characteristic signatures of the studied mesophases. Liquid‐crystal ordering depends on intermolecular potentials among neighboring molecules. Solutes dissolved in LC solvents are good candidates to reveal different ordering mechanisms. Nonzero spin interactions are used to determine molecular structure of large and small molecules, and/or their conformation statistics. Nuclear spin relaxation times are readily measured by means of different NMR pulse techniques and can be powerful for revealing dynamic properties of mesogens with increasing structural complexity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.030 |
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".