Testing Assumptions about Solute Concentration Dependence in Liquid Crystal NMR
Bibliographic record
Abstract
The NMR spectra of four solutes, used as probes of liquid crystal orientational order, were analyzed. For each solute, samples were prepared at different solute concentrations, and the concentration dependence was used to extrapolate zero-concentration properties. The mean-field (Maier-Saupe) model when applied to solutes neglects solute-solute interactions and assumes all solutes in a mixed-solute sample see the same average environment. The first assumption is only valid as one approaches zero concentration, while experiments are typically carried out at concentrations between 0 and 10 mol %. The solute concentration dependence has in the past been "scaled out" using an internal solute reference as an orientational standard. We measured the concentration dependence of the orientational order parameter and calculate the corresponding interaction energies based on a mean-field interaction potential for a solute. We find agreement at the 3% level between experiments for different solutes while using (i) the zero-concentration values as solute-dependent orientational references and (ii) scaling to either order parameters or interaction energies; these two scalings gave equivalent but not identical results. We find, too, that errors inherent in the experiment and the calculations will limit attempts to refine the theory to push the comparisons beyond the 2% level.
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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 | 0.033 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".