Effects of Sodium and Magnesium Cations on the Aggregation of Chromonic Solutions Using Molecular Dynamics
Bibliographic record
Abstract
Lyotropic chromonic liquid crystals (LCLCs) constitute a unique variety of water-soluble mesogens that spontaneously assemble into elongated aggregates, thereby resulting in the formation of liquid crystal phases depending on the temperature and concentration. The influence of ionic additives on the aggregation of LCLC has been extensively studied, but the molecular mechanisms governing these effects remain unclear. In this investigation, we perform atomistic molecular dynamics simulations of dilute sunset yellow (SSY) LCLC solutions doped with NaCl and MgCl 2 salts. Structural and dynamical properties of SSY hydration shells are considerably modified by the partial substitution of their H bonds with sodium/magnesium-sulfonate ion pairs. Although the intermolecular distance of ∼3.4 Å between SSY mesogens is preserved regardless of the ionic content, the growing number of ion pairs favors the reduction of the electrostatic repulsion between mesogens, increasing the length of SSY stacks. Moreover, magnesium cations exert the strongest electrostatic effects due to their higher hydration capabilities and acute electrostatic binding to SSY. For these reasons, experimental observations of dilute SSY solutions doped with Mg 2+ exhibit higher nematic-to-isotropic transition temperatures than Na + . This work provides a fundamental understanding of the influence of ionic additives on the self-assembly of diluted LCLC solutions derived from the synergistic molecular mechanisms between mesogens, the solvent, and cations.
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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".