Application of the Multisolute Osmotic Virial Equation to Solutions Containing Electrolytes
Bibliographic record
Abstract
The prediction of multisolute solution behavior of solutions containing electrolytes is important in many areas of research, including cryopreservation. In this study, the use of a novel form of the osmotic virial equation for multisolute solutions containing an electrolyte is investigated and compared to a rigorous electrolyte solution theory, the Pitzer-Debye-Huckel equation. For aqueous solutions containing a small molecule (either dimethyl sulfoxide or glycerol) and sodium chloride, the multisolute osmotic virial equation, which utilizes only two parameters to capture the electrolyte solution behavior, is shown to be as accurate as the Pitzer-Debye-Huckel equation, which utilizes six empirical parameters and multiple functions to capture the electrolyte solution behavior. In addition, an approach based on the multisolute osmotic virial equation to investigate the effect of electrolyte concentration on macromolecule solution behavior is presented and applied to aqueous solutions of hydroxyethyl starch and sodium chloride. The multisolute osmotic virial equation is shown to be an accurate, straightforward predictive solution theory for important multisolute solutions containing electrolytes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".