Bibliographic record
Abstract
In the course of studying ionic liquids, it is customary to obtain density data, either for simple practical needs or for use with other physical measurements such as the determination of solute concentrations. Interest in molar volumes and their inverse, molar concentrations, has been minimal. We have assembled density data for a wide range of ionic liquids, including molten single and mixed alkali metal salts and ambient temperature chloroaluminate liquids. It is simple to calculate the molarities of the ions present in most cases. We observe a range from ~35 M for Li + and Cl − in liquid LiCl to ~1.5 M for the ions of a particular phosphonium salt with values of 3 – 6 M for a selection of chloroaluminate liquids. Within chloroaluminate systems, the anion concentrations are determined (a) by the AlCl 3 mole fraction but also (b) by the density which varies with the cation. Thus if the anion is a reactant, its reactivity should depend on the complete constitution of the liquid. Liquid water has a molarity of ~55 but aqueous solutions can be prepared containing concentrations of ions within the 1.5 to 35 M span. In the ionic liquids, have we simply substituted space for water compared to the aqueous solutions? Molecular orbital calculations were performed for constituent ions of liquids to estimate close-packed volumes. For simple salts these values can be compared to the lattice parameters from X-ray studies of solids. In all cases, ionic liquids appear to contain substantial (~40%) free volume. This approach offers the possibility of designing an ionic liquid of appropriate density and species concentrations, although non-ionic interactions such as H-bonding need to be accounted for in certain systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".