Significance of Cations in Ionic Liquids Chemistry
Bibliographic record
Abstract
There is a tendancy to classify ionic liquids either as individual solvents (LiCl—KCl vs. NaCl—KCl, NaNO 3 —KNO 3 vs. LiNO 3 —KNO 3 ) or in terms of the anions. In fact, a compromise is appropriate. We shall point out how the cation choice influences the chemistry, including the electrochemistry, of an anion in an ionic liquid. Some examples follow. The effect of the cation on the structure of liquid alkali halides is exemplified by the coordination chemistry of dissolved Ni(II) as well as by diffraction studies. Raman spectroscopy demonstrates the cation influence on the fundamental equilibrium 2AlCl 4 - → Cl - + Al 2 Cl 7 - in alkali chloroaluminates. When the electrochemical reduction of NO 3 - in alkali nitrates is examined, we observe product dependence that can be traced back to metal + oxygen reactions. Regarding the Lewis acidity of room temperature ionic liquids, it has been shown that the cracking of alkanes and the dissolution of heavy oil are influenced by the cation present with a given haloaluminate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".