Limited ionicity in poor protic ionic liquids: Association Gibbs energies
Bibliographic record
Abstract
Protic ionic liquids (PILs), made from anhydrous mixtures of Bronsted acids HA and bases B (HA + B → BH+ + A−), occasionally suffer from limited ionicity. In cases of “poor” PILs (<10% ionicity, e.g., using carboxylic acids), past simulations have hinted that ion-pair association, more than incomplete proton transfer, is at fault. To improve upon the Fuoss equation for predicting the degree of ion pairing, new electrostatic equations (including induced dipoles) are presented, for ion-pair and other associations that occur in anhydrous amine/carboxylic acid mixtures. The equations present the association Gibbs energies ΔGA (and thus the association constants KA) as functions of three fundamental properties: the acid/base mixing ratio (n = xA/xB), the HA-to-B proton-transfer strength (ΔpKa,ε=78), and the dielectric constant (relative permittivity) of the mixture (ε). Parameter values were obtained from fits to constant-dielectric quantum chemistry data (obtained and presented here). These ΔGA functions were then used to predict ΔGioniz values for the net ion-generating (autoionization) equilibrium in carboxylic acid/amine mixtures: 2B(HA)n⇄B(HA)n−dHB++A(HA)n+d−1−, where n = xA/xB and d = degree of disproportionation. The agreement with experiment was excellent, demonstrating that these equations could have useful predictive power.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".