Probing the Impact of Solvent on Lewis Acid Catalysis via Fluorescent Lewis Adducts
Bibliographic record
Abstract
Over the years, various multiparameter methods have been developed to measure the strength of a Lewis acid. However, a major challenge for these measurements lies in the complexity that arises from variables such as solvent and other fundamental interactions, as well as perturbations of Lewis acids as their reaction environment changes. Herein, we evaluate the impact of solvent effects on the Fluorescent Lewis Adduct (FLA) method using a series of representative Lewis acids. The solution-state nature of the FLA method in particular, offers the ability to correlate Lewis Acid Units (LAUs) obtained from the FLA measurement with chemical reactivity. The binding of a Lewis acid in various solvents reveals a measurable dichotomy between both polarity and donor ability of the solvent. While not strictly separable, we observe that, as solvent polarity increases observed LAUs increase; however, as solvent donor ability increases, the observed LAUs decrease. This dichotomy was confirmed by titration data and catalytic Diels-Alder cycloaddition and hydrosilylation reactions, illustrating that solvation effects can be appropriately gauged by LAU values determined from the FLA method.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".