IR, Raman, and Vibrational Optical Activity Spectra of Methyl Glycidate in Chloroform and Water: The <i>Clusters‐in‐a‐liquid</i> Solvation Model
Bibliographic record
Abstract
Abstract Solvent effects, in particular those involving water as the solvent, are of significant interest to the chemistry and physics communities. IR, vibrational circular dichroism (VCD), Raman, and Raman optical activity (ROA) spectra of methyl glycidate in two very different solvents, namely CCl4 and water, have been measured experimentally and simulated theoretically. The observed spectra in CCl4 could be well modelled using the polarizable continuum model for the solvent, whereas the situation is much different in water. The experimental VCD spectrum of methyl glycidate in water reveals strong induced VCD signatures in the water bending region, indicating the presence of the relatively long‐lived methyl glycidate‐watern complexes. We applied the clusters‐in‐a‐liquid approach to identify the dominant methyl glycidate‐water1,2 complexes which are the long‐lived species responsible for all the spectra observed in water. We examined the influences of solvent dielectric environment and the hydrogen‐bonding interactions on the conformational distribution of methyl glycidate. The geometry optimizations, frequency calculations, IR, VCD, Raman and ROA intensity calculations were performed at the B3LYP/6‐311++G(2d,p) and aug‐cc‐pVTZ levels of theory with D3BJ dispersion correction. It is particularly satisfying to note that the clusters‐in‐a‐liquid approach has captured all main experimental features in IR, VCD, Raman and ROA spectra of methyl glycidate in water.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".