Raman Optical Activity of N‐Acetyl‐L‐Cysteine in Water and in Methanol: The “Clusters‐in‐a‐Liquid” Model and ab Initio Molecular Dynamics Simulations
Bibliographic record
Abstract
Raman and Raman Optical Activity (ROA) spectra of N-acetyl-L-cysteine (NALC), a flexible chiral molecule, were measured in water and in methanol to evaluate the solvent effects. Two different solvation approaches, that is, the DFT based "clusters-in-a-liquid" solvent model and the ab initio molecular dynamics (AIMD) simulations, were applied to simulate the Raman and ROA spectra. Systematic conformational searches were carried out using a recently developed conformational searching tool, CREST, with the inclusion of polarizable continuum model of water and of methanol. The CREST candidates of NALC and the NALC-solvent complexes were re-optimized and their Raman and ROA simulations were done at the B3LYP-D3BJ/def2-TZVP and the B3LYP-aug-cc-pVDZ//cc-pVTZ levels. Also, AIMD simulations, which includes some anharmonic effects and all intermolecular interactions in solution, were performed. By empirically weighting the computed Raman and ROA spectra of each conformer, good agreements with the experimental data were achieved with both approaches, while AIMD offered some improvements in the carbonyl and in the low wavenumber regions over the static DFT approach. The pros and cons of these two different approaches for accounting the solvent effects on Raman and ROA of this flexible chiral system will also be discussed.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".