Probing chiral solute-water hydrogen bonding networks by chirality transfer effects: A vibrational circular dichroism study of glycidol in water
Bibliographic record
Abstract
Vibrational absorption (VA) and vibrational circular dichroism (VCD) spectra of (S)-(-)-glycidol were measured in water with a concentration of 6.0M in the 1000-1750 cm(-1) region. Prominent and complex VCD spectral features were detected at the water bending vibrational region. Our experimental results show that water molecules can become optically active through hydrogen bonding interactions with glycidol molecules. To model the glycidol-water hydrogen bonding network in the solution, molecular dynamics simulations using the AMBER9 suite of programs were carried out. Altogether, 34 conformers of the small glycidol-(water)(N) clusters with N=1, 2, 3, and 4 were considered. Geometry optimizations, harmonic frequency calculations, and the VA and VCD intensity predictions of these small glycidol-water clusters were performed at the B3LYP/6-311++G(d,p) level of theory using the GAUSSIAN 03 program package. Strong cooperative hydrogen bonding effects were detected in the larger glycidol-(water)(N) clusters. The population weighted VA and VCD spectra of each N group of glycidol (water)(N=1,2,3,4) were used to produce the simulated VA and VCD spectra, which are in good agreement with the experimental VA and VCD spectra. The study shows that all these clusters make important contributions to the observed spectra and are the most important species in the aqueous solution with complicated equilibriums among them.
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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".