Vibrational spectroscopic studies of aqueous solutions of <i>tert</i>-butyl alcohol and <i>tert</i>-butylamine
Bibliographic record
Abstract
Raman and FT-IR absorption spectra of aqueous tert-butyl alcohol (t-BuOH) and tert-butylamine (t-BuNH2) in the region of the OH and NH2 stretching and bending modes have been measured as a function of organic co-solvent concentration in the whole co-solvent mole fraction region. The major observed changes of the aqueous binary solution spectra compared with the solvent spectra are a loss or gain of band intensity. In particular, the observed changes in intensities and linewidths of some bands were significantly more pronounced at low concentrations of organic co-solvents in water, where t-BuOH and t-BuNH2 tend to integrate into the water structure. Clear evidence of structural enhancement of the network is obtained in dilute solutions as well as destruction of the network by hydrophobic interactions as the concentration is increased. Generally, the interpretation of the spectra is in agreement with the capacity of the hydrophobic co-solvent to break the structure of water in the more concentrated aqueous solutions and to enhance the structure in dilute solutions. Vibrational intensities and frequency shifts of some bands show definite trends with varying the concentration of the solutions. In the concentration-dependence study, unusual linewidth changes of certain bands were observed.Key words: infrared, Raman spectra, aqueous, tert-butanol, tert-butylamine.
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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".