A spectroscopic and computer simulation study of butanol vapors
Bibliographic record
Abstract
Clusters of butanol formed above neat liquid samples were entrained in a supersonic jet and probed using 10.5 eV vacuum ultraviolet laser single-photon ionization/time-of-flight mass spectrometry. The four different isomers of butanol (n-butanol, sec-butanol, iso-butanol, and tert-butanol) were studied separately to assess the influence of the structure of the alkyl chain on the formation and stability of the hydrogen bonded clusters. Most of the higher mass features observed in the mass spectra could be assigned to protonated alcohol clusters, H(ROH)n+, n⩽3; R=C4H9, that arise from facile proton-alkoxy radical/alkoxide anion dissociation. Signals due to protonated trimers were only evident in the spectra of tert- and sec-butanol. Empirical force fields, density functional theory and ab initio methods were used to identify the geometries of all clusters up to the pentamers for the different isomers. Monte Carlo simulations established vapor-phase cluster distributions, while molecular dynamics was used to assess the relative stability of the isomeric tetramers. Together, these experimental and theoretical results suggest that butanol tetramers are “magic-number” structures, and that the protonated ion signals of size n could be correlated with the neutral cluster of size n+1, provided the vapor pressures sampled in the supersonic jet exceeded equilibrium values.
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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) | 0.002 | 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".