Trapping proton transfer intermediates in the disordered hydrogen-bonded network of cryogenic hydrofluoric acid solutions
Bibliographic record
Abstract
A molecular-level description of the structural and dynamical aspects that are responsible for the weak acid behaviour of dilute hydrofluoric acid solutions and their unusual increased acidity at near equimolar concentrations continues to elude us. We address this problem by reporting reflection-absorption infrared spectra (RAIRS) of cryogenic HF-H(2)O binary mixtures at various compositions prepared as nanoscopic films using molecular beam techniques. Optical constants for these cryogenic solutions [n(omega) and k(omega)] are obtained by iteratively solving Fresnel equations for stratified media. Modeling of the experimental RAIRS spectra allow for a quantitative interpretation of the complex interplay between multiple reflections, optical interference and absorption effects. The evolution of the strong absorption features in the intermediate 1000-3000 cm(-1) range with increasing HF concentration reveals the presence of various ionic dissociation intermediates that are trapped in the disordered H-bonded network of cryogenic hydrofluoric acid solutions. Our findings are discussed in light of the conventional interpretation of why hydrofluoric acid is a weak acid revealing molecular-level details of the mechanism for HF ionization that may be relevant to analogous elementary processes involved in the ionization of weak acids in aqueous solutions.
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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".