Diol it up: The influence of NaCl on methylglyoxal surface adsorption and hydration state at the air–water interface
Bibliographic record
Abstract
Methylglyoxal (MG)-an atmospherically important α-dicarbonyl implicated in aqueous-phase secondary organic aerosol formation-is known to be surface-active. Due to the presence of carbonyl moieties, MG can hydrate to form geminal diols in solution. Recently, it has been shown that MG exists predominantly as a monohydrate at the neat air-water interface. However, inorganic aerosol constituents have the potential to "salt-out" MG to the interface, shift its hydration equilibria, and catalyze self- and cross-oligomerization reactions. Here, we study the influence of the non-reactive salt, sodium chloride (NaCl), on the MG's surface adsorption and hydration state using vibrational sum frequency spectroscopy. The presence of NaCl is found to enhance MG's surface activity but not to the extent that water is fully excluded from the interface. Perturbations in the interfacial water structure are attributed to shifts in MG's hydration equilibrium at higher ionic strengths. Evidence of surface-active MG oligomer species is presented, but such oligomers are not thought to contribute significantly to the interfacial population. This work builds on the published studies on MG in pure water and gives insight into the interface's perturbation by NaCl, which has important implications for understanding MG's atmospheric fate.
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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".