Long-Range Dipole Alignment of Molecules Generates Strong Local Electric Fields at Air–Water Interfaces
Bibliographic record
Abstract
It is currently thought that chemical and physical properties of air−water interfaces can accelerate organic reactions in microdroplets. Recent studies have focused on strong electric fields due to charge separation near the air–water interface as a potential source of this enhanced reactivity. Herein, we propose that strong electric fields can also arise from the dipole alignment of organic molecules perpendicular to the air–water interface. Full-spectral confocal fluorescence microsco-py images show that the voltage-sensitive fluorophore, 4-Di-1-ASP, aligns its dipole perpendicu-lar to the air–water interface and exhibits an 18 nm shift in emission. Solvatochromic measure-ments and density functional theory calculations estimate the strength of this electric field at 7.2-13 MV/cm and 4.6 MV/cm, respectively. The interfacial region extends ~1 µm into the droplet and is largely insensitive to the addition of up to 2 M NaCl. Thus, these fields cannot be ex-plained solely by charge separation near the air–water interface. Measurements in a range of or-ganic solvents demonstrate that water must be present to observe a shift in the emission maxima of voltage-sensitive dyes at gas–liquid interfaces, indicating that water plays a key role in orient-ing the dyes. These results set the stage for molecular-level control at gas–liquid interfaces.
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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 | 0.001 |
| 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".