Direct Observations of Anthracene Clusters at Ice Surfaces
Bibliographic record
Abstract
Heterogeneous processes can control atmospheric composition. Snow and ice present important, but poorly understood, reaction media that can greatly alter the composition of air in the cryosphere in polar and temperate regions. Atmospheric scientists struggle to reconcile model predictions with field observations in snow-covered regions due to experimental challenges associated with monitoring reactions at air-ice interfaces, and debate regarding reaction kinetics and mechanisms has persisted for over a decade. In this work, we use wavelength-resolved fluorescence microscopy to determine the distribution and chemical speciation of the pollutant anthracene at the surfaces of environmentally relevant frozen surfaces. We show that anthracene adsorbs to frozen surfaces in monomeric form, but that following lateral diffusion, molecules ultimately reside within brine channels at saltwater ice surfaces, and in micron-sized clusters at freshwater ice surfaces; emission profiles indicate extensive self-association. We also measure anthracene photodegradation kinetics in aqueous solution and artificial snow prepared from frozen freshwater and saltwater solutions and use the micro-spectroscopic observations to explain the rate constants measured in different environments. These results resolve long-standing debates and will improve predictions of pollutant fate in the cryosphere. The techniques used can be applied to numerous surfaces within and beyond the atmospheric sciences.
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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) | 0.003 | 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".