Photochemical transformation of weakly absorbing organics by visible light in microdroplets
Bibliographic record
Abstract
Microdroplets provide a solute environment where concentrations readily surpass bulk saturation limits. How this persistent supersaturation influences reaction rates and photochemical pathways remains largely unexplored. Here, we investigate photochemical transformations of six "non-absorbing" organic molecules (glycine, proline, serine, glucose, ribose, and deoxyribose) in single aqueous microdroplets using optical trapping at two different laser wavelengths (473 and 532 nm). The contact-free environment allows for the suspension of supersaturated microdroplets, similar to those observed in natural aerosol particles in the atmosphere. In microdroplets containing water and the organic species, we observed photochemical transformations at optical wavelengths for all solutes. The organic molecules considered here are typically known to absorb light in the UV range, specifically at wavelengths no longer than 200 nm. The observed timescales for these photochemical reactions scale with the droplet radius-squared. We expand upon a photochemical adaptation of the Finke-Watzky model to demonstrate that in all cases, volume-dependent photochemical reactions occur. Through systematic elimination of confounding factors, we establish that weak visible absorption in the low-energy tail of UV bands drives photochemistry across all of these organic solutes, demonstrating that extremely weak molar absorptivities can facilitate significant photochemical transformations in the confined environment of a microdroplet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".