A Novel Flow Reactor for Studying Reactions on Liquid Surfaces Coated by Organic Monolayers: Methods, Validation, and Initial Results
Bibliographic record
Abstract
A new flow reactor has been developed that allows the study of heterogeneous kinetics on an aqueous surface coated by an organic monolayer. Computational fluid dynamics simulations have been used to determine the flow characteristics for various experimental conditions. In addition a mathematical framework has been developed to derive the true first-order wall loss rate coefficient, k(1st)(w), from the experimentally observed wall loss rate, k(obs). Validation of the new flow reactor is performed by measuring the uptake of O(3) by canola oil as a function of pressure and flow velocity and the reactive uptake coefficients of N(2)O(5) by aqueous 60 wt % and 80 wt % H(2)SO(4). Using this new flow reactor, we also determined the reactive uptake coefficient of N(2)O(5) on aqueous 80 wt % H(2)SO(4) solution coated with an 1-octadecanol (C(18)H(37)OH) monolayer. The uptake coefficient was determined as (8.1 +/- 3.2) x 10-4, which is about 2 orders of magnitude lower compared to the reactive uptake coefficient on a pure aqueous 80 wt % H(2)SO(4) solution. Our measured reactive uptake coefficient can be considered as a lower limit for the reactive uptake coefficient of aqueous aerosols coated with organic monolayers in the atmosphere, because in the atmosphere organic monolayers will likely also consist of surfactants with shorter lengths and branched structures which will have a smaller overall effect.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".