Reactive Uptake of N<sub>2</sub>O<sub>5</sub>on Aqueous H<sub>2</sub>SO<sub>4</sub>Solutions Coated with 1-Component and 2-Component Monolayers
Bibliographic record
Abstract
Reactive uptake of N(2)O(5) on aqueous sulfuric acid solutions was studied in the presence of 1-component (octadecanol) and 2-component (octadecanol + phytanic acid) monolayers. In the 1-component monolayer experiments, the reactive uptake coefficient depended strongly on the molecular surface area of the surfactant. Also, the 1-component monolayer showed significant resistance to mass transfer even when the fractional surface coverage of the surfactant was less than 1. For example, a monolayer of 1-octadecanol with a fractional surface coverage of 0.75 decreased the reactive uptake coefficient by a factor of 10. This is consistent with previous studies. In the 2-component monolayer experiments, the reactive uptake coefficient depended strongly on the composition of the monolayer. When the monolayer contained only straight-chain molecules (1-octadecanol), the reactive uptake coefficient decreased by a factor of 42 due to the presence of the monolayer. However, when the monolayer contained 0.20 mole fraction of a branched surfactant (phytanic acid) the reactive uptake coefficient only decreased by a factor of 2. Hence, a small amount of branched surfactant drastically changes the overall resistance to reactive uptake. Also, our results show that the overall resistance to reactive uptake of 2-component monolayers can be predicted reasonably accurately by a model that assumes the resistances to mass transfer can be combined in parallel.
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) | 0.001 | 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".