Size Effect on the Reaction Rate of Surface Nanodroplets
Bibliographic record
Abstract
Compartmentalizing reagents within small droplets is promising for highly efficient conversion and simplified procedures in many biphasic chemical reactions. In this work, surface nanodroplets (i.e., less than 100 nm in their maximal height) were employed to quantitatively understand the size effect on the chemical reaction rate of droplets. In our systems, a surface-active reactant in pure or binary nanodroplets reacted with the reactant in the bulk flow. Meanwhile, the product was removed from the droplet surface. The shrinkage rate of the nanodroplets was characterized by analyzing the lateral size as a function of time, where the droplet size was solely determined by the chemical reaction rate under a given flow condition for the transport of the reactant and the product. We found that the overall kinetics increases rapidly with the decrease in droplet’s lateral radius R, as d R /d t ∼ R –2 . The faster increase in the concentration of the product in smaller droplets contributes to accelerating reaction kinetics. The enhancement of reaction rates from small droplet sizes was further confirmed when a nonreactive compound was present inside the droplets without reducing the concentrations of the reactant and the product on the droplet surface. The results of our study improve the understanding of chemical kinetics with droplets. Our findings highlight the effectiveness of small droplets for the design and control of enhanced chemical reactions in a broad range of applications.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".