Automated femtoliter droplet-based determination of oil-water partition coefficient

25Citations
Citations of this article
27Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The oil-water partition coefficient of organic compounds is an essential parameter for the determination of their behaviors in environments, food, drug delivery, and biomedical systems, just to name a few. In this work, we establish a highly efficient approach to quantify the partition/distribution coefficient using surface femtoliter droplets. In our approach, droplets of 1-octanol were produced on the surface of a solid substrate in contact with the flow of an aqueous solution of the analyte. The analyte was rapidly enriched in the droplets from the flow and reached the partition equilibrium in a few seconds. The entire procedure was automated by continuous solvent exchange, and the analyte partition in the droplets was quantified from the in situ UV-vis spectrum collected by a microspectrophotometer. Our approach was validated for several substances with the octanol-water partition/distribution coefficient ranging from -1.5 to 4, where our results were in good agreement with the values reported in the literature. This method took ∼3 min to detect one analyte with the volume of the organic solvent at ∼50 μL. Thus, our surface droplet platform can greatly minimize the consumption of both solvent and analytes and can shorten the time for the determination of the partition of new compounds, which overcomes the drawbacks of the traditional shake-flask method and presents excellent reproducibility, high accuracy, cost-effectiveness, and labor-saving operation. The highly efficient micro/nanoextraction, partition, and real-time detection enabled by the surface droplets has the potential for many other high-throughput applications.

Cite

CITATION STYLE

APA

Li, M., Dyett, B., & Zhang, X. (2019). Automated femtoliter droplet-based determination of oil-water partition coefficient. Analytical Chemistry, 91(16), 10371–10375. https://doi.org/10.1021/acs.analchem.9b02586

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free