Issue 1, 2023

Integrating machine learning and digital microfluidics for screening experimental conditions

Abstract

Digital microfluidics (DMF) has the signatures of an ideal liquid handling platform – as shown through almost two decades of automated biological and chemical assays. However, in the current state of DMF, we are still limited by the number of parallel biological or chemical assays that can be performed on DMF. Here, we report a new approach that leverages design-of-experiment and numerical methodologies to accelerate experimental optimization on DMF. The integration of the one-factor-at-a-time (OFAT) experimental technique with machine learning algorithms provides a set of recommended optimal conditions without the need to perform a large set of experiments. We applied our approach towards optimizing the radiochemistry synthesis yield given the large number of variables that affect the yield. We believe that this work is the first to combine such techniques which can be readily applied to any other assays that contain many parameters and levels on DMF.

Graphical abstract: Integrating machine learning and digital microfluidics for screening experimental conditions

Supplementary files

Article information

Article type
Paper
Submitted
15 Aug. 2022
Accepted
14 Nov. 2022
First published
23 Nov. 2022

Lab Chip, 2023,23, 81-91

Integrating machine learning and digital microfluidics for screening experimental conditions

F. Ahmadi, M. Simchi, J. M. Perry, S. Frenette, H. Benali, J. Soucy, G. Massarweh and S. C. C. Shih, Lab Chip, 2023, 23, 81 DOI: 10.1039/D2LC00764A

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