Optimization of Liquid Handling Parameters for Viscous Liquid Transfers with Pipetting Robots, a “Sticky Situation”

29 December 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Automated air-displacement pipettes have become a standard equipment for the transfer of liquids in laboratory settings. However, these tools fail to perform accurate and precise transfers for liquids with viscosities higher than 100 cP. In this study, we report a systematic protocol for optimizing liquid-handling parameters of automated pipettes to achieve accurate and precise transfers of viscous liquids (within 5% of percentage transfer error) with viscosities as high as 1275 cP using minimal transfer times. The protocol is based on the iterative gravimetric testing of different combinations of aspiration and dispense rates obtained by a Multi-Objective Bayesian Optimization (MOBO) algorithm. We demonstrate that optimal solutions obtained through MOBO can match or outperform solutions derived from human intuition, showing a consistent performance even when different pipetting equipment and tip geometries are used. Finally, we demonstrate that the protocol can be performed in fully automated closed-loop fashion by integrating an automated mass balance, increasing the manpower efficiency of the method. This offers a valuable advancement in the accurate manipulation of highly viscous liquids, with broad applications for the automation of various laboratory experiments.

Keywords

Automated Experimentation
Viscous Liquid Transfer
Machine Learning

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