Issue 5, 2025

High-throughput robotic colourimetric titrations using computer vision

Abstract

A high-throughput (HTE) robotic colourimetric titration workstation was developed using a commercial liquid handling robot (Opentrons OT-2) and computer vision-based analysis. While designed for multiple titration applications, hydrogen peroxide (H2O2) determination serves as the most elaborate and well-characterized demonstration of its capabilities. Specifically, potassium permanganate (KMnO4) redox titration was employed to quantify the hydrogen peroxide (H2O2) concentration, leveraging the distinct colourimetric transition from colourless to pale pink at the titration endpoint. To monitor this colour change, a webcam was installed on the OT-2 pipette mount, capturing real-time titration progress. Image analysis was enhanced through VGG-augmented UNet for segmentation and the CIELab colour model, ensuring robust and reproducible detection of subtle colour changes. The sensitivity test of the computer vision-aided colour analysis was strongly correlated to UV-vis spectroscopy (R2 = 0.9996), with a good linear dynamic range at low concentrations. The analytical accuracy of this workstation was ±11.9% in a 95% confidence interval and its corresponding absolute concentration difference was only 0.50 mM. To validate its real-world applicability, this workstation was first deployed to monitor the photoproduction of H2O2 over a conjugated polymer photocatalyst, DE7. In addition to performing redox titrations, we demonstrated that this workstation can also be used for acid–base titration and complexometric titration, capturing a diverse range of colour changes.

Graphical abstract: High-throughput robotic colourimetric titrations using computer vision

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Article information

Article type
Paper
Submitted
18 Oct 2024
Accepted
26 Mar 2025
First published
11 Apr 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025,4, 1276-1283

High-throughput robotic colourimetric titrations using computer vision

Y. Li, B. Dutta, Q. J. Yeow, R. Clowes, C. E. Boott and A. I. Cooper, Digital Discovery, 2025, 4, 1276 DOI: 10.1039/D4DD00334A

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