A Robotic Approach to Polymerization Kinetics: A Case Study on Copolymerization Parameter Estimation

Abstract

Automation and high-throughput (HTP) experimentation are transforming chemistry, yet the high cost of robotic platforms limits accessibility. Pipetting robots such as the Opentrons OT-2 provide a cost-effective, open-source alternative, but their application to radical polymerization in well plates has been restricted by challenges such as deoxygenation at microliter scale. Here, we establish a robust workflow for thermal radical polymerization in 96-well plates using the OT-2, supported by custom 3D-printed components for automated NMR sample preparation. This system enables rapid and reproducible data generation while eliminating human bias from experimentation. We demonstrate its utility through the study of copolymerization kinetics, where inconsistent methods, reporting, and model selection have created significant data gaps for predictive modeling. By combining robotic HTP experimentation with IUPAC-recommended evaluation methodology, we provide standardized datasets for predicting reactivity ratios of six monomer pairs: BMA-BA (r1=2.33, r2=0.78), BMA-St (r1=0.61, r2=1.67), St-BA (r1=2.01, r2=0.40), St-MMA (r1=0.80, r2=1.02), GMA-BA (r1=1.42, r2=0.55), and GMA-St (r1 = 0.66, r2 = 1.60). Each dataset can be generated and analyzed within hours, offering a powerful automated platform for systematic polymerization studies. This work establishes the OT-2 as a practical, accessible tool for accelerating polymer research and enabling data-driven chemical discovery.

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Article information

Article type
Edge Article
Submitted
18 Mar 2026
Accepted
15 Apr 2026
First published
17 Apr 2026
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2026, Accepted Manuscript

A Robotic Approach to Polymerization Kinetics: A Case Study on Copolymerization Parameter Estimation

L. Alexander, V. F. Jafari and T. Junkers, Chem. Sci., 2026, Accepted Manuscript , DOI: 10.1039/D6SC02232D

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