Autonomous Hyperspectral Characterisation Station: Robot Aided Measuring of Polymer Degradation

Abstract

This paper addresses a gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL). We introduce an innovative approach to the temporal characterisation of materials. The article discusses the challenges posed by manual methods involving established laboratory equipment and presents an automated hyperspectral characterisation station. This station integrates robot-aided hyperspectral imaging (HSI), complex material characterisation modelling, and automated data analysis, offering a non-destructive and comprehensive approach. This work explains how the proposed assembly can automatically measure the half-life of biodegradable polymers with higher throughput and accuracy than manual methods. The investigation explores the effect of pH, number of average molecular weight (Mn), end groups, and blends on the degradation rate of polylactic acid (PLA). The novel contributions of the paper lie in introducing an adaptable classification station for characterisation and presenting an innovative methodology for polymer degradation rate measurements. The proposed system holds promise for expediting the development of high-throughput screening and characterisation methods within advanced material and chemistry laboratories.