  This paper addresses the gap between the capabilities and utilisation of
robotics and automation in laboratory settings and builds upon the concept of
Self Driving Labs (SDL). %to significantly impact laboratory operations. 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, complex material characterisation modeling, 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 contributions of the paper lie in introducing an adaptable classification
station for novel characterisation methods and presenting an innovative
methodology for polymer degradation rate measurement. The proposed system has
the potential to accelerate the development of high-throughput screening and
characterisation methods in material and chemistry laboratories.
