Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation

TL;DR

The paper presents an automated, robot‑assisted hyperspectral imaging station designed to perform high‑throughput, non‑destructive temporal characterization of polymer degradation, addressing the gap between laboratory automation and extended‑duration measurements. By integrating a modular ROS‑based control framework with a robust image‑processing pipeline and automated data analysis, the system measures degradation rates—primarily for PLA—under varying pH, molecular weight, end groups, and blends. The study compares simple exponential and hybrid surface‑mass degradation models, finding the latter provides superior fit (≈$R^2>0.99$) while still reporting half‑life via the exponential form; GPC validation confirms the degradation dynamics. The results demonstrate substantial throughput gains (30–42% of data points with maintained accuracy) and reveal that end‑group chemistry and molecular weight jointly influence degradation, laying groundwork for automated, high‑throughput screening of polymer blends and other temporally evolving materials in chemical labs.

Abstract

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.

Figures (12)

• Figure 1: Robot-aided hyperspectral mapping and data sampling.
• Figure 2: Overall system design of autonomous hyperspectral characterisation station for laboratories.
• Figure 3: A black and white representation of hyperspectral images, used to measure the degradation rate of polymers. The degradation process is evident as time (in hours) progresses from the left to the right image.
• Figure 4: Total light reflected by the control samples. Lactide and Lactic acid aren't contributing to a signal change (plotted with the same range as the actual results shown in subsequent figures). The standard deviation of reflection intensity values for these controlled samples is less than 5% of their average values for all cases.
• Figure 5: Schematic diagram of the imaging procedure. The lid manipulations are crucial steps to minimise evaporation when idle and mitigate any potential artefacts during the imaging process.