Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation
<|reference_start|>Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation: 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.<|reference_end|>
@article{azizi2024autonomous,
    title={Autonomous Hyperspectral Characterisation Station: Robotically Assisted
  Characterisation of Polymer Degradation},
    author={Shayan Azizi, Ehsan Asadi, Shaun Howard, Benjamin W. Muir, Riley
  O'Shea, Alireza Bab-Hadiashar},
    journal={arXiv preprint arXiv:2402.11763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11763},
    primaryClass={eess.SY cs.SY}
}