Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.815602
Title: A mobile robotic researcher
Author: Burger, Benjamin
ISNI:       0000 0004 9358 4665
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2020
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Abstract:
This work describes the development of an autonomous system aimed at photocatalysis research. Recent advancements in enabling technologies (e.g. collaborative robots) allow for novel autonomous approaches in materials science. The introduction of mobile robots in a laboratory environment, along with recent advancements in AI driven searches and a drift towards automation in various analytical equipment have led to various autonomous research systems. Here, we aim to combine automated experiments with machine-learning to automate the researcher completely during the experiment. By introducing this concept into the field of photocatalysis, we have reduced human-error in the measurements and reduced the labor required for performing these experiments. To this end, we have developed modular stations, each performing one atomic operation of the experiment, such as solid dispensing, capping, or analysis, operated using a mobile robot. The mobile robot was programmed to handle vials, cartridges filled with solids, and racks. With the modular workflow, machine-learning was use to generate new candidates based on previous experiments in an active learning paradigm. We show that the KUKA Mobile Robot (KMR) can operate each step of the workflow using modular stations. Finally, we formulated five chemical hypotheses to improve a hydrogen-evolving catalyst formulation. Each hypothesis selects one or two compounds, and thereby collectively defined a chemical formulation space of 11 components. We show how Bayesian Optimization can evaluate the hypotheses within this search space to ultimately improve the catalytic performance by a factor of six.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.815602  DOI:
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