<p>The notion of autonomous laboratories is of much interest to the chemical science community. Promises of increased efficiency and throughput of discovery, beyond that of automated platforms, has already begun to be fulfilled by autonomous continuous flow reactors and desktop robots. For fully autonomous laboratories to be further realized, various components in these systems require automation. Herein this work, are presented multiple data-driven statistical methods for automating and optimizing various chemical systems and processes. Presented are: the development and deployment of a general stochastic optimization algorithm, a machine learning-based solvent selection pipeline for organic transformations, a generalized data-dependent scoring methodology for antibody assay development, the prototyping of an automated platform for ion-molecule reactions inside a linear ion trap, and a review on recent developments for machine learning and mass spectrometry. In summary, these works present various components for furthering the automation of chemistry.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23297942 |
| Date | 06 June 2023 |
| Creators | Armen G Beck (14905903) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/BIT_BY_BIT_CHEMISTRY_OPTIMIZATION_AND_AUTOMOATION_OF_CHEMICAL_SYSTEMS/23297942 |
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