Calibration-free quantification and automated data analysis for high-throughput reaction screeningOpen Access

Katzenburg, Felix; Boser, Florian; Schaefer, Felix R.; Pfluger, Philipp M.; Glorius, Frank

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

The accelerated generation of reaction data through high-throughput experimentation and automation has the potential to boost organic synthesis. However, efforts to generate diverse reaction datasets or identify generally applicable reaction conditions are still hampered by limitations in reaction yield quantification. In this work, we present an automatable screening workflow that facilitates the analysis of reaction arrays with distinct products without relying on the isolation of product references for external calibrations. The workflow is enabled by a flexible liquid handler and parallel GC-MS and GC-Polyarc-FID analysis while we introduce pyGecko, an open-source Python library for processing GC raw data. pyGecko offers comprehensive analysis tools allowing for the determination of reaction outcomes of a 96-reaction array in under a minute. Our workflow's utility is showcased for the scope evaluation of a site-selective thiolation of halogenated heteroarenes and the comparison of four cross-coupling protocols for challenging C–N bond formations.

Details zur Publikation

FachzeitschriftDigital Discovery
Jahrgang / Bandnr. / Volume4
Ausgabe / Heftnr. / Issue2
StatusVeröffentlicht
Veröffentlichungsjahr2025
DOI10.1039/d4dd00347k
Link zum Volltexthttps://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00347k
StichwörterpyGecko; high-throughput experimentation (HTE); machine learning; screenings

Autor*innen der Universität Münster

Glorius, Frank
Professur für Organische Chemie (Prof. Glorius)
Katzenburg, Felix
Professur für Organische Chemie (Prof. Glorius)
Pflüger, Philipp Miro
Professur für Organische Chemie (Prof. Glorius)
Schäfer, Felix Richard
Professur für Organische Chemie (Prof. Glorius)