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

被引:1
|
作者
Katzenburg, Felix [1 ]
Boser, Florian [1 ]
Schaefer, Felix R. [1 ]
Pfluger, Philipp M. [1 ]
Glorius, Frank [1 ]
机构
[1] Westfal Wilhelms Univ, Organ Chem Inst, Corrensstrasse 40, D-48149 Munster, Germany
来源
DIGITAL DISCOVERY | 2025年 / 4卷 / 02期
关键词
MASS-SPECTROMETRY; DISCOVERY; HYDROGENATION; OPTIMIZATION; PLATFORM; PHENOLS; TOOL;
D O I
10.1039/d4dd00347k
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页码:384 / 392
页数:9
相关论文
共 50 条
  • [21] High-throughput metaproteomics data analysis with Unipept: A tutorial
    Mesuere, Bart
    Van der Jeugt, Felix
    Willems, Toon
    Naessens, Tom
    Devreese, Bart
    Martens, Lennart
    Dawyndt, Peter
    JOURNAL OF PROTEOMICS, 2018, 171 : 11 - 22
  • [22] Novel trends in high-throughput screening
    Mayr, Lorenz M.
    Bojanic, Dejan
    CURRENT OPINION IN PHARMACOLOGY, 2009, 9 (05) : 580 - 588
  • [23] High-Throughput Screening and Quantification of Pesticides in Paprika by UHPLC-Q-TOF/MS
    Liu, Xuan
    Fan, Yanhong
    Chang, Chi-Peng
    Lo, Chih-Kang
    Wang, Xu
    FOOD ANALYTICAL METHODS, 2021, 14 (10) : 2186 - 2198
  • [24] Feature cluster selection for high-throughput data analysis
    Yu, Lei
    INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2009, 3 (02) : 177 - 191
  • [25] High-Throughput Screening and Quantification of Pesticides in Paprika by UHPLC-Q-TOF/MS
    Xuan Liu
    Yanhong Fan
    Chi-Peng Chang
    Chih-Kang Lo
    Xu Wang
    Food Analytical Methods, 2021, 14 : 2186 - 2198
  • [26] Improving the Screening Analysis of Pesticide Metabolites in Human Biomonitoring by Combining High-Throughput In Vitro Incubation and Automated LC-HRMS Data Processing
    Huber, Carolin
    Mueller, Erik
    Schulze, Tobias
    Brack, Werner
    Krauss, Martin
    ANALYTICAL CHEMISTRY, 2021, 93 (26) : 9149 - 9157
  • [27] Combinatorial methods, automated synthesis and high-throughput screening in polymer research: Past and present
    Hoogenboom, R
    Meier, MAR
    Schubert, US
    MACROMOLECULAR RAPID COMMUNICATIONS, 2003, 24 (01) : 16 - 32
  • [28] Development of an Automated, High-throughput Sample Preparation Protocol for Proteomics Analysis
    Arul, Albert-Baskar
    Byambadorj, Munkhtsolmon
    Han, Na-Young
    Park, Jong Moon
    Lee, Hookeun
    BULLETIN OF THE KOREAN CHEMICAL SOCIETY, 2015, 36 (07): : 1791 - 1798
  • [29] Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics
    Malinowska, Julia M.
    Palosaari, Taina
    Sund, Jukka
    Carpi, Donatella
    Lloyd, Gavin R.
    Weber, Ralf J. M.
    Whelan, Maurice
    Viant, Mark R.
    METABOLITES, 2022, 12 (01)
  • [30] High-throughput, low-cost reaction screening using a modified 3D printer
    Schrader, Robert L.
    Ayrton, Stephen T.
    Kaerner, Andreas
    Cooks, R. Graham
    ANALYST, 2019, 144 (16) : 4978 - 4984