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<p>The statistical design of experiment (sDoE) enables efficient exploration of complex chemical space by screening multiple factors simultaneously, overcoming the limitations of the one-factor-at-a-time (OFAT) approaches. In this study, Plackett–Burman design (PBD) was applied to screen five factors: electronic effect and Tolman’s cone angle of phosphine ligands (4), catalyst loading (2), bases (2), and solvent polarity (2) across twelve (12) C–C cross-coupling reactions for the Mizoroki–Heck, Suzuki–Miyaura, and Sonogashira–Hagihara reactions. While the importance of these factors is well established in cross-coupling chemistry, this study aimed to demonstrate how statistical PBD can compare and rank factors’ effect via high-throughput screening (HTS) context. The outcome identified influential factors for each reaction, indicating the efficiency of integrating HTS and sDoE. This proof-of-concept study demonstrates an initial screening approach for future optimisation using advanced designs such as response surface methodologies (RSM), providing a deeper understanding of complex chemical spaces by investigating factor interactions, catalyst design, and process development.</p>

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Application of statistical design of experiment to identify key factors in cross coupling reactions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Fulufhelo Radzilani,
  • Orpah Zinyemba,
  • Reinout Meijboom

摘要

The statistical design of experiment (sDoE) enables efficient exploration of complex chemical space by screening multiple factors simultaneously, overcoming the limitations of the one-factor-at-a-time (OFAT) approaches. In this study, Plackett–Burman design (PBD) was applied to screen five factors: electronic effect and Tolman’s cone angle of phosphine ligands (4), catalyst loading (2), bases (2), and solvent polarity (2) across twelve (12) C–C cross-coupling reactions for the Mizoroki–Heck, Suzuki–Miyaura, and Sonogashira–Hagihara reactions. While the importance of these factors is well established in cross-coupling chemistry, this study aimed to demonstrate how statistical PBD can compare and rank factors’ effect via high-throughput screening (HTS) context. The outcome identified influential factors for each reaction, indicating the efficiency of integrating HTS and sDoE. This proof-of-concept study demonstrates an initial screening approach for future optimisation using advanced designs such as response surface methodologies (RSM), providing a deeper understanding of complex chemical spaces by investigating factor interactions, catalyst design, and process development.