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<p>Methanol-to-hydrocarbons (MTH) processes offer the potential for transitioning the production of base chemicals and liquid fuels to alternative fossil and renewable resources. The commercialization of ZSM-5 (MFI) and SAPO-34 (CHA) catalysts, coupled with the complexity of the reaction mechanism, has spurred extensive research to increase the selectivity for desired hydrocarbons and also mitigate coke-induced deactivation. Here we present a quantitative performance assessment of the zeolites and zeotypes studied as MTH catalysts over the past three decades. We leverage a comprehensive dataset to evaluate selectivity–stability profiles and examine how structural descriptors and operating conditions govern performance, with additional insights imparted through machine-learning-based analytics. These structure–conditions–performance relationships are discussed in the context of current mechanistic understanding, providing a perspective on MTH catalysis. The analysis shows that product selectivity and coking sensitivity are strongly determined by catalyst structure, particularly the micropore topology, but are also influenced by conditions such as temperature and the addition of co-feeds, which substantially influence competing pathways.</p><p></p>

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Exploring the landscape of methanol-to-hydrocarbons conversion catalysts

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Vladimir Paunović,
  • Georg Liesche,
  • Kai Sundmacher,
  • Jeroen A. van Bokhoven

摘要

Methanol-to-hydrocarbons (MTH) processes offer the potential for transitioning the production of base chemicals and liquid fuels to alternative fossil and renewable resources. The commercialization of ZSM-5 (MFI) and SAPO-34 (CHA) catalysts, coupled with the complexity of the reaction mechanism, has spurred extensive research to increase the selectivity for desired hydrocarbons and also mitigate coke-induced deactivation. Here we present a quantitative performance assessment of the zeolites and zeotypes studied as MTH catalysts over the past three decades. We leverage a comprehensive dataset to evaluate selectivity–stability profiles and examine how structural descriptors and operating conditions govern performance, with additional insights imparted through machine-learning-based analytics. These structure–conditions–performance relationships are discussed in the context of current mechanistic understanding, providing a perspective on MTH catalysis. The analysis shows that product selectivity and coking sensitivity are strongly determined by catalyst structure, particularly the micropore topology, but are also influenced by conditions such as temperature and the addition of co-feeds, which substantially influence competing pathways.