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Computational Study of Alkane Adsorption in Brønsted Acid Zeolites for More Efficient Alkane Cracking.

Li-Chiang Lin
Published in: Langmuir : the ACS journal of surfaces and colloids (2022)
Alkane cracking using Brønsted acid zeolites, catalytically converting long-chain molecules into smaller ones, is critical to fuel and chemical production. To enable more energy-efficient cracking processes, developing zeolite catalysts with enhanced performance (i.e., a faster reaction rate with reduced methane formation) plays a substantial role. Given the adsorption thermodynamics of alkanes onto the protons of Brønsted acid zeolites is a key step in the overall cracking reactions; therefore, catalysts possessing a more negative Gibbs free energy of adsorption for alkanes with a larger central-to-terminal bond adsorption selectivity to promote central cracking are of particular interest. This Feature Article discusses recent computational developments and discoveries by Lin and co-workers in studying the adsorption of alkanes in Brønsted acid zeolites. Their developed approach, employing configurational bias Monte Carlo with domain decomposition, with a newly parametrized molecular potential to compute the adsorption properties is first introduced. With these developments, the roles of the Si/Al ratio and Al sitting are explored and discussed. Subsequently, the Feature Article discusses the key findings obtained from a large-scale computational screening of studying more than 100 000 possible zeolite structures. The performance of identified top candidates and associated key structural features leading to desirable adsorption properties are highlighted.
Keyphrases
  • aqueous solution
  • machine learning
  • monte carlo
  • deep learning
  • risk assessment
  • neural network
  • anaerobic digestion