A “Holy Grail” of particle science has been to be able to predict and control particle average size and particle size distribution (PSD) for the myriad of catalyst and other particles formed synthetically, or naturally, throughout nature. We have been able to make a significant advance on this problem following a long-term, Professor Bruce Gates-type approach consisting of: (i) multiple years of synthesizing and fully characterizing the novel polyoxometalate (POM)-supported organometallic precatalyst, (Bu4N)5Na3{(1,5-COD)IrIPOM, where here POM = P2W15Nb3O629-, in a “molecular cookie-cutter” approach to a (soluble) oxide-supported, atomically dispersed (pre)catalyst; (ii) solving over a 5+ year period the challenging “is it homogeneous or heterogeneous catalysis problem” that resulted once the precatalyst is placed under H2 in the presence of olefins such as cyclohexene; (iii) fully characterizing the novel, record Ir(0)n catalytic activity Ir(0)n(POM9-)m nanoparticle catalyst that resulted; and then (iv) 25 years of developing the minimum mechanisms for particle formation and agglomeration—now a total of ≥96 distinct mechanism where there were zero prior to the first minimum mechanism for in particle formation that resulted from this system in 1997. Most recently and excitingly (v) what we have termed Mechanism-Enabled Population Balance Modeling (ME-PBM) has been developed via the combination of those minimum mechanisms with classic Population Balance Modeling—resulting in the ability to predict and begin to control particle-size and PSDs. The talk will strive to present the Gates-type approach that was both needed and successful en route to the current state-of-the-art of ME-PBM for the prediction, synthetic fine-tuning, and other understanding and control of the property-determining PSDs of catalyst and other particles formed throughout nature.

Nanoparticle catalysts particle-size distribution prediction and control via mechanism-enabled population balance modeling: Requirement for, and success of, a Bruce Gates-type approach

ACS Spring 2022

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