Advancing the Compositional Analysis of Olefin Polymerization Catalysts with High-Throughput Fluorescence Microscopy

Maximilian J Werny, Kirsten B Siebers, Nicolaas H Friederichs, Coen Hendriksen, Florian Meirer, Bert M Weckhuysen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

To optimize the performance of supported olefin polymerization catalysts, novel methodologies are required to evaluate the composition, structure, and morphology of both pristine and prepolymerized samples in a resource-efficient, high-throughput manner. Here, we report on a unique combination of laboratory-based confocal fluorescence microscopy and advanced image processing that allowed us to quantitatively assess support fragmentation in a large number of autofluorescent metallocene-based catalyst particles. Using this approach, significant inter- and intraparticle heterogeneities were detected and quantified in a representative number of prepolymerized catalyst particles (2D: ≥135, 3D: 40). The heterogeneity that was observed over several stages of slurry-phase ethylene polymerization (10 bar) is primarily attributed to the catalyst particles' diverse support structures and to the inhomogeneities in the metallocene distribution. From a mechanistic point of view, the 2D and 3D analyses revealed extensive contributions from a layer-by-layer fragmentation mechanism in synergy with a less pronounced sectioning mechanism. A significant number of catalyst particles were also found to display limited support fragmentation at the onset of the reaction (i.e., at lower polymer yields). This delay in activity or "dormancy" is believed to contribute to a broadening of the particle size distribution during the early stages of polymerization. 2D and 3D catalyst screening via confocal fluorescence microscopy represents an accessible and fast approach to characterize the structure of heterogeneous catalysts and assess the distribution of their fluorescent components and reaction products. The automation of both image segmentation and postprocessing with machine learning can yield a powerful diagnostic tool for future research as well as quality control on industrial catalysts.

Original languageEnglish
Pages (from-to)21287-21294
Number of pages8
JournalJournal of the American Chemical Society
Volume144
Issue number46
Early online date8 Nov 2022
DOIs
Publication statusPublished - 23 Nov 2022

Bibliographical note

Funding Information:
The research was funded by a grant from the Dutch Polymer Institute (DPI, P.O. Box 902, 5600 AX Eindhoven, The Netherlands) and represents a part of the research program of DPI project no. 813. F.M. acknowledges additional funding from the Netherlands Organization for Scientific Research (NWO) VIDI grant (723.015.007).

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

Keywords

  • Ethylene polymerization
  • Fragmentation
  • Heterogeneous polymerization
  • Metallocene
  • Particle
  • Phase polymerization
  • Single-molecule
  • Supported catalysts
  • Time
  • Video-microscopy

Fingerprint

Dive into the research topics of 'Advancing the Compositional Analysis of Olefin Polymerization Catalysts with High-Throughput Fluorescence Microscopy'. Together they form a unique fingerprint.

Cite this