A High‐Throughput Approach to Repurposing Olefin Polymerization Catalysts for Polymer Upcycling

Abstract

Efficient and economical plastic waste upcycling relies on the development of catalysts capable of polymer degradation. A systematic high-throughput screening of twenty-eight polymerization catalyst precursors, belonging to the catalyst families of metallocenes, ansa-metallocenes, and hemi-and post-metallocenes, in cis-1,4-polybutadiene (PB) degradation reveals, for the first time, important structure-activity correlations. The upcycling conditions involve activation of the catalysts (at 0.18 % catalyst loading) with tri-isobutyl aluminum at 50 °C in toluene. The data indicate the ability to degrade PB is a general reactivity profile of neutral group 4 metal hydrides. A simple quantitative-structure activity relationship (QSAR) model utilizing two descriptors for the distribution of steric bulk in the active pocket and one measuring the metal ion electrophilicity reveals the degradation ability improves with increased but not overbearing steric congestion and lower electrophilicity.

Triptycene as a scaffold in metallocene catalyzed olefin polymerization

“…[74][75][76] The protocol has been successfully used, in combination with M06-2X 77 single point energy (SP) corrections, to address several polymerization related problems: absolute barrier heights for propagation, 78 comonomer reactivity ratios, 19,20 metal-carbon bond strengths under polymerization conditions, [21][22][23] electronic and steric tuning of MW capability, 33 and was previously used to generate quantitative structure-property relationship (QSPR) models for stereoselectivity, regioselectivity, molar mass capability in propene polymerization and comonomer affinity in copolymerization, 12,13,16,29,31,32,40 and metal catalysed polybutadiene degradation. 79 The density fitting approximation (Resolution of Identity, RI) [80][81][82][83] and standard Gaussian16 quality settings were used at the optimization stage and SP calculations. All structures represent true minima (as indicated by the absence of imaginary frequencies).…”

End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler-Natta Propylene Polymerization

“…In such a context, the introduction of high-throughput experimentation (HTE) represented a breakthrough. [34][35][36][37][38] Throughput intensification in organometallic catalysis is much lower than in pharma; yet, a 100-fold increase relative to conventional methods with similar or even higher quality standards is easily achieved. In our labs, we recently undertook a collaborative program for implementing advanced HTE tools and methods for the rapid exploration of the variables hyperspace in ZN PP catalysis.…”

Prediction of Reaction Orthogonality using Machine Learning

“…Machine learning has become an attractive tool in chemistry to make predictions such as reactivity, [1][2][3][4][5][6] optimal reaction conditions, 7-10 molecular properties, 11 and mechanistic information. [12][13] These cases typically require large datasets, generated either through systematic high-throughput experiments [14][15][16] or large-scale computational studies. [17][18] It is, however, less feasible for organometallic systems [19][20][21][22][23][24] to generate large quantities of data, given the complexity of syntheses that may require multiple steps along with catalytic mechanisms involving several intermediates and transition states.…”