Synthesis of Catalyst for Aqueous Polymerization: Perform Artificial Neural Network for The Prediction of Maximum Yield of Polymer
1Department of Chemistry, Shri G. S. Institute of Engineering and Technology, India
2Mechanical, Galgotias College of Engineering and Technology, India
*Author to whom correspondence should be addressed:
E-mail: yogeshshrivastava90@gmail.com (YS)
E-mail: yogeshshrivastava90@gmail.com (YS)
Received: December 05, 2024 | Revised: July 22, 2025 | Accepted: August 02, 2025 | Published: September 2025
Abstract
This research synthesized a new catalyst LTiCl2 using LH2, which is 2-(3, 5-Di-tert-butyl-2-hydroxybenzylamino)-succinic acid. This compound includes an additional donor to enhance the catalyst efficiency for the polymerization of polar olefins in aqueous medium. The properties of the resulting polymer were characterized by 1H NMR spectroscopy and dynamic light scattering (DLS). Moreover, it has been found that upon activation with BPh4-, the complex exhibits higher activity as a single-site catalytic species for polymerization reactions. These highly active species produce a syndiotactic-rich polymer with a narrow polydispersity index (PDI value in the range of 0.1-0.2). Moreover, the yield of the synthesized polymer has been measured at different combinations of catalyst, co-catalyst, and monomer. Furthermore, a prediction model has been developed to identify the variation in the yield of polymer concerning the variation in the moles of ingredients (catalyst, co-catalyst, and monomer). To develop the prediction model, an artificial neural network was employed. The analysis led to the identification of a safe zone that is predicted to achieve optimal yield. This zone was validated through additional experiments, and the results confirmed that it effectively maximizes polymer yield.
Keywords
yield ; Mathematical modeling ; polymethyl methacrylate ; Non-metallocene catalyst ; early transition metal ; aqueous polymerization
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