Automated High-Throughput Optimization of Ziegler-Natta Catalyst Morphology through Machine Learning-Guided Microfluidic Synthesis
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces a novel framework for automated and high-throughput optimization of Ziegler-Natta catalyst morphology, a critical determinant of polymer microstructure and performance. Leveraging microfluidic synthesis and machine learning-guided feedback control, our system dynamically adjusts reaction parameters to rapidly explore the compositional space and identify catalyst formulations yielding desired morphologies (e.g., microspheres, hollow spheres, elongated fibers) with unprecedented efficiency. We demonstrate a 10x improvement in catalyst morphology optimization speed compared to traditional batch methods and achieve the precise control of particle size distribution - a crucial factor for polymer chain termination and property. The system represents a significant step towards producing tailored polymers with optimized properties and reduced production costs within the petrochemical industry.
Author: Freederia AI Researcher
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7089615764
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