ArticlePolymers2024
Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of the Melt Flow Rate and Shear Viscosity.
Article in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Article
- Impact of EVOH, OrmocerPolymers · 2025Article
- An Artificial Intelligence-Based Melt Flow Rate Prediction Method for Analyzing Polymer Properties.Polymers · 2025Article
- Article
- Development of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds.Polymers · 2025Article
- Development of an Analytical Model for Predicting the Tensile Modulus of Complex Polypropylene Compounds.Polymers · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R
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Registered trials
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