Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
Determination of Molar Masses of Polymers I01:24

Determination of Molar Masses of Polymers I

Polymerization produces macromolecules with a range of chain lengths due to the random nature of molecular growth processes. As chains form and terminate at different stages, a single polymer sample contains molecules of varying sizes rather than a uniform structure. This variability is described using average molar masses and distribution-related parameters, which together provide a comprehensive understanding of polymer characteristics.The distribution of molar masses plays a critical role in...
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
Determination of Molar Masses of Polymers II01:27

Determination of Molar Masses of Polymers II

Polymer samples typically consist of macromolecular chains with a distribution of lengths, resulting in a range of molar masses rather than a single discrete value. Conventional descriptors such as the number-average molar mass and weight-average molar mass quantify this distribution but do not fully capture polymer behavior in solution..The viscosity-average molar mass provides a more realistic description of polymer behavior in solution because it accounts for the enhanced contribution of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparative Adsorption of Phenol and <i>p</i>-Chlorophenol on a Chitosan-Cellobiose Dimer in an Aqueous Medium: A DFT Study of Hydrogen Bonding and Noncovalent Interactions.

Molecules (Basel, Switzerland)·2026
Same author

Selective Production of Diesel-Range Hydrocarbons from Catalytic Pyrolysis of Polypropylene Waste Using Modified Natural Zeolites: Interplay of Acidity, Temperature, and Reaction Parameters.

Polymers·2026
Same author

Chemical Recycling of Post-Consumer Polystyrene by Thermal Pyrolysis: High-Yield Recovery of Aromatic Hydrocarbons for Circular Plastic Economy.

Polymers·2026
Same author

Investigation of the Antioxidant Activity of Hydroxycinnamic Acids, Hydroxybenzoic Acids, and Their Synthetic Diazomethane Derivatives.

Molecules (Basel, Switzerland)·2026
Same author

Computational Study of Graphene Quantum Dots (GQDs) Functionalized with Thiol and Amino Groups for the Selective Detection of Heavy Metals in Wastewater.

Molecules (Basel, Switzerland)·2025
Same author

Design and Characterization of Sustainable PLA-Based Systems Modified with a Rosin-Derived Resin: Structure-Property Relationships and Functional Performance.

Biomimetics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jul 16, 2026

Characteristics of Precipitation-formed Polyethylene Glycol Microgels Are Controlled by Molecular Weight of Reactants
11:32

Characteristics of Precipitation-formed Polyethylene Glycol Microgels Are Controlled by Molecular Weight of Reactants

Published on: December 23, 2013

Operational Domains Governing Melt Flow Index Variability in Industrial Polypropylene Production.

Joaquín Hernández-Fernández1,2, Juan López-Martínez3

  • 1Chemistry Program, Department of Natural and Exact Sciences, University of Cartagena, San Pablo Campus, Cartagena de Indias 130015, Colombia.

Polymers
|July 15, 2026
PubMed
Summary

Stable melt flow index (MFI) in polypropylene production is key for processing. This study found that residual MFI variability stems from interactions between reactor hydrodynamics, thermal conditions, and catalyst systems, not single factors, even with pure feedstocks.

Keywords:
Ziegler–Natta catalystsgas-phase polymerizationmelt flow indexmultivariate analysispolypropyleneprincipal component analysisprocess–quality relationshipsreactor fouling

More Related Videos

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

Ethylene Polymerizations Using Parallel Pressure Reactors and a Kinetic Analysis of Chain Transfer Polymerization
07:28

Ethylene Polymerizations Using Parallel Pressure Reactors and a Kinetic Analysis of Chain Transfer Polymerization

Published on: November 27, 2015

Related Experiment Videos

Last Updated: Jul 16, 2026

Characteristics of Precipitation-formed Polyethylene Glycol Microgels Are Controlled by Molecular Weight of Reactants
11:32

Characteristics of Precipitation-formed Polyethylene Glycol Microgels Are Controlled by Molecular Weight of Reactants

Published on: December 23, 2013

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

Ethylene Polymerizations Using Parallel Pressure Reactors and a Kinetic Analysis of Chain Transfer Polymerization
07:28

Ethylene Polymerizations Using Parallel Pressure Reactors and a Kinetic Analysis of Chain Transfer Polymerization

Published on: November 27, 2015

Area of Science:

  • Polymer Science and Engineering
  • Chemical Process Optimization
  • Industrial Catalysis

Background:

  • Melt Flow Index (MFI) stability is crucial for industrial polypropylene (PP) production, directly impacting molecular weight and processability.
  • While catalyst and hydrogen effects are known, the sources of MFI fluctuations under stable industrial conditions are poorly understood.
  • Investigating feedstock quality and operational variables is essential for controlling MFI variability in PP manufacturing.

Purpose of the Study:

  • To investigate the relationships between feedstock quality, process operations, and residual MFI variability in industrial gas-phase PP production.
  • To identify key operational variables influencing MFI fluctuations despite highly purified feedstocks and stable conditions.
  • To establish a framework for understanding and mitigating subtle polymer quality variations in stabilized production systems.

Main Methods:

  • Assembled a dataset of 61 industrial observations, integrating laboratory quality measurements with operational variables.
  • Quantified catalyst inhibitor concentrations before and after a modified zeolite purification system.
  • Applied Principal Component Analysis (PCA) and Variable Importance in Projection (VIP) analysis to identify dominant process variability domains and key influencing variables.

Main Results:

  • A modified zeolite purification system reduced catalyst poisons to ppb levels, achieving polymer-grade propylene purity >99.95 wt.%.
  • The production campaign showed high quality stability (average MFI = 3.03 g/10 min, CV = 6.63%).
  • PCA identified two main operational domains (hydrodynamics/thermal/fouling and catalyst/hydrogen) explaining 86.49% of variability.
  • Plate Fouling Factor (VIP=2.17), Production Rate (VIP=1.33), and H2/C3 Ratio (VIP=1.17) were most associated with residual MFI fluctuations.

Conclusions:

  • Residual MFI variability in highly stabilized PP production arises from complex interactions among hydrodynamic, thermal, and catalytic domains.
  • Effective feedstock purification minimizes variability, but operational factors remain critical for fine-tuning MFI.
  • The study provides insights into process-quality relationships and a framework for identifying operational origins of subtle quality variations.