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

Olefin Metathesis Polymerization: Overview01:13

Olefin Metathesis Polymerization: Overview

2.3K
Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists...
2.3K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.7K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
3.7K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.4K
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...
2.4K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.5K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
3.5K
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

2.7K
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...
2.7K
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

3.1K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Predicting the Effect of Mould Material and Design on the Efficiency of the LLDPE Rotational Moulding Process: Thermal and Time Analyses.

Materials (Basel, Switzerland)·2026
Same author

A Review on Injection Molding: Conformal Cooling Channels, Modelling, Surrogate Models and Multi-Objective Optimization.

Polymers·2025
Same author

Shape-Memory Polymers Based on Carbon Nanotube Composites.

Micromachines·2024
Same author

Soybean-Based Polyol as a Substitute of Fossil-Based Polyol on the Synthesis of Thermoplastic Polyurethanes: The Effect of Its Content on Morphological and Physicochemical Properties.

Polymers·2023
Same author

Biocompatible 3D-Printed Tendon/Ligament Scaffolds Based on Polylactic Acid/Graphite Nanoplatelet Composites.

Nanomaterials (Basel, Switzerland)·2023
Same author

Experimental Validation of a Micro-Extrusion Set-Up with In-Line Rheometry for the Production and Monitoring of Filaments for 3D-Printing.

Micromachines·2023

Related Experiment Video

Updated: Oct 3, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.8K

Optimization of Polymer Processing: A Review (Part II-Molding Technologies).

António Gaspar-Cunha1, José A Covas1, Janusz Sikora2

  • 1Institute of Polymers and Composites, University of Minho, Campus de Azurém, 4804-533 Guimarães, Portugal.

Materials (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

Optimization techniques enhance polymer processing, leading to material savings and improved product properties. Various methods, including artificial intelligence, offer successful solutions for injection molding, blow molding, and thermoforming.

Keywords:
artificial intelligenceblow moldinginjection moldingoptimizationpolymer processingsingle screwthermoformingtwin screw

More Related Videos

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication
07:01

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Published on: July 18, 2025

532
Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding
10:43

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Published on: June 27, 2014

20.1K

Related Experiment Videos

Last Updated: Oct 3, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
05:32

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars

Published on: August 4, 2018

12.8K
Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication
07:01

Improved Polydimethylsiloxane (PDMS) Double Casting via Silicone Oil Treatment for Densely Packed Microstructure Replication

Published on: July 18, 2025

532
Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding
10:43

Rapid and Low-cost Prototyping of Medical Devices Using 3D Printed Molds for Liquid Injection Molding

Published on: June 27, 2014

20.1K

Area of Science:

  • Polymer Science and Engineering
  • Manufacturing Optimization
  • Computational Materials Science

Background:

  • Polymer processing optimization offers significant material and energy savings.
  • Improved product properties and recycling are key benefits.
  • Optimization is crucial for efficient polymer manufacturing.

Purpose of the Study:

  • Review optimization techniques for polymer processing.
  • Identify key characteristics of optimization problems.
  • Discuss applications in injection molding, blow molding, and thermoforming.

Main Methods:

  • Analysis of objective functions, algorithms, and process modeling.
  • Review of optimization methodologies applied to polymer processing.
  • Exploration of artificial intelligence approaches.

Main Results:

  • Various optimization methods yield good results with low computational cost.
  • Artificial intelligence approaches demonstrate significant success.
  • Optimization is applicable to diverse polymer processing technologies.

Conclusions:

  • Optimization techniques are highly effective for polymer processing.
  • Successful application across various molding and forming technologies.
  • Further research can enhance efficiency and sustainability in polymer manufacturing.