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Molecular Weight of Step-Growth Polymers

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...

Polymers: Molecular Weight Distribution

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.

Step-Growth Polymerization: Overview

Step-Growth Polymerization: Overview

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...

Polymers

Polymers

The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...

Cationic Chain-Growth Polymerization: Mechanism

Cationic Chain-Growth Polymerization: Mechanism

The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...

Ziegler–Natta Chain-Growth Polymerization: Overview

Ziegler–Natta Chain-Growth Polymerization: Overview

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...

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Updated: Aug 8, 2025

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Published on: September 6, 2012

Data-Driven Methods for Accelerating Polymer Design.

Tarak K Patra¹

¹Department of Chemical Engineering, Center for Atomistic Modeling and Materials Design and Center for Carbon Capture Utilization and Storage, Indian Institute of Technology Madras, Chennai, TN 600036, India.

ACS Polymers Au

|March 1, 2023

Summary

This summary is machine-generated.

Data-driven methods, including machine learning, are revolutionizing polymer design by navigating vast chemical spaces. These approaches accelerate the discovery of novel polymers with superior properties.

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Area of Science:

Polymer Science
Materials Science
Computational Chemistry

Background:

Polymer design is complex due to vast chemical and configurational spaces.
Advances in computation, machine learning, and data availability offer solutions.

Purpose of the Study:

To review data-driven methods for polymer design.
To discuss their principles, historical context, and future scope.
To highlight their role in discovering new polymers and advancing fundamental understanding.

Main Methods:

Review of data-driven methodologies in polymer research.
Discussion of machine learning and computational characterization techniques.
Presentation of case studies demonstrating practical applications.

Main Results:

Data-driven strategies facilitate the discovery of polymers with exceptional properties.
These methods help establish new correlations and deepen the fundamental understanding of polymers.
The integration of these approaches promises to transform polymer research and development.

Conclusions:

The synergy of machine learning, rapid computational polymer characterization, and open-sourced data will significantly advance polymer science.
Data-driven methods are essential tools for future polymer research and education.
This review serves as a reference for researchers implementing data-driven strategies.