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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...

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Related Experiment Video

Updated: Aug 29, 2025

Fabrication of Large-area Free-standing Ultrathin Polymer Films

Fabrication of Large-area Free-standing Ultrathin Polymer Films

Published on: June 3, 2015

A small-data-driven model for predicting adsorption properties in polymeric thin films.

Uiyoung Han¹, Taegyu Kang², Jongho Im²

¹Department of Chemical & Biomolecular Engineering, College of Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea. jinkee.hong@yonsei.ac.kr.

Chemical Communications (Cambridge, England)

|September 9, 2022

Summary

This summary is machine-generated.

We developed a new method using artificial intelligence to predict polymer properties from adsorption data, avoiding large datasets. This advances material science by analyzing causal relationships for accurate predictions.

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Area of Science:

Material Science
Polymer Science
Computational Chemistry

Background:

Artificial intelligence (AI) is increasingly used for predicting material properties.
Traditional methods often require large datasets, which can be challenging to acquire for polymers.

Purpose of the Study:

To develop a novel AI-driven methodology for predicting polymer physicochemical properties.
To utilize polymer adsorption data as a predictive basis, bypassing the need for extensive polymer datasets.

Main Methods:

Developed a methodology analyzing causal relationships between polymer properties and experimental adsorption data.
Employed AI techniques to establish predictive models based on adsorption characteristics.

Main Results:

Successfully demonstrated the use of polymer adsorption data for property prediction.
Established a causal analysis framework for AI-driven material property prediction.

Conclusions:

The proposed AI methodology offers an efficient alternative to traditional big data approaches in polymer science.
This work facilitates data-driven prediction of polymer properties, accelerating material discovery and development.