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Classification and Mechanical Properties of Synthetic Polymers

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...

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.

Olefin Metathesis Polymerization: Overview

Olefin Metathesis Polymerization: Overview

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 of a...

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 properties that they exhibit. Additionally,...

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 properties that they exhibit. Additionally,...

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 properties that they exhibit. Additionally,...

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Related Experiment Video

Updated: Jul 2, 2026

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

Pore-permeability evolution in fuel-related copolymers via machine learning.

Mingkun Pang¹, Jiadong Bai², Hongyu Pan¹

¹College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Scientific Reports

|July 1, 2026

Summary

This summary is machine-generated.

This study develops a machine learning framework to link copolymer nanoporosity to fuel membrane permeability. The model predicts material performance for fuel separation and energy systems.

Keywords:

Conformational relationship Gray-scale processing Molecular structure optimization Pore–permeability Random copolymers

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Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Published on: July 1, 2013

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Last Updated: Jul 2, 2026

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Multi-Scale Modification of Metallic Implants With Pore Gradients, Polyelectrolytes and Their Indirect Monitoring In vivo

Published on: July 1, 2013

Area of Science:

Materials Science
Computational Chemistry
Machine Learning

Background:

Fuel-related copolymers possess controllable nanopore structures crucial for separation and energy applications.
Understanding the relationship between nanoscale porosity and macroscopic permeability is vital for material design.

Purpose of the Study:

To establish a machine learning-enhanced computational framework for decoding the pore-permeability relationship in P(AA₀.₃-r-St₀.₇)₁₈₅ random copolymers.
To quantitatively correlate nanoscale porosity features with macroscopic transport properties for fuel-related applications.

Main Methods:

Multi-scale modeling combining Materials Studio-based conformational sampling (Markov chain Monte Carlo) and CNN-assisted grayscale analysis of simulated pore structures.
Utilized a Convolutional Neural Network (CNN) as an auxiliary tool for efficient porosity extraction from grayscale images.
Applied the Kozeny-Carman equation to analyze the porosity-permeability correlation.

Main Results:

A bifurcated linear correlation between porosity and permeability was identified, with an inflection point at 0.131 porosity.
Microstructural analysis revealed pore network reorganization as the cause for the transition in permeability evolution.
The hybrid computational approach demonstrated robustness and validated against experimental trends.

Conclusions:

The developed framework accurately captures molecular dynamics and mesoscale microstructure-transport coupling.
Provides a predictive tool for designing functional copolymer membranes for fuel processing and energy storage.
Potential for extension to other porous material systems via transfer learning.