Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by

Same author

ForestSAR-YOLO: real-time detection of small human targets in UAV imagery.

Scientific reports·2026

Same author

Assessing the onset of spring water-level rise in snowmelt-dominated rivers of northeastern Russia using machine learning.

Scientific reports·2026

Same author

Multi-objective optimization of a regional biogas supply chain using organic waste.

Scientific reports·2026

Same author

A review of applications of machine learning in quantum dots research.

Discover nano·2026

Same author

Dual-loop proportional control for high-precision induction brazing of thin-walled aluminum waveguides.

Scientific reports·2026

Same author

Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review.

Polymers·2025

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review.

Ivan Malashin¹, Dmitriy Martysyuk¹, Vadim Tynchenko¹

¹Bauman Moscow State Technical University, 105005 Moscow, Russia.

|December 17, 2024

Summary

This summary is machine-generated.

Machine learning (ML) optimizes biopolymer production by analyzing complex data for efficiency and quality. This review details ML applications in sustainable biopolymer manufacturing, addressing challenges from variable feedstocks.

Keywords:

ML biopolymers materials science process optimization

More Related Videos

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Published on: September 28, 2018

Related Experiment Videos

Last Updated: Jun 4, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Published on: September 28, 2018

Area of Science:

Materials Science
Chemical Engineering
Biotechnology

Background:

Biopolymers are sustainable alternatives to petrochemical plastics.
Biopolymer production faces challenges due to variable bio-based feedstocks and complex processing.
Machine learning (ML) offers advanced data analysis capabilities for manufacturing optimization.

Purpose of the Study:

To systematically review current ML applications in biopolymer production.
To provide a comprehensive reference for future research in ML for biopolymers.
To highlight ML's potential in enhancing biopolymer manufacturing efficiency, cost-effectiveness, and product quality.

Main Methods:

Review of scientific literature on ML applications in biopolymer manufacturing.
Categorization of ML techniques, including supervised, unsupervised, and deep learning.
Analysis of data patterns and insights from ML in biopolymer production stages.

Main Results:

ML techniques are applied across various stages of biopolymer production.
ML enables the analysis of complex production data, revealing insights beyond traditional methods.
ML algorithms can identify patterns to optimize processes, reduce costs, and improve quality.

Conclusions:

ML integration is crucial for advancing biopolymer production processes.
ML offers significant potential to overcome challenges in sustainable biopolymer manufacturing.
Future research should focus on leveraging diverse ML algorithms for enhanced biopolymer production.