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Step-Growth Polymerization: Overview01:03

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.
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Preparation and Testing of Plant Seed Meal-based Wood Adhesives
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Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning.

Ye Cheng1, Takuma Araki2, Naofumi Kamimura3

  • 1Department of Materials Science and Engineering, Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan.

ACS Applied Materials & Interfaces
|February 28, 2025
PubMed
Summary
This summary is machine-generated.

Researchers optimized biomass-based adhesives using machine learning. They improved 2-pyrone-4,6-dicarboxylic acid (PDC)-based polyurethane (PU) adhesive strength to 10.04 MPa, accelerating new material development.

Keywords:
Bayesian optimizationadhesive strengthbiomassmachine learningpolyurethane

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Area of Science:

  • Polymer Chemistry
  • Materials Science
  • Biotechnology

Background:

  • 2-Pyrone-4,6-dicarboxylic acid (PDC) derived from lignin offers a sustainable route to biomass-based polymers.
  • PDC-based polyurethanes (PUs) exhibit promising adhesive properties, but require optimization for enhanced performance.

Purpose of the Study:

  • To improve the adhesive strength of biomass-based polyurethanes (PUs) using 2-pyrone-4,6-dicarboxylic acid (PDC).
  • To combine experimental approaches with machine learning (ML) for efficient optimization of adhesive properties.

Main Methods:

  • A Taguchi L25 orthogonal design was used to synthesize 25 adhesive samples with varying polyols, isocyanates, and ratios.
  • Adhesive strengths were measured after hot-pressing under diverse temperature and time conditions.
  • Gaussian process-based Bayesian optimization (BO) and Random Forest regression were employed for data analysis and optimization.

Main Results:

  • Bayesian optimization identified an optimal PDC-based PU adhesive formulation.
  • The optimized adhesive achieved a significantly improved strength of 10.04 ± 1.26 MPa within five iterations.
  • Random Forest regression validated the findings from Bayesian optimization.

Conclusions:

  • Bayesian optimization effectively guides experimental parameters for enhanced adhesive performance.
  • This ML-driven approach accelerates the development and optimization of novel biomass-based adhesive materials.
  • PDC-based PUs show potential as high-performance, sustainable adhesives.