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Related Concept Videos

Bioplastics01:27

Bioplastics

Bioplastics derived from microbial processes present a sustainable alternative to conventional petroleum-based plastics. Among these, polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrates (PHBs), have emerged as prominent candidates due to their biodegradability and biocompatibility. These polymers are synthesized by a variety of bacteria, such as Cupriavidus necator and Pseudomonas putida, which naturally accumulate PHAs as intracellular carbon and energy reserves, especially under...

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Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.

Zhizhou Zhang1,2, Zach Z Tao1, Ruiling Du3

  • 1School of Engineering, The University of Manchester, Manchester M13 9PL, UK.

Gels (Basel, Switzerland)
|December 24, 2025
PubMed
Summary

Artificial intelligence (AI) enhances hydrogel manufacturing by linking material composition to printability. AI-driven control improves the precision and viability of 3D printed soft materials for diverse applications.

Keywords:
additive manufacturingbiomaterialshydrogelmachine learningmaterial design

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Area of Science:

  • Materials Science and Engineering
  • Biotechnology
  • Robotics

Background:

  • Additive manufacturing of hydrogels is crucial for soft tissue engineering, biointerfaces, and soft robotics.
  • Current limitations arise from the complex interplay between formulation, rheology, and processing conditions.

Purpose of the Study:

  • To review how artificial intelligence (AI) integrates formulation, rheology, and process control for advanced hydrogel manufacturing.
  • To establish a framework for predictive and reproducible hydrogel design and fabrication.

Main Methods:

  • AI-driven analysis linking hydrogel composition to printability across various 3D printing techniques (DIW, inkjet, VPP, LIFT).
  • Application of machine learning models for rheology prediction, droplet regime classification, and optical dose optimization.
  • Utilizing polymer informatics (e.g., BigSMILES) for precursor screening and Bayesian optimization for experimental efficiency.

Main Results:

  • AI models predict hydrogel strand stability from rheological data and optimize printing parameters for enhanced accuracy and viability.
  • Generative screening of precursors and crosslinkers is accelerated through polymer informatics.
  • Autonomous platforms with in situ sensing enable rapid iteration and closed-loop control.

Conclusions:

  • AI provides quantitative pathways from hydrogel formulation to functional performance, enabling predictive manufacturing.
  • This approach facilitates application-oriented design and reproducible fabrication of complex hydrogel structures.
  • A strategic progression towards real-time correction and predictive fault prevention using digital twins is outlined.