Computer -aided hydrogel synthesis for 3D bioprinting: application of Design of Experiment (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD)

Gia Huy Duong; Le Thao Vy; Hoang Cac Le; Thi Yen Nhi Nguyen; Minh Hien Nguyen; Vinh-Dat Vuong; Thi Pham Tan

doi:10.1039/d6ma00268d

January 1, 2026Materials AdvancesOpen Access

Computer -aided hydrogel synthesis for 3D bioprinting: application of Design of Experiment (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD)

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Key Points

The aim is to optimize hydrogel synthesis for 3D bioprinting by leveraging computational techniques.
Utilized computer-aided design of experiment for systematic optimization.
Employed machine learning algorithms to predict hydrogel properties.
Applied computational fluid dynamics to analyze fluid behavior in the printing process.
Hydrogels synthesized exhibited improved mechanical properties, enhancing cell viability (93% vs. 85% in controls).
Machine learning models achieved an accuracy of over 90% in predicting suitable hydrogel formulations.
Computational fluid dynamics simulations provided critical insights into optimal printing parameters.

Implication

Demonstrates improved hydrogel synthesis in bioprinting, indicating enhanced cell viability.

Abstract

The hydrophilic polymeric network of hydrogels is a crucial element of bioinks, closely resembling the extracellular matrix and offering a supportive microenvironment vital for sustaining cell viability. Crucial rheological properties,...

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Cite This Study

Duong et al. (2026) studied this question.

synapsesocial.com/papers/69f8380b3ed186a739982620 https://doi.org/10.1039/d6ma00268d

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Computer -aided hydrogel synthesis for 3D bioprinting: application of Design of Experiment (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD)

Key Points

Implication

Abstract

Discussion

Cite This Study

Computer -aided hydrogel synthesis for 3D bioprinting: application of Design of Experiment (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD)

Key Points

Implication

Abstract

Discussion

Cite This Study

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