ArticleACS biomaterials science & engineering2023
Modeling a Dynamic Printability Window on Polysaccharide Blend Inks for Extrusion Bioprinting.
Article in ACS biomaterials science & engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MXene Bioinks for 3D Bioprinting: Design and Translation.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Biofabricated Alginate Hydrogels to Study Prostate Tumoral Microenvironments In Vitro.ACS omega · 2026Article
- Volumetric Control vs. Pneumatic Pressure: A Comparative Analysis of Extrusion in 3D Bioprinting.Micromachines · 2026Article
- Synthesis and Characterization of a Marine Collagen-Chitosan/HA-SiOGels (Basel, Switzerland) · 2026Article
- 3D Bioprinted Natural Hydrogels: Rheological Characterization, Cytotoxicity, and Printability Assessment of a Polysaccharide-Based Bioink.ACS omega · 2026Article
- An asymptotic slender-body model for predicting print resolution in extrusion-based bioprinting.Journal of engineering mathematics · 2026Article
- Geometrical constraints dictate assembly and phenotype of human iPSC-derived motoneuronal spheroids.Stem cell research & therapy · 2025Article
- Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting.Polymers · 2025Review
- Enhanced Bioprinting of 3D Corneal Stroma Patches with Reliability, Assessing Product Consistency and Quality through Optimized Electron Beam Sterilization.Advanced healthcare materials · 2025Article
- Bioprinting vascularized skin analogs: a stepwise approach.Burns & trauma · 2025Review
- Silk fibroin-based inks forBioactive materials · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extrusion-based bioprinting is one of the most widespread technologies due to its affordability, wide range of processable materials, and ease of use. However, the formulation of new inks for this technique is based on time-consuming trial-and-error processes to establish the optimal ink composition and printing parameters. Here, a dynamic printability window was modeled for the assessment of the printability of polysaccharide blend inks of alginate and hyaluronic acid with the intent to build a versatile predictive tool to speed up the testing procedures. The model considers both the rheological properties of the blends (viscosity, shear thinning behavior, and viscoelasticity) and their printability (in terms of extrudability and the ability of forming a well-defined filament and detailed geometries). By imposing some conditions on the model equations, it was possible to define empirical bands in which the printability is ensured. The predictive capability of the built model was successfully verified on an untested blend of alginate and hyaluronic acid chosen to simultaneously optimize the printability index and minimize the size of the deposited filament.
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