AccScience Publishing / IJB / Online First / DOI: 10.36922/ijb.2135
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Optimizing cell deposition for inkjet-based bioprinting

Wei Long Ng1* Viktor Shkolnikov2*
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1 Singapore Centre for 3D Printing (SC3DP), School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
2 HP Inc., 1501 Page Mill Road, Palo Alto, California, United States of America
Submitted: 29 October 2023 | Accepted: 2 January 2024 | Published: 5 February 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Although inkjet-based bioprinting enables precise drop-on-demand cell deposition within three-dimensional (3D) tissue constructs and facilitates critical cell–cell and cell–matrix interactions, it faces challenges such as poor cell homogeneity and low cell viability. To date, there is a lack of comprehensive review papers addressing the optimization of cell deposition in inkjet-based bioprinting. This review aims to fill that gap by providing an overview of various critical aspects in bioprinting, ranging from bio-ink properties to the impact of printed droplets. The bio-ink section begins by exploring how cells influence the physical properties of bio-inks and emphasizes the significance of achieving cell homogeneity within bio-inks to ensure consistent and reliable printing. The discussion then delves into inkjet-based printing chambers (thermal and piezoelectric), the effect of shear stress on printed cells, droplet formation dynamics, the influence of polymer-based and cell-laden droplets on the underlying substrate surface, and the dynamics of droplet impact. Beyond droplet formation and impact, the review highlights the importance of biophysical and biological cues within 3D hydrogel matrices for cell proliferation and differentiation. Finally, the paper highlights current and potential applications, with a specific focus on skin and lung tissue engineering using inkjet-based bioprinting techniques, and provides insights into the emerging role of machine learning in optimizing the cell deposition process for inkjet-based bioprinting.

3D bioprinting
Inkjet bioprinting
Machine learning
This work is supported and funded by NTU Presidential Postdoctoral Fellowship and HP Inc.
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Conflict of interest
Viktor Shkolnikov is an employee of HP Inc. and may own stock as part of the standard compensation package.
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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing