Skip to main content

Research Repository

Advanced Search

High-throughput characterization of fluid properties to predict droplet ejection for three-dimensional inkjet printing formulations

Zhou, Zuoxin; Ruiz Cantu, Laura; Chen, Xuesheng; Alexander, Morgan R.; Roberts, Clive J.; Hague, Richard; Tuck, Christopher; Irvine, Derek; Wildman, Ricky

High-throughput characterization of fluid properties to predict droplet ejection for three-dimensional inkjet printing formulations Thumbnail


Authors

Zuoxin Zhou

Laura Ruiz Cantu

Xuesheng Chen

Profile Image

MORGAN ALEXANDER MORGAN.ALEXANDER@NOTTINGHAM.AC.UK
Professor of Biomedical Surfaces

RICHARD HAGUE RICHARD.HAGUE@NOTTINGHAM.AC.UK
Professor of Additive Manufacturing

CHRISTOPHER TUCK CHRISTOPHER.TUCK@NOTTINGHAM.AC.UK
Professor of Materials Engineering

DEREK IRVINE derek.irvine@nottingham.ac.uk
Professor of Materials Chemistry

RICKY WILDMAN RICKY.WILDMAN@NOTTINGHAM.AC.UK
Professor of Multiphase Flow and Mechanics



Abstract

Inkjet printing has been used as an Additive Manufacturing (AM) method to fabricate three-dimensional (3D) structures. However, a lack of materials suitable for inkjet printing poses one of the key challenges that impedes industry from fully adopting this technology. Consequently, many industry sectors are required to spend significant time and resources on formulating new materials for an AM process, instead of focusing on product development. To achieve the spatially controlled deposition of a printed voxel in a predictable and repeatable fashion, a combination of the physical properties of the ‘ink’ material, print head design, and processing parameters is associated. This study demonstrates the expedited formulation of new inks through the adoption of a high-throughput screening (HTS) approach. Use of a liquid handler containing multi-pipette heads, to rapidly prepare inkjet formulations in a micro-array format, and subsequently measure the viscosity and surface tension for each in a high-throughput manner is reported. This automatic approach is estimated to be 15 times more rapid than conventional methods. The throughput is 96 formulations per 13.1 working hours, including sample preparation and subsequent printability determination. The HTS technique was validated by comparison with conventional viscosity and surface tension measurements, as well as the observation of droplet ejection during inkjet printing processes. Using this approach, a library of 96 acrylate/methacrylate materials was screened to identify the printability of each formulation at different processing temperatures. The methodology and the material database established using this HTS technique will allow academic and industrial users to rapidly select the most ideal formulation to deliver printability and a predicted processing window for a chosen application.

Citation

Zhou, Z., Ruiz Cantu, L., Chen, X., Alexander, M. R., Roberts, C. J., Hague, R., …Wildman, R. (2019). High-throughput characterization of fluid properties to predict droplet ejection for three-dimensional inkjet printing formulations. Additive Manufacturing, 29, Article 100792. https://doi.org/10.1016/j.addma.2019.100792

Journal Article Type Article
Acceptance Date Jul 8, 2019
Online Publication Date Jul 12, 2019
Publication Date 2019-10
Deposit Date Jul 29, 2019
Publicly Available Date Mar 29, 2024
Journal Additive Manufacturing
Print ISSN 2214-8604
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 29
Article Number 100792
DOI https://doi.org/10.1016/j.addma.2019.100792
Keywords High-throughput screening; Additive manufacturing; 3D printing; Inkjet printing; Viscosity; Surface tension; Liquid handler
Public URL https://nottingham-repository.worktribe.com/output/2351220
Publisher URL https://www.sciencedirect.com/science/article/pii/S2214860419300843?via%3Dihub
Additional Information This article is maintained by: Elsevier; Article Title: High-throughput characterization of fluid properties to predict droplet ejection for three-dimensional inkjet printing formulations; Journal Title: Additive Manufacturing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.addma.2019.100792; Content Type: article; Copyright: © 2019 The Authors. Published by Elsevier B.V.

Files




You might also like



Downloadable Citations