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Cited by 266

The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.

3D Printed Sensors for Biomedical Applications: A Review

Open access
Han, Tao, Kundu, Sudip, Nag, Anindya, Xu, Yongzhao·2019·Sensors

Abstract

This paper showcases a substantial review on some of the significant work done on 3D printing of sensors for biomedical applications. The importance of 3D printing techniques has bloomed in the sensing world due to their essential advantages of quick fabrication, easy accessibility, processing of varied materials and sustainability. Along with the introduction of the necessity and influence of 3D printing techniques for the fabrication of sensors for different healthcare applications, the paper explains the individual methodologies used to develop sensing prototypes. Six different 3D printing techniques have been explained in the manuscript, followed by drawing a comparison between them in terms of their advantages, disadvantages, materials being processed, resolution, repeatability, accuracy and applications. Finally, a conclusion of the paper is provided with some of the challenges of the current 3D printing techniques about the developed sensing prototypes, their corresponding remedial solutions and a market survey determining the expenditure on 3D printing for biomedical sensing prototypes.

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Crossref · 10.3390/s19071706

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Metadata

Authors
Han, T., Kundu, S., Nag, A., & Xu, Y.
Venue
Sensors
Publisher
MDPI AG
Published
2019-04-10
Volume
19
Issue
7
Pages
1706
ISSN
1424-8220 (ISSN-L)
OpenAlex
W2939978610
Cited by
266
References
118
FWCI
14.99
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: 3D Printed Sensors for Biomedical Applications: A Review. https://app.cochat.ai/papers/10.3390/s19071706
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Han, T., Kundu, S., Nag, A., & Xu, Y. (2019). 3D Printed Sensors for Biomedical Applications: A Review. Sensors. https://doi.org/10.3390/s19071706

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.