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Design-for-testability for paper-based digital microfluidic biochips

2017 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), 2017
Microfluidic biochips have recently emerged as promising solution for biochemical bioassay, which can be classified into two main categories: flow-based microfluidic biochips [1] and digital microfluidic biochips (DMFBs) [2]. However, both of them have to be fabricated in factories with specific equipment, which makes biochips expensive and inflexible.
Jain-De Li   +3 more
openaire   +1 more source

Paper-based Microfluidics for Tissue Engineering and Regenerative Medicine

Cells Tissues Organs
Background: Paper-based microfluidics have gained significant attention as cost-effective and biocompatible platforms for various biological and medical applications. These devices facilitate the replication of complex tissue environments and offer a versatile alternative to traditional microfluidic systems.
Jaehun, Lee   +6 more
openaire   +2 more sources

Microfluidic paper-based devices

2021
Akbar Hasanzadeh, Iman Hashemzadeh
openaire   +1 more source

Enhanced Watermarking for Paper-Based Digital Microfluidic Biochips

ACM Transactions on Design Automation of Electronic Systems
Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, the PB-DMFB manufacturing process may suffer from potential security threats. For example, a Trojan insertion attack may affect the functionality of PB-DMFBs.
Jian-De Li   +3 more
openaire   +1 more source

Recent Developments in Paper-Based Microfluidic Devices

Analytical Chemistry, 2014
David M, Cate   +3 more
openaire   +2 more sources

Fabrication of paper-based microfluidic device.

2013
​Master of Science (Biomedical Engineering)
openaire   +1 more source

Lipid in Chips: A Brief Review of Liposomes Formation by Microfluidics

International Journal of Nanomedicine, 2021
Jiaming Sun, Guo Zhang, Guo Zhang
exaly  

Exploiting machine learning for bestowing intelligence to microfluidics

Biosensors and Bioelectronics, 2021
Yuxin Zhang, Shiyang Tang, Jeeson Kim
exaly  

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