Jump to main content
Jump to site search
PLANNED MAINTENANCE Close the message box

Scheduled maintenance work on Wednesday 27th March 2019 from 11:00 AM to 1:00 PM (GMT).

During this time our website performance may be temporarily affected. We apologise for any inconvenience this might cause and thank you for your patience.

Issue 19, 2018
Previous Article Next Article

High-throughput cell focusing and separation via acoustofluidic tweezers

Author affiliations


Separation of particles and cells is an important function in many biological and biomedical protocols. Although a variety of microfluidic-based techniques have been developed so far, there is clearly still a demand for a precise, fast, and biocompatible method for separation of microparticles and cells. By combining acoustics and hydrodynamics, we have developed a method which we integrated into three-dimensional acoustofluidic tweezers (3D-AFT) to rapidly and efficiently separate microparticles and cells into multiple high-purity fractions. Compared with other acoustophoresis methods, this 3D-AFT method significantly increases the throughput by an order of magnitude, is label-free and gently handles the sorted cells. We demonstrate not only the separation of 10, 12, and 15 micron particles at a throughput up to 500 μl min−1 using this 3D-AFT method, but also the separation of erythrocytes, leukocytes, and cancer cells. This 3D-AFT method is able to meet various separation demands thus offering a viable alternative with potential for clinical applications.

Graphical abstract: High-throughput cell focusing and separation via acoustofluidic tweezers

Back to tab navigation

Supplementary files

Publication details

The article was received on 26 Apr 2018, accepted on 10 Aug 2018 and first published on 22 Aug 2018

Article type: Paper
DOI: 10.1039/C8LC00434J
Citation: Lab Chip, 2018,18, 3003-3010

  •   Request permissions

    High-throughput cell focusing and separation via acoustofluidic tweezers

    M. Wu, K. Chen, S. Yang, Z. Wang, P. Huang, J. Mai, Z. Li and T. J. Huang, Lab Chip, 2018, 18, 3003
    DOI: 10.1039/C8LC00434J

Search articles by author