scholarly journals Contactless microparticle control via ultrahigh frequency needle type single beam acoustic tweezers

2016 ◽  
Vol 109 (17) ◽  
pp. 173509 ◽  
Author(s):  
Chunlong Fei ◽  
Ying Li ◽  
Benpeng Zhu ◽  
Chi Tat Chiu ◽  
Zeyu Chen ◽  
...  
2017 ◽  
Vol 114 (11) ◽  
pp. 2637-2647 ◽  
Author(s):  
Xiaoyang Chen ◽  
Kwok Ho Lam ◽  
Ruimin Chen ◽  
Zeyu Chen ◽  
Ping Yu ◽  
...  

2013 ◽  
Vol 9 (4) ◽  
pp. 10 ◽  
Author(s):  
Ying Li ◽  
Jae Youn Hwang ◽  
K. Kirk Shung ◽  
Jungwoo Lee

AIP Advances ◽  
2016 ◽  
Vol 6 (3) ◽  
pp. 035102 ◽  
Author(s):  
Benpeng Zhu ◽  
Jiong Xu ◽  
Ying Li ◽  
Tian Wang ◽  
Ke Xiong ◽  
...  

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Junfei Li ◽  
Alexandru Crivoi ◽  
Xiuyuan Peng ◽  
Lu Shen ◽  
Yunjiao Pu ◽  
...  

AbstractAcoustic tweezers use ultrasound for contact-free manipulation of particles from millimeter to sub-micrometer scale. Particle trapping is usually associated with either radiation forces or acoustic streaming fields. Acoustic tweezers based on single-beam focused acoustic vortices have attracted considerable attention due to their selective trapping capability, but have proven difficult to use for three-dimensional (3D) trapping without a complex transducer array and significant constraints on the trapped particle properties. Here we demonstrate a 3D acoustic tweezer in fluids that uses a single transducer and combines the radiation force for trapping in two dimensions with the streaming force to provide levitation in the third dimension. The idea is demonstrated in both simulation and experiments operating at 500 kHz, and the achieved levitation force reaches three orders of magnitude larger than for previous 3D trapping. This hybrid acoustic tweezer that integrates acoustic streaming adds an additional twist to the approach and expands the range of particles that can be manipulated.


2014 ◽  
Vol 105 (17) ◽  
pp. 173701 ◽  
Author(s):  
Ying Li ◽  
Changyang Lee ◽  
Ruimin Chen ◽  
Qifa Zhou ◽  
K. Kirk Shung

Author(s):  
Hae Gyun Lim ◽  
Ying Li ◽  
Ming-Yi Lin ◽  
Changhan Yoon ◽  
Changyang Lee ◽  
...  

2021 ◽  
Author(s):  
Junfei Li ◽  
Alexandru Crivoi ◽  
Xiuyuan Peng ◽  
Lu Shen ◽  
Yunjiao Pu ◽  
...  

Abstract Acoustic tweezers use ultrasound for contact-free manipulation of particles from millimeter to sub-micrometer scale. Particle trapping originated in either radiation forces or acoustic streaming fields. Acoustic tweezers based on single-beam focused acoustic vortices have attracted considerable attention due to their selective trapping capability, but have proven difficult to use for 3D trapping without a complex transducer array and significant constraints on the trapped particle properties. Here we demonstrate the first 3D acoustic tweezer that uses a single transducer and combines the radiation force for trapping in two dimensions with the streaming force to provide levitation in the third dimension. The idea is demonstrated in both simulation and experiments, and the achieved levitation force reaches three orders of magnitude larger than for previous 3D trapping. This hybrid acoustic tweezer that integrates acoustic streaming adds a new twist to the approach and expands the range of particles that can be manipulated.


Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1212
Author(s):  
Hae Gyun Lim ◽  
O-Joun Lee ◽  
K. Kirk Shung ◽  
Jin-Taek Kim ◽  
Hyung Ham Kim

Single-beam acoustic tweezers (SBAT) is a widely used trapping technique to manipulate microscopic particles or cells. Recently, the characterization of a single cancer cell using high-frequency (>30 MHz) SBAT has been reported to determine its invasiveness and metastatic potential. Investigation of cell elasticity and invasiveness is based on the deformability of cells under SBAT’s radiation forces, and in general, more physically deformed cells exhibit higher levels of invasiveness and therefore higher metastatic potential. However, previous imaging analysis to determine substantial differences in cell deformation, where the SBAT is turned ON or OFF, relies on the subjective observation that may vary and requires follow-up evaluations from experts. In this study, we propose an automatic and reliable cancer cell classification method based on SBAT and a convolutional neural network (CNN), which provides objective and accurate quantitative measurement results. We used a custom-designed 50 MHz SBAT transducer to obtain a series of images of deformed human breast cancer cells. CNN-based classification methods with data augmentation applied to collected images determined and validated the metastatic potential of cancer cells. As a result, with the selected optimizers, precision, and recall of the model were found to be greater than 0.95, which highly validates the classification performance of our integrated method. CNN-guided cancer cell deformation analysis using SBAT may be a promising alternative to current histological image analysis, and this pretrained model will significantly reduce the evaluation time for a larger population of cells.


2012 ◽  
Vol 110 (3) ◽  
pp. 881-886 ◽  
Author(s):  
Kwok Ho Lam ◽  
Hsiu-Sheng Hsu ◽  
Ying Li ◽  
Changyang Lee ◽  
Anderson Lin ◽  
...  

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