Evidence map›Paper›PMID 36643460›Full record

ArticleACS omega2023

Optimization Analysis of Particle Separation Parameters for a Standing Surface Acoustic Wave Acoustofluidic Chip.

Junlong Han, Hong Hu, Yulin Lei, Qingyun Huang, Chen Fu, Chenhui Gai, Jia Ning

Abstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Junlong HanSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China.
Hong HuSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China.ORCID https://orcid.org/0000-0002-7443-0885
Yulin LeiSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China.
Qingyun HuangShenzhen Polytechnic, Shenzhen518055, China.
Chen FuCollege of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen518055, China.
Chenhui GaiSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China.
Jia NingSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microparticle separation technology is an important technology in many biomedical and chemical engineering applications from sample detection to disease diagnosis. Although a variety of microparticle separation techniques have been developed thus far, surface acoustic wave (SAW)-based microfluidic separation technology shows great potential because of its high throughput, high precision, and integration with polydimethylsiloxane (PDMS) microchannels. In this work, we demonstrate an acoustofluidic separation chip that includes a piezoelectric device that generates tilted-angle standing SAWs and a permanently bonded PDMS microchannel. We established a mathematical model of particle motion in the microchannel, simulated the particle trajectory through finite element simulation and numerical simulation, and then verified the validity of the model through acoustophoresis experiments. To improve the performance of the separation chip, the influences of particle size, flow rate, and input power on the particle deflection distance were studied. These parameters are closely related to the separation purity and separation efficiency. By optimizing the control parameters, the separation of micron and submicron particles under different throughput conditions was achieved. Moreover, the separation samples were quantitatively analyzed by digital light scattering technology and flow cytometry, and the results showed that the maximum purity of the separated particles was ∼95%, while the maximum efficiency was ∼97%.

Identifiers

PMID36643460
PMCPMC9835635

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.