Evidence map›Paper›PMID 33023173›Full record

ReviewMicromachines2020

Acoustic Microfluidic Separation Techniques and Bioapplications: A Review.

Yuan Gao, Mengren Wu, Yang Lin, Jie Xu

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 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

4 authors.

Yuan GaoDepartment of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
Mengren WuDepartment of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
Yang LinDepartment of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA.
Jie XuDepartment of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

Funding

NASA 80NSSC17K0522
6 · The paper itself

Abstract

Microfluidic separation technology has garnered significant attention over the past decade where particles are being separated at a micro/nanoscale in a rapid, low-cost, and simple manner. Amongst a myriad of separation technologies that have emerged thus far, acoustic microfluidic separation techniques are extremely apt to applications involving biological samples attributed to various advantages, including high controllability, biocompatibility, and non-invasive, label-free features. With that being said, downsides such as low throughput and dependence on external equipment still impede successful commercialization from laboratory-based prototypes. Here, we present a comprehensive review of recent advances in acoustic microfluidic separation techniques, along with exemplary applications. Specifically, an inclusive overview of fundamental theory and background is presented, then two sets of mechanisms underlying acoustic separation, bulk acoustic wave and surface acoustic wave, are introduced and discussed. Upon these summaries, we present a variety of applications based on acoustic separation. The primary focus is given to those associated with biological samples such as blood cells, cancer cells, proteins, bacteria, viruses, and DNA/RNA. Finally, we highlight the benefits and challenges behind burgeoning developments in the field and discuss the future perspectives and an outlook towards robust, integrated, and commercialized devices based on acoustic microfluidic separation.

Indexed as

acoustic separation techniquesBAWbioapplicationsmicrofluidicsSAW

Identifiers

PMID33023173
PMCPMC7600273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.