Evidence map›Paper›PMID 30181484›Full record

ArticleSensors (Basel, Switzerland)2018

Development of Coplanar Electro-Wetting Based Microfluidic Sorter to Select Micro-Particles in High Volume Throughput at Milliliter Amount within Twenty Minutes.

Yuanyu Chen, Shauchun Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

2 authors.

Yuanyu ChenDepartment of Chemistry and Biochemistry, National Chung Cheng University, Chia-Yi 621, Taiwan. curtis992250@gmail.com.ORCID 0000-0002-8589-9726
Shauchun WangDepartment of Chemistry and Biochemistry, National Chung Cheng University, Chia-Yi 621, Taiwan. chescw@ccu.edu.tw.

Funding

Ministry of Science and Technology, Taiwan 106-2113-M-194-005 and 103-2627-M-194-001
6 · The paper itself

Abstract

This paper reports the work of developing one coplanar microfluidic sorter while using the electro-wetting on dielectrics (EWOD) technique. When connected with delivery capillary to receive sample solution containing micro-particles, this device can select about 10 micro-particles in high volume throughput of milliliter amount within 20 min, to potentially match the requirement of efficiently determining the low amounts of bacteria in concentrated food and environmental samples, of which the typical bacteria density is 10 colony forming unit or less, much smaller than that of clinical pathogen samples. This coplanar T-shape EWOD device contains two fluidic channels, one inlet channel and the other collection channel stemmed from the middle of inlet channel. When the solution droplet falls from the delivery capillary to the entrance end of inlet channel, the droplet is driven to the intersection of two channels. The droplet containing fluorescent particle will be diverted to the lower channel to collect. Otherwise, the non-fluorescent droplet keeps moving toward the other end of inlet channel to waste zone. The particle fluorescence is collected through microscope lens to detect with one photomultiplier tube. The detected signals trigger the personal computer control board to active each EWOD electrode to direct the droplet moving directions. When the solution of 1 mL containing about 10 fluorescent micro-particles is delivered into this sorting device, nearly all the particles were correctly directed into collection zone in 20 min.

Indexed as

electrowetting-on-dielectricsfluorescence detectionfood safety pathogenmicrofluidics

Identifiers

PMID30181484
PMCPMC6164432

What OpenQuestion holds

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Registered trials

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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.