Evidence map›Paper›PMID 32164167›Full record

ArticleMicromachines2020

A Microfluidic Concentration Gradient Maker with Tunable Concentration Profiles by Changing Feed Flow Rate Ratios.

Tao Zhang, Jiyu Meng, Shanshan Li, Chengzhuang Yu, Junwei Li, Chunyang Wei, Shijie Dai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2020. 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
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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.

Tao ZhangHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.
Jiyu MengHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.
Shanshan LiHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.ORCID 0000-0001-8293-9676
Chengzhuang YuHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.
Junwei LiDepartment of Computer Science and Electrical Engineering, Hebei University of Technology, Langfang 065000, China.
Chunyang WeiHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.
Shijie DaiHebei Key Laboratory of Robotic Sensing and Human-robot interactions, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300132, China.

Funding

China Postdoctoral Science Foundation 2015M580190China Postdoctoral Science Foundation 2017T100149Fund for Distinguish Young Scholars in Tianjin 2018 3rd Round 180191Hebei Science and Technology Foundation 19271707DHebei Science and Technology Foundation C2017202208National Natural Science Foundation of China 51505123National Natural Science Foundation of China 51728502Scientific Innovation Grant for Excellent Young Scientists of HEBUT 2015010
6 · The paper itself

Abstract

Microfluidic chips-in which chemical or biological fluid samples are mixed into linear or nonlinear concentration distribution profiles-have generated enormous enthusiasm of their ability to develop patterns for drug release and their potential toxicology applications. These microfluidic devices have untapped potential for varying concentration patterns by the use of one single device or by easy-to-operate procedures. To address this challenge, we developed a soft-lithography-fabricated microfluidic platform that enabled one single device to be used as a concentration maker, which could generate linear, bell-type, or even S-type concentration profiles by tuning the feed flow rate ratios of each independent inlet. Here, we present an FFRR (feed flow rate ratio) adjustment approach to generate tens of types of concentration gradient profiles with one single device. To demonstrate the advantages of this approach, we used a Christmas-tree-like microfluidic chip as the demo. Its performance was analyzed using numerical simulation models and experimental investigations, and it showed an excellent time response (~10 s). With on-demand flow rate ratios, the FFRR microfluidic device could be used for many lab-on-a-chip applications where flexible concentration profiles are required for analysis.

Indexed as

concentration gradientfeed flow rate ratios mixingmicrofluidicsnonlinear

Identifiers

PMID32164167
PMCPMC7142998

What OpenQuestion holds

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LicenceCC BY
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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.