Evidence map›Paper›PMID 35548132›Full record

ArticleRSC advances2018

Numerical simulations of wall contact angle effects on droplet size during step emulsification.

Meng Wang, Chuang Kong, Qisen Liang, Jianxiang Zhao, Maolin Wen, Zhongbin Xu, Xiaodong Ruan

Abstract read
In one paragraph

Article in RSC advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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.

Meng WangInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.ORCID https://orcid.org/0000-0002-8283-5508
Chuang KongInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.
Qisen LiangInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.
Jianxiang ZhaoInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.
Maolin WenInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.
Zhongbin XuInstitute of Process Equipment, College of Energy Engineering, Zhejiang University Zheda Road No.38 Hangzhou 310027 China xuzhongbin@zju.edu.cn.ORCID https://orcid.org/0000-0001-7225-8420
Xiaodong RuanThe State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University Hangzhou 310007 China xdruan@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Terrace-based microfluidic devices are currently used to prepare highly monodisperse micro-droplets. Droplets are generated due to the spontaneous pressure drop induced by the Laplace pressure, and so the flow rate of a dispersed phase has little effect on droplet size. As a result, control over the droplet is limited once a step emulsification device has been fabricated. In this work, a terrace model was established to study the effect of the wall contact angle on droplet size based on computational fluid dynamics simulations. The results for contact angles from 140° to 180° show that a lower contact angle induces wall-wetting, increasing the droplet size. The Laplace pressure equations for droplet generation were determined based on combining pressure change curves with theoretical analyses, to provide a theoretical basis for controlling and handling droplets generated through step emulsification.

Identifiers

PMID35548132
PMCPMC9086337

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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.