Evidence map›Paper›PMID 32127482›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Embedded droplet printing in yield-stress fluids.

Arif Z Nelson, Binu Kundukad, Wai Kuan Wong, Saif A Khan, Patrick S Doyle

Open access · hybridAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
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

18 citing papers in PubMed, 86 citations in OpenAlex.

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  18. On-demand modulation of 3D-printed elastomers using programmable droplet inclusions.Proceedings of the National Academy of Sciences of the United States of America · 2020
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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

5 authors at 2 institutions in 2 countries.

Arif Z NelsonBiological Systems and Micromechanics, Singapore-MIT Alliance for Research and Technology, 138602 Singapore, Singapore.ORCID 0000-0002-4539-0968
Binu KundukadBiological Systems and Micromechanics, Singapore-MIT Alliance for Research and Technology, 138602 Singapore, Singapore.
Wai Kuan WongDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 117585 Singapore, Singapore.
Saif A KhanCampus for Research Excellence and Technological Enterprise, 138602 Singapore, Singapore; saifkhan@nus.edu.sg pdoyle@mit.edu.
Patrick S DoyleBiological Systems and Micromechanics, Singapore-MIT Alliance for Research and Technology, 138602 Singapore, Singapore; saifkhan@nus.edu.sg pdoyle@mit.edu.ORCID 0000-0003-2147-9172
Singapore-MIT Alliance for Research and Technology · SGNational University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microfluidic tools and techniques for manipulating fluid droplets have become core to many scientific and technological fields. Despite the plethora of existing approaches to fluidic manipulation, non-Newtonian fluid phenomena are rarely taken advantage of. Here we introduce embedded droplet printing-a system and methods for the generation, trapping, and processing of fluid droplets within yield-stress fluids, materials that exhibit extreme shear thinning. This technique allows for the manipulation of droplets under conditions that are simply unattainable with conventional microfluidic methods, namely the elimination of exterior influences including convection and solid boundaries. Because of this, we believe embedded droplet printing approaches an ideal for the experimentation, processing, or observation of many samples in an "absolutely quiescent" state, while also removing some troublesome aspects of microfluidics including the use of surfactants and the complexity of device manufacturing. We characterize a model material system to understand the process of droplet generation inside yield-stress fluids and develop a nascent set of archetypal operations that can be performed with embedded droplet printing. With these principles and tools, we demonstrate the benefits and versatility of our method, applying it toward the diverse applications of pharmaceutical crystallization, microbatch chemical reactions, and biological assays.

Indexed as

complex fluidscrystallizationdropletsmicrofluidicsyield-stress fluids

Identifiers

PMID32127482
PMCPMC7084155
OpenAlexW3010142441

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.