Evidence map›Paper›PMID 37512600›Full record

ArticleMicromachines2023

Silicon-Based 3D Microfluidics for Parallelization of Droplet Generation.

Diego Monserrat Lopez, Philipp Rottmann, Martin Fussenegger, Emanuel Lörtscher

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Diego Monserrat LopezIBM Research Europe-Zurich, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland.
Philipp RottmannDepartment of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, CH-4058 Basel, Switzerland.
Martin FusseneggerDepartment of Biosystems Science and Engineering, ETH Zürich, Mattenstrasse 26, CH-4058 Basel, Switzerland.
Emanuel LörtscherIBM Research Europe-Zurich, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland.ORCID 0000-0002-5671-5051

Funding

Swiss National Science Foundation 51NF40-182895
6 · The paper itself

Abstract

Both the diversity and complexity of microfluidic systems have experienced a tremendous progress over the last decades, enabled by new materials, novel device concepts and innovative fabrication routes. In particular the subfield of high-throughput screening, used for biochemical, genetic and pharmacological samples, has extensively emerged from developments in droplet microfluidics. More recently, new 3D device architectures enabled either by stacking layers of PDMS or by direct 3D-printing have gained enormous attention for applications in chemical synthesis or biomedical assays. While the first microfluidic devices were based on silicon and glass structures, those materials have not yet been significantly expanded towards 3D despite their high chemical compatibility, mechanical strength or mass-production potential. In our work, we present a generic fabrication route based on the implementation of vertical vias and a redistribution layer to create glass-silicon-glass 3D microfluidic structures. It is used to build different droplet-generating devices with several flow-focusing junctions in parallel, all fed from a single source. We study the effect of having several of these junctions in parallel by varying the flow conditions of both the continuous and the dispersed phases. We demonstrate that the generic concept enables an upscaling in the production rate by increasing the number of droplet generators per device without sacrificing the monodispersity of the droplets.

Indexed as

droplet generationsiliconthree-dimensional microfluidicsupscaling

Identifiers

PMID37512600
PMCPMC10386391

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.