Literature DB >> 32483390

μdroPi: A Hand-Held Microfluidic Droplet Imager and Analyzer Built on Raspberry Pi.

Meng Sun1, Zhengda Li1, Qiong Yang1.   

Abstract

We built a low-cost and hand-held device to image and analyze microfluidic droplets mainly for educational/teaching purposes in laboratory settings of universities. The device was assembled based on a Raspberry Pi with a camera attached on the back and an LCD screen on the top. We evaluated the performance of this device to capture images and videos to visualize high-throughput droplet generation in a microfluidic device. The qualities of imaging resolution and speed were sufficient for us to perform subsequent droplet analysis quantitatively through automatic image possessing. Droplet characteristics including droplet size, volume, and dispersity, as well as droplet intensity, have been measured, showing the potential of this device to analyze droplet-based assays. Most importantly, in addition to learning the knowledge and principles from classroom lectures, students can thus gain practice of using an advanced, state-of-the-art technology in a laboratory course. It will also open up opportunities to train students with skills of interdisciplinary thinking and learning.

Entities:  

Keywords:  Analytical Chemistry; Graduate Education/Research; Hands-On Learning/Manipulatives; Interdisciplinary/Multidisciplinary; Laboratory Equipment/Apparatus; Microscale Lab; Upper-Division Undergraduate

Year:  2019        PMID: 32483390      PMCID: PMC7263740          DOI: 10.1021/acs.jchemed.8b00975

Source DB:  PubMed          Journal:  J Chem Educ        ISSN: 0021-9584            Impact factor:   2.979


  19 in total

1.  High-throughput sample introduction for droplet-based screening with an on-chip integrated sampling probe and slotted-vial array.

Authors:  Meng Sun; Qun Fang
Journal:  Lab Chip       Date:  2010-08-16       Impact factor: 6.799

2.  Universal electronics for miniature and automated chemical assays.

Authors:  Pawel L Urban
Journal:  Analyst       Date:  2015-02-21       Impact factor: 4.616

3.  An Inexpensive, Open-Source USB Arduino Data Acquisition Device for Chemical Instrumentation.

Authors:  James P Grinias; Jason T Whitfield; Erik D Guetschow; Robert T Kennedy
Journal:  J Chem Educ       Date:  2016-06-22       Impact factor: 2.979

4.  Prototyping Instruments for the Chemical Laboratory Using Inexpensive Electronic Modules.

Authors:  Pawel L Urban
Journal:  Angew Chem Int Ed Engl       Date:  2018-07-24       Impact factor: 15.336

5.  Rapid Prototyping of Microfluidic Systems in Poly(dimethylsiloxane).

Authors:  D C Duffy; J C McDonald; O J Schueller; G M Whitesides
Journal:  Anal Chem       Date:  1998-12-01       Impact factor: 6.986

6.  Single-cell analysis and sorting using droplet-based microfluidics.

Authors:  Linas Mazutis; John Gilbert; W Lloyd Ung; David A Weitz; Andrew D Griffiths; John A Heyman
Journal:  Nat Protoc       Date:  2013-04-04       Impact factor: 13.491

7.  DNA sequence analysis with droplet-based microfluidics.

Authors:  Adam R Abate; Tony Hung; Ralph A Sperling; Pascaline Mary; Assaf Rotem; Jeremy J Agresti; Michael A Weiner; David A Weitz
Journal:  Lab Chip       Date:  2013-12-21       Impact factor: 6.799

8.  A robust and tunable mitotic oscillator in artificial cells.

Authors:  Ye Guan; Zhengda Li; Shiyuan Wang; Patrick M Barnes; Xuwen Liu; Haotian Xu; Minjun Jin; Allen P Liu; Qiong Yang
Journal:  Elife       Date:  2018-04-05       Impact factor: 8.140

9.  Frugal Droplet Microfluidics Using Consumer Opto-Electronics.

Authors:  Caroline Frot; Nicolas Taccoen; Charles N Baroud
Journal:  PLoS One       Date:  2016-08-25       Impact factor: 3.240

10.  Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation.

Authors:  William Stephenson; Laura T Donlin; Andrew Butler; Cristina Rozo; Bernadette Bracken; Ali Rashidfarrokhi; Susan M Goodman; Lionel B Ivashkiv; Vivian P Bykerk; Dana E Orange; Robert B Darnell; Harold P Swerdlow; Rahul Satija
Journal:  Nat Commun       Date:  2018-02-23       Impact factor: 14.919

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  1 in total

1.  Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications.

Authors:  Alessandro Tonelli; Veronica Mangia; Alessandro Candiani; Francesco Pasquali; Tiziana Jessica Mangiaracina; Alessandro Grazioli; Michele Sozzi; Davide Gorni; Simona Bussolati; Annamaria Cucinotta; Giuseppina Basini; Stefano Selleri
Journal:  Sensors (Basel)       Date:  2021-05-20       Impact factor: 3.576

  1 in total

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