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Microfluidics and Nanofluidics

, Volume 18, Issue 4, pp 657–665 | Cite as

Automating microfluidic part verification

  • Ryan S. Pawell
  • Robert A. Taylor
  • Kevin V. Morris
  • Tracie J. Barber
Research Paper

Abstract

Microfluidic devices promise to significantly advance the field of medicine by providing diagnostic tools that may be administered at the point-of-care (POC). Currently, a major barrier to entry for POC microfluidic diagnostic technologies is part qualification where specific features of a microfluidic technology must be qualified during the manufacturing process to ensure the device performs properly, e.g., Tantra and van Heeren (Lab Chip 13:2199, 2013). Additionally, microfluidic device research is moving toward therapeutic applications where quality control requirements may be more stringent. In this paper, we use soft embossing—a replication technology—to manufacture high aspect ratio thermoplastic deterministic lateral displacement (DLD) parts, where DLD is a size-based microfluidic particle separation technology based on shift post arrays. Morphological data are collected using optical profilometry, and key metrics are automatically extracted using a novel image analysis algorithm. This algorithm allows us to rapidly quantify the average post height, post shape, array pitch and surface roughness in order to compare a lot of 12 devices to the original design and the deep reactive ion etched silicon master mold. During the run of 12 devices, the post height was 63.0 ± 5.1 μm (mean ± 6σ), the pitch was 35.6 ± 0.31 μm, the post eccentricity was 0.503 ± 0.197, the post circularity was 1.195 ± 0.084 and the root-mean-square surface roughness was 4.19 ± 3.39 μm. Importantly, this automated part qualification technique reduced data analysis time by 75- to 100-fold while allowing for additional qualification metrics and significantly reducing the need for manual measurement. Overall, this paper indicates that it is possible to improve the quality and efficiency of microfluidic part qualification using optical profilometry coupled with a novel image analysis algorithm.

Keywords

Microfluidics Manufacturing Verification Qualification Image analysis 

List of symbols

σ

Standard deviation

θ

Tilt angle

\(\ominus \)

Image erosion

\(\oplus \)

Image dilation

3D

Three-dimensional

a

Major axis length

A

Area

b

Minor axis length

c

Circularity

CPU

Central processing unit

CSV

Comma separated value

DLD

Deterministic lateral displacement

e

Eccentricity

DRIE

Deep reactive ion etching

E

Euclidean space

f(x, y)

Planar surface fit

\(f_{\mathrm{max}}\)

Maximum f(x, y) value

\(f_{\mathrm{min}}\)

Maximum f(x, y) value

I

Image

P

Perimeter

\(p_0\)

X-coefficient for the surface fit

\(p_1\)

Y-coefficient for the surface fit

\(p_2\)

Z-coefficient for the surface fit

PDMS

Polydimethylsiloxane

PO

Polyolefin

POC

Point-of-care

PQ

Part qualification

QC

Quality control

RAM

Random access memory

S

Structured element

\(S_z\)

Translation of S to position z

\(s(\theta =0)\)

s(x, y) without tilt

\(s(\theta =0)_{\mathrm{corr}}\)

\(s(\theta =0)\) with minimum value of 0

s(x,y)

Surface contour data

SEM

Scanning electron micrograph

Notes

Acknowledgments

This work was partially funded by the Australian Research Council (DP0110102207). The DRIE work was performed in part at the New South Wales node of the Australian National Fabrication Facility, a company established under the National Collaborative Research Infrastructure Strategy to provide nano- and micro-fabrication facilities for Australians researchers.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ryan S. Pawell
    • 1
  • Robert A. Taylor
    • 1
  • Kevin V. Morris
    • 2
  • Tracie J. Barber
    • 1
  1. 1.School of Mechanical and Manufacturing EngineeringUniversity of New South WalesKensingtonAustralia
  2. 2.School of Biotechnology and Biomolecular SciencesUniversity of New South WalesKensingtonAustralia

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