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Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine

Soo Chung1, Andrew Loh2, Christian M Jennings3

  • 1Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, United States; Department of Biosystems Engineering, Integrated Major in Global Smart Farm, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea.

Journal of Hazardous Materials
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

A novel paper-based microfluidic platform rapidly screens oil types using capillary flow velocity. This method accurately classifies crude and non-crude oils in under 30 seconds, aiding early tracking and analysis.

Keywords:
Capillary actionOil spillPaper microfluidic chipRaspberry PiSVM

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Fluid Dynamics

Background:

  • Accurate and rapid oil type screening is crucial for environmental monitoring and forensic analysis.
  • Traditional laboratory fingerprinting methods can be time-consuming and resource-intensive.
  • Developing portable and cost-effective screening tools is essential for field applications.

Purpose of the Study:

  • To develop a novel, rapid, and cost-effective method for screening various oil types.
  • To utilize a paper-based microfluidic platform for oil analysis.
  • To achieve high accuracy in classifying different oil categories, including crude and non-crude oils.

Main Methods:

  • A wax-printed paper-based microfluidic device was used for oil sample analysis.
  • Capillary action drove oil flow through micrometer-scale channels, with flow velocity profiles recorded.
  • Video data captured by a Raspberry Pi camera was analyzed using custom Python code.
  • Machine learning algorithms, including Principal Component Analysis (PCA), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA), were employed for classification.
  • A second-order polynomial SVM model with PCA pre-processing demonstrated superior performance.

Main Results:

  • The developed assay successfully screened oil types based on capillary flow velocity profiles.
  • The SVM model with PCA achieved 90% accuracy for crude oil classification and 81% for non-crude oil classification.
  • The entire assay, from sample introduction to result, was completed in under 30 seconds.
  • The capillary flow-driven analysis itself took only 5 seconds.

Conclusions:

  • The paper-based microfluidic platform offers a simple, effective, and rapid solution for preliminary oil type screening.
  • This assay enables early tracking of oil spills and reduces the number of samples requiring extensive laboratory analysis.
  • The technology holds potential for field deployment in environmental monitoring and oil spill response scenarios.