Machine learning based microfluidic sensing device for viscosity measurements

Adil Mustafa*, Daniyal Haider, Arnab Barua, Melikhan Tanyeri*, Ahmet Erten, Ozlem Yalcin*

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2 Atıf (Scopus)

Özet

A microfluidic sensing device utilizing fluid-structure interactions and machine learning algorithms is demonstrated. The deflection of microsensors due to fluid flow within a microchannel is analysed using machine learning algorithms to calculate the viscosity of Newtonian and non-Newtonian fluids. Newtonian fluids (glycerol/water solutions) within a viscosity range of 5-100 cP were tested at flow rates of 15-105 mL h−1 (γ = 60.5-398.4 s−1) using a sample volume of 80-400 μL. The microsensor deflection data were used to train machine learning algorithms. Two different machine learning (ML) algorithms, support vector machine (SVM) and k-nearest neighbour (k-NN), were employed to determine the viscosity of unknown Newtonian fluids and whole blood samples. An average accuracy of 89.7% and 98.9% is achieved for viscosity measurement of unknown solutions using SVM and k-NN algorithms, respectively. The intelligent microfluidic viscometer presented here has the potential for automated, real-time viscosity measurements for rheological studies.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1509-1520
Sayfa sayısı12
DergiSensors and Diagnostics
Hacim2
Basın numarası6
DOI'lar
Yayın durumuYayınlandı - 30 Ağu 2023

Bibliyografik not

Publisher Copyright:
© 2023 RSC.

Finansman

This study was supported by the Turkish Scientific and Technological Research Council grant SBAG-15S.

FinansörlerFinansör numarası
Turkish Scientific and Technological Research CouncilSBAG-15S

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