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Machine learning based microfluidic sensing device for viscosity measurements
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DOI：10.1039/d3sd00099k.png)
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摘要
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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 (gamma = 60.5-398.4 s-1) using a sample volume of 80-400 mu L. 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. An increase in microsensor deflection with an increase in blood viscosity during coagulation.
Keyword:
VISCOMETER
PRESSURE
LIQUIDS
期刊
S
IF:
4.1
论文数:
496
被引数:
1.2K
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引用论文
Analysis of non-Newtonian liquids using a microfluidic capillary viscometer.png)
ANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
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