Publication:
Machine learning based microfluidic sensing device for viscosity measurements

dc.contributor.coauthorHaider, Daniyal
dc.contributor.coauthorBarua, Arnab
dc.contributor.coauthorTanyeri, Melikhan
dc.contributor.coauthorErten, Ahmet
dc.contributor.departmentSchool of Medicine
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorMustafa, Adil
dc.contributor.kuauthorYalçın, Özlem
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2025-01-19T10:33:04Z
dc.date.issued2023
dc.description.abstractA 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. © 2023 RSC.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue6
dc.description.openaccessAll Open Access; Gold Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipThis study was supported by the Turkish Scientific and Technological Research Council grant SBAG-15S.
dc.description.volume2
dc.identifier.doi10.1039/d3sd00099k
dc.identifier.issn26350998
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85172860512
dc.identifier.urihttps://doi.org/10.1039/d3sd00099k
dc.identifier.urihttps://hdl.handle.net/20.500.14288/26540
dc.identifier.wos1193103000001
dc.keywordsViscometer
dc.keywordsPressure
dc.keywordsLiquids
dc.language.isoeng
dc.publisherRoyal Society of Chemistry
dc.relation.grantnoTurkish Scientific and Technological Research Council, (SBAG-15S)
dc.relation.ispartofSensors and Diagnostics
dc.subjectMedicine
dc.titleMachine learning based microfluidic sensing device for viscosity measurements
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorYalçın, Özlem
local.contributor.kuauthorMustafa, Adil
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2School of Medicine
local.publication.orgunit2Graduate School of Sciences and Engineering
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