Publication: Machine learning based microfluidic sensing device for viscosity measurements
dc.contributor.coauthor | Haider, Daniyal | |
dc.contributor.coauthor | Barua, Arnab | |
dc.contributor.coauthor | Tanyeri, Melikhan | |
dc.contributor.coauthor | Erten, Ahmet | |
dc.contributor.department | School of Medicine | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Mustafa, Adil | |
dc.contributor.kuauthor | Yalçın, Özlem | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.contributor.schoolcollegeinstitute | SCHOOL OF MEDICINE | |
dc.date.accessioned | 2025-01-19T10:33:04Z | |
dc.date.issued | 2023 | |
dc.description.abstract | 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. © 2023 RSC. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.issue | 6 | |
dc.description.openaccess | All Open Access; Gold Open Access | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | This study was supported by the Turkish Scientific and Technological Research Council grant SBAG-15S. | |
dc.description.volume | 2 | |
dc.identifier.doi | 10.1039/d3sd00099k | |
dc.identifier.issn | 26350998 | |
dc.identifier.quartile | Q2 | |
dc.identifier.scopus | 2-s2.0-85172860512 | |
dc.identifier.uri | https://doi.org/10.1039/d3sd00099k | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/26540 | |
dc.identifier.wos | 1193103000001 | |
dc.keywords | Viscometer | |
dc.keywords | Pressure | |
dc.keywords | Liquids | |
dc.language.iso | eng | |
dc.publisher | Royal Society of Chemistry | |
dc.relation.grantno | Turkish Scientific and Technological Research Council, (SBAG-15S) | |
dc.relation.ispartof | Sensors and Diagnostics | |
dc.subject | Medicine | |
dc.title | Machine learning based microfluidic sensing device for viscosity measurements | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Yalçın, Özlem | |
local.contributor.kuauthor | Mustafa, Adil | |
local.publication.orgunit1 | SCHOOL OF MEDICINE | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit2 | School of Medicine | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
relation.isOrgUnitOfPublication | d02929e1-2a70-44f0-ae17-7819f587bedd | |
relation.isOrgUnitOfPublication | 3fc31c89-e803-4eb1-af6b-6258bc42c3d8 | |
relation.isOrgUnitOfPublication.latestForDiscovery | d02929e1-2a70-44f0-ae17-7819f587bedd | |
relation.isParentOrgUnitOfPublication | 434c9663-2b11-4e66-9399-c863e2ebae43 | |
relation.isParentOrgUnitOfPublication | 17f2dc8e-6e54-4fa8-b5e0-d6415123a93e | |
relation.isParentOrgUnitOfPublication.latestForDiscovery | 434c9663-2b11-4e66-9399-c863e2ebae43 |
Files
Original bundle
1 - 1 of 1