Keyword search (4,164 papers available)

"Microfluidics" Keyword-tagged Publications:

Title Authors PubMed ID
1 Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review Neriya Hegade KP; Bhat RB; Packirisamy M; 40650129
ENCS
2 An Automated Single-Cell Droplet-Digital Microfluidic Platform for Monoclonal Antibody Discovery Ahmadi F; Tran H; Letourneau N; Little SR; Fortin A; Moraitis AN; Shih SCC; 38441226
BIOLOGY
3 Measuring prion propagation in single bacteria elucidates a mechanism of loss Jager K; Orozco-Hidalgo MT; Springstein BL; Joly-Smith E; Papazotos F; McDonough E; Fleming E; McCallum G; Yuan AH; Hilfinger A; Hochschild A; Potvin-Trottier L; 37738299
PHYSICS
4 An electrochemical aptasensor for Δ9-tetrahydrocannabinol detection in saliva on a microfluidic platform Kékedy-Nagy L; Perry JM; Little SR; Llorens OY; Shih SCC; 36549107
BIOLOGY
5 Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications Allard P; Papazotos F; Potvin-Trottier L; 36312536
BIOLOGY
6 Microfluidics in smart packaging of foods Pou KRJ; Raghavan V; Packirisamy M; 36192908
ENCS
7 Microfluidic Platforms for the Isolation and Detection of Exosomes: A Brief Review Raju D; Bathini S; Badilescu S; Ghosh A; Packirisamy M; 35630197
ENCS
8 Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers López RR; Sánchez LM; Alazzam A; Burnier JV; Stiharu I; Nerguizian V; 34577745
ENCS
9 Magnetic particle based liquid biopsy chip for isolation of extracellular vesicles and characterization by gene amplification Bathini S; Pakkiriswami S; Ouellette RJ; Ghosh A; Packirisamy M; 34517262
ENCS
10 Microfluidic Shear Processing Control of Biological Reduction Stimuli-Responsive Polymer Nanoparticles for Drug Delivery. Huang Y, Jazani AM, Howell EP, Reynolds LA, Oh JK, Moffitt MG 33455300
CHEMBIOCHEM
11 Controlled Microfluidic Synthesis of Biological Stimuli-Responsive Polymer Nanoparticles. Huang Y, Moini Jazani A, Howell EP, Oh JK, Moffitt MG 31820915
CHEMBIOCHEM
12 One Cell, One Drop, One Click: Hybrid Microfluidics for Mammalian Single Cell Isolation. Samlali K, Ahmadi F, Quach ABV, Soffer G, Shih SCC 32705796
BIOLOGY
13 Surface Response Based Modeling of Liposome Characteristics in a Periodic Disturbance Mixer. López RR, Ocampo I, Sánchez LM, Alazzam A, Bergeron KF, Camacho-León S, Mounier C, Stiharu I, Nerguizian V 32106424
ENCS
14 Lab-On-A-Chip for the Development of Pro-/Anti-Angiogenic Nanomedicines to Treat Brain Diseases. Subramaniyan Parimalam S, Badilescu S, Sonenberg N, Bhat R, Packirisamy M 31817343
ENCS
15 Dielectrophoresis Multipath Focusing of Microparticles through Perforated Electrodes in Microfluidic Channels. Alazzam A, Al-Khaleel M, Riahi MK, Mathew B, Gawanmeh A, Nerguizian V 31394810
ENCS

 

Title:Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers
Authors:López RRSánchez LMAlazzam ABurnier JVStiharu INerguizian V
Link:https://pubmed.ncbi.nlm.nih.gov/34577745/
DOI:10.3390/mi12091102
Publication:Micromachines
Keywords:image processingmicrofluidicsmicromixersmixing efficiencynumerical model
PMID:34577745 Category: Date Added:2021-09-28
Dept Affiliation: ENCS
1 Department of Electrical Engineering, École de Technologie Supérieure, 1100 Notre Dame West, Montreal, QC H3C 1K3, Canada.
2 Cancer Research Program, RI-MUHC, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.
3 Department of Engineering, Universidad Autónoma de Querétaro, Cerro de las Campanas, Santiago de Querétaro 76010, Querétaro, Mexico.
4 Department of Mechanical Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.
5 Department of Mechanical and Industrial Engineering, Concordia University, 1515 Saint Catherine West, Montreal, QC H3G 1M8, Canada.

Description:

The shape and dimensions of a micromixer are key elements in the mixing process. Accurately quantifying the mixing efficiency enables the evaluation of the performance of a micromixer and the selection of the most suitable one for specific applications. In this paper, two methods are investigated to evaluate the mixing efficiency: a numerical model and an experimental model with a software image processing technique. Using two methods to calculate the mixing efficiency, in addition to corroborating the results and increasing their reliability, creates various possible approaches that can be selected depending on the circumstances, resources, amount of data to be processed and processing time. Image processing is an easy-to-implement tool, is applicable to different programming languages, is flexible, and provides a quick response that allows the calculation of the mixing efficiency using a process of filtering of images and quantifying the intensity of the color, which is associated with the percentage of mixing. The results showed high similarity between the two methods, with a difference ranging between 0 and 6% in all the evaluated points.





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