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Microfluidic generation of droplets with a high loading of nanoparticles.

ABSTRACT: Microfluidic approaches for controlled generation of colloidal clusters, for example, via encapsulation of colloidal particles in droplets, have been used for the synthesis of functional materials including drug delivery carriers. Most of the studies, however, use a low concentration of an original colloidal suspension (<10 wt %). Here we demonstrate microfluidic approaches for directly making droplets with moderate (10-25 wt %) and high (>60 wt %) particle concentrations. Three types of microfluidic devices, PDMS flow-focusing, PDMS T-junction, and microcapillary devices, are investigated for direct encapsulation of a high concentration of polystyrene (PS) nanoparticles in droplets. In particular, it is shown that PDMS devices fabricated by soft lithography can generate droplets from a 25 wt % PS suspension, whereas microcapillary devices made from glass capillary tubes are able to produce droplets from a 67 wt % PS nanoparticle suspension. When the PS concentration is between 0.6 and 25 wt %, the size of the droplets is found to change with the oil-to-water flow rate ratio and is independent of the concentration of particles in the initial suspensions. Drop sizes from ~12 to 40 ?m are made using flow rate ratios Q(oil)/Q(water) from 20 to 1, respectively, with either of the PDMS devices. However, clogging occurs in PDMS devices at high PS concentrations (>25 wt %) arising from interactions between the PS colloids and the surface of PDMS devices. Glass microcapillary devices, on the other hand, are resistant to clogging and can produce droplets continuously even when the concentration of PS nanoparticles reaches 67 wt %. We believe that our findings indicate useful approaches and guidelines for the controlled generation of emulsions filled with a high loading of nanoparticles, which are useful for drug delivery applications.

SUBMITTER: Wan J

PROVIDER: S-EPMC3856230 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Microfluidic generation of droplets with a high loading of nanoparticles.

Wan Jiandi J Shi Lei L Benson Bryan B Bruzek Matthew J MJ Anthony John E JE Sinko Patrick J PJ Prudhomme Robert K RK Stone Howard A HA

Langmuir : the ACS journal of surfaces and colloids 20120904 37

Microfluidic approaches for controlled generation of colloidal clusters, for example, via encapsulation of colloidal particles in droplets, have been used for the synthesis of functional materials including drug delivery carriers. Most of the studies, however, use a low concentration of an original colloidal suspension (<10 wt %). Here we demonstrate microfluidic approaches for directly making droplets with moderate (10-25 wt %) and high (>60 wt %) particle concentrations. Three types of microfl ...[more]

PMID: 22934976

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Project description:Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization.

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Microfluidic generation of droplets with a high loading of nanoparticles.

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Microfluidic generation of droplets with a high loading of nanoparticles.

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