Continuous nanoparticle production by microfluidic-based emulsion, mixing and crystallization

Su, Yi‐Feng; Kim, H.; Kovenklioglu, Suphan; Lee, W.Y.

doi:10.1016/j.jssc.2007.06.033

Cited by 59 publications

(44 citation statements)

References 13 publications

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“…The extent of polymer-solvent interaction can be estimated from a 2-D graph (Bagley et al, 1971), in which a hydrogen bonding solubility parameter, , 1976;Choi et al, 2002;Su et al, 2007). Fig.…”

Section: Prediction Of Polymer-solvent and Polymer-water Interactionsmentioning

confidence: 99%

“…The size of the generated NPs was controlled by the flow rates of solvent and anti-solvent, with smaller particles being formed at higher flow rates. Su et al (2007) prepared BaSO 4 and 2,2-dipyridylamine NPs using a microfluidic set-up composed of three T-junctions. Solvent and anti-solvent droplets 4 were formed in two upstream T junctions and then merged together in a downstream T junction.…”

Section: Introductionmentioning

confidence: 99%

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Preparation of biodegradable polymeric nanoparticles for pharmaceutical applications using glass capillary microfluidics

Othman

Vladisavljević

Nagy

2015

Chemical Engineering Science

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The aim of this study was to develop a new microfluidic approach for the preparation of nanoparticles with tuneable sizes based on micromixing / direct nanoprecipitation in a coaxial assembly of tapered-end glass capillaries. The organic phase was 1 wt% poly(-caprolactone) (PCL) or poly(dl-lactic acid) (PLA) in tetrahydrofuran and the antisolvent was Milli-Q water.The size of nanoparticles was precisely controlled over a range of 190-650 nm by controlling phase flow rates, orifice size and flow configuration (two-phase co-flow or countercurrent flow focusing). Smaller particles were produced in a flow focusing device, because the organic phase stream was significantly narrower than the orifice and remained narrow for a longer distance downstream of the orifice. The mean size of PCL particles produced in a flow focusing device with an orifice size of 200 m, an organic phase flow rate of 1.7 mL h -1 and an aqueous-toorganic flow rate ratio of 10 was below 200 nm. The size of nanoparticles decreased with decreasing the orifice size and increasing the aqueous-to-organic phase flow rate ratio. Due to *Revised Manuscript Click here to view linked References 2 higher affinity for water and amorphous structure, PLA nanoparticles were smaller and exhibited a smoother surface and more rounded shape than PCL particles.

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Section: Prediction Of Polymer-solvent and Polymer-water Interactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preparation of biodegradable polymeric nanoparticles for pharmaceutical applications using glass capillary microfluidics

Othman

Vladisavljević

Nagy

2015

Chemical Engineering Science

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“…Considering the literature, a few works so far have reported the factors affecting the particle size of nanosuspensions prepared by microfluidic reactors (17)(18)(19). However, no comprehensive work so far has reported the possible factors influencing the polydispersity of the nanoparticles prepared through this approach.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

2012

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Abstract. Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.

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“…Su et al regarding preparation of continuous nanoparticle using microfluidic-based emulsion (30). In fact, this finding may be due to enhancement in the supersaturation level against increased mixing, obtained with increase at flow rate of solvent (18,30).…”

Section: Discussionmentioning

confidence: 99%

Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

et al. 2015

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Abstract. In this study, nanosuspension of stable iodine ( 127 I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering. The response surfaces obtained from ANNs model illustrated the determining effect of input variables (solvent and antisolvent flow rate, surfactant concentration, and solvent temperature) on the output variable (nanoparticle size). Comparing the 3D graphs revealed that solvent and antisolvent flow rate had reverse relation with size of nanoparticles. Also, those graphs indicated that the solvent temperature at low values had an indirect relation with size of stable iodine ( 127 I) nanoparticles, while at the high values, a direct relation was observed. In addition, it was found that the effect of surfactant concentration on particle size in the nanosuspension of stable iodine ( 127 I) was depended on the solvent temperature.

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Continuous nanoparticle production by microfluidic-based emulsion, mixing and crystallization

Cited by 59 publications

References 13 publications

Preparation of biodegradable polymeric nanoparticles for pharmaceutical applications using glass capillary microfluidics

Preparation of biodegradable polymeric nanoparticles for pharmaceutical applications using glass capillary microfluidics

The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

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