Abstract
Recent years have witnessed an increased interest in the development of nanoparticles (NPs) owing to their potential use in a wide variety of biomedical applications, including drug delivery, imaging agents, gene therapy, and vaccines, where recently, lipid nanoparticle mRNA-based vaccines were developed to prevent SARS-CoV-2 causing COVID-19. NPs typically fall into two broad categories: organic and inorganic. Organic NPs mainly include lipid-based and polymer-based nanoparticles, such as liposomes, solid lipid nanoparticles, polymersomes, dendrimers, and polymer micelles. Gold and silver NPs, iron oxide NPs, quantum dots, and carbon and silica-based nanomaterials make up the bulk of the inorganic NPs. These NPs are prepared using a variety of top-down and bottom-up approaches. Microfluidics provide an attractive synthesis alternative and is advantageous compared to the conventional bulk methods. The microfluidic mixing-based production methods offer better control in achieving the desired size, morphology, shape, size distribution, and surface properties of the synthesized NPs. The technology also exhibits excellent process repeatability, fast handling, less sample usage, and yields greater encapsulation efficiencies. In this article, we provide a comprehensive review of the microfluidic-based passive and active mixing techniques for NP synthesis, and their latest developments. Additionally, a summary of microfluidic devices used for NP production is presented. Nonetheless, despite significant advancements in the experimental procedures, complete details of a nanoparticle-based system cannot be deduced from the experiments alone, and thus, multiscale computer simulations are utilized to perform systematic investigations. The work also details the most common multiscale simulation methods and their advancements in unveiling critical mechanisms involved in nanoparticle synthesis and the interaction of nanoparticles with other entities, especially in biomedical and therapeutic systems. Finally, an analysis is provided on the challenges in microfluidics related to nanoparticle synthesis and applications, and the future perspectives, such as large-scale NP synthesis, and hybrid formulations and devices.
Graphical abstract
In this review article we have covered the state-of-the-art microfluidic methodologies for the synthesis of a broad range of nanoparticle (NPs) for potential applications in the fields of biomedicine and drug delivery.
Apart from the experimental methodologies, this review also details the most common multiscale simulation methods used to unravel the critical mechanisms involved in nanoparticle synthesis and their interactions with other entities.
A comprehensive summary of the microfluidic techniques, divided into passive and active micro-mixing methods, have been provided, while highlighting advantages and disadvantages of individual methods.
We have discussed challenges related to NPs synthesis, their application in new fields, and future perspectives.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Department of Mechanical Engineering, Khalifa University, Abu Dhabi, UAE (GRID: grid.440568.b) (ISNI: 0000 0004 1762 9729)
2 Department of Mechanical Engineering, Khalifa University, Abu Dhabi, UAE (GRID: grid.440568.b) (ISNI: 0000 0004 1762 9729); System on Chip Center, Khalifa University, Abu Dhabi, UAE (GRID: grid.440568.b) (ISNI: 0000 0004 1762 9729)
3 Concordia University, Montreal, QC, Canada (GRID: grid.410319.e) (ISNI: 0000 0004 1936 8630)
4 École de Technologie Supérieure ÉTS, Montreal, QC, Canada (GRID: grid.459234.d) (ISNI: 0000 0001 2222 4302)
5 Department of Electrical Engineering, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany (GRID: grid.6936.a) (ISNI: 0000000123222966)





