Optimization of Gold Nanoparticle Synthesis in Continuous‐Flow Micromixers Using Response Surface Methodology

Sarsfield, Marissa C.; Roberts, Alexa; Streletzky, Kiril A.; Fodor, Petru S.; Kothapalli, Chandrasekhar R.

doi:10.1002/ceat.202000314

Cited by 6 publications

(7 citation statements)

References 39 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Utilizing the same gold precursor and reducing agent, Lazarus et al [172] prepared AuNPs in a PDMS serpentine microchannel with good control over size and morphology (4.38 ± 0.53 nm). Sarsfield et al [173], synthesized AuNPs in a reverse-staggered herringbone PDMS micromixer. Several parameters, such as the ratio of the sodium citrate to HAuCl 4 , and the pH of the HAuCl 4 solution, were considered to study the effect on AuNP size and size distribution.…”

Section: Inorganic Np Synthesismentioning

confidence: 99%

A review on microfluidic-assisted nanoparticle synthesis, and their applications using multiscale simulation methods

et al. 2023

View full text Add to dashboard Cite

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

show abstract

Section: Inorganic Np Synthesismentioning

confidence: 99%

A review on microfluidic-assisted nanoparticle synthesis, and their applications using multiscale simulation methods

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The synthesis of nanoparticles through micromixers is predominantly affected by the reaction conditions and the nature of reactants utilized, thereby influencing their physical attributes. These influential factors encompass the concentration ratio of reactants, 31 temperature of reaction, 32 time of reaction, 33 inflow characteristics, 34 and the geometrical configuration of the microchannel. 35 Micromixers utilized for the nanoparticle synthesis process fall into two main categories: active and passive, each of which have their own means for controlling the physical properties of the synthesized particles.…”

Section: Introductionmentioning

confidence: 99%

Controllable Microfluidic System through Intelligent Framework: Data-Driven Modeling, Machine Learning Energy Analysis, Comparative Multiobjective Optimization, and Experimental Study

Kouhkord,

Hassani,

Amirmahani

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Intelligent microfluidics in nanoparticle synthesis embodies a comprehensive synergistic approach that merges numerical modeling, artificial intelligence, and experimental analysis, striving for controllability over an energyefficient microfluidic device designed for nanoparticle synthesis with desired physical properties. This study delves into a microfluidic mass transfer system, employing an innovative methodology that combines data-driven modeling, machine learning-based comparative multiobjective optimization, and experimental analysis to model a micromixing system. A surrogate data-driven model is employed to the microfluidic mass transfer system, considering four critical geometrical parameters and inlet Reynolds as design variables. The model provides insights into mixer's functionality. It is observed that at lower Reynolds numbers, increasing NoT increases the mixing efficiency by more than 20%. Moreover, altering SND i value leads to a significant 80% reduction in pressure drop. Identifying the optimal system from numerous design parameters is challenging but accomplished through machine learning. Two distinct machine learning algorithms were integrated with mathematical surrogate modeling to optimize the mixer for three objectives. RSM-Differential Evolution significantly outperforms RSM-NSGA-II in enhancing mixing characteristics and reducing the mechanical energy consumption by over 85%. Additionally, improvement in energy dissipation and effective energy efficiency of microsystem was made, alongside a comparable enhancement of mixing index and management of pressure drop. Fabrication of two optimal designs confirms an over 80% drop in pressure and an increase in mixing efficiency by over 20% at low Reynolds, outperforming conventional microfluidic mixers. The intelligent micromixer allows precise control over nanoparticle synthesis by adjusting microtransfer design parameters. This controlled process is crucial for tissue engineering hydrogel synthesis, nanotechnology, and targeted drug delivery.

show abstract

“…These factors are often correlated with each other, so it is necessary to investigate their interaction effects. Response surface methodology (RSM) is a useful statistical tool which is widely applied for optimization in many fields such as extraction (Zhang et al, 2018;Yu et al, 2019;Andres et al, 2020), organic synthesis (Hamsaveni et al, 2001;García-Cabeza et al, 2015;He et al, and Development 22(6) www.jad.hcmuaf.edu.vn 2017), nanomaterial synthesis (Perez et al, 2017;Abdulhameed et al, 2019;Sarsfield et al, 2021). This study, therefore, aimed to apply RSM to optimize a Friedel-Crafts sulfonylation between 1,3-dimethoxybenzene and p-toluenesulfonic anhydride using chloroaluminate ionic liquid immobilized on magnetic nanoparticles as the catalyst.…”

Section: Introductionmentioning

confidence: 99%

Friedel-Crafts sulfonylation catalyzed by chloroaluminate ionic liquid immobilized on magnetic nanoparticles: Optimization by response surface methodology

Nguyen,

Luu,

et al. 2023

The J. Agric. Dev.

View full text Add to dashboard Cite

This study focused on optimizing the Friedel-Crafts sulfonylation reaction between 1,3-dimethoxybenzene and p-toluenesulfonic anhydride using chloroaluminate ionic liquid immobilized on magnetic nanoparticles as the catalyst. Various reaction conditions including the ratio between reagents (0.9:1.0 - 1.1:1.0), the catalyst amount (0.1 - 0.3 g), reaction temperature (100 - 120oC), and time (1 - 3 h) were optimized using response surface methodology based on a central composite design model. The results showed that the optimal reaction conditions were achieved at 115°C for 2.3 h, using 0.24 g of catalyst with a reagent ratio of 1.0:1.0, resulting in the highest sulfones yield of 82%.

show abstract

Optimization of Gold Nanoparticle Synthesis in Continuous‐Flow Micromixers Using Response Surface Methodology

Cited by 6 publications

References 39 publications

A review on microfluidic-assisted nanoparticle synthesis, and their applications using multiscale simulation methods

A review on microfluidic-assisted nanoparticle synthesis, and their applications using multiscale simulation methods

Controllable Microfluidic System through Intelligent Framework: Data-Driven Modeling, Machine Learning Energy Analysis, Comparative Multiobjective Optimization, and Experimental Study

Friedel-Crafts sulfonylation catalyzed by chloroaluminate ionic liquid immobilized on magnetic nanoparticles: Optimization by response surface methodology

Contact Info

Product

Resources

About