3D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters

Shapovalov, V.V.; Chapek, S.V.; Tereshchenko, A.A.; Bulgakov, A.N.; Bagliy, A.P.; Volkov, V.V.; Konarev, P.V.; Soldatov, M.A.; Soldatov, S.A.; Guda, A.A.; Soldatov, A.V.

doi:10.1016/j.mne.2023.100224

Cited by 5 publications

(3 citation statements)

References 101 publications

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“…The protocols reported by Shapavalov et al [ 43 ] were adapted to avoid delamination of the model from the platform. The following parameters were chosen: the height of the first layer—25 µm; the exposure time of the first layer—20 s. The subsequent thicknesses and exposure times of the layers were 25 µm and 800 ms, respectively.…”

Section: Methodsmentioning

confidence: 99%

Microfluidically Assisted Synthesis of Calcium Carbonate Submicron Particles with Improved Loading Properties

Ermakov,

Chapek,

Lengert

et al. 2023

Micromachines

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The development of advanced methods for the synthesis of nano- and microparticles in the field of biomedicine is of high interest due to a range of reasons. The current synthesis methods may have limitations in terms of efficiency, scalability, and uniformity of the particles. Here, we investigate the synthesis of submicron calcium carbonate using a microfluidic chip with a T-shaped oil supply for droplet-based synthesis to facilitate control over the formation of submicron calcium carbonate particles. The design of the chip allowed for the precise manipulation of reaction parameters, resulting in improved porosity while maintaining an efficient synthesis rate. The pore size distribution within calcium carbonate particles was estimated via small-angle X-ray scattering. This study showed that the high porosity and reduced size of the particles facilitated the higher loading of a model peptide: 16 vs. 9 mass.% for the particles synthesized in a microfluidic device and in bulk, correspondingly. The biosafety of the developed particles in the concentration range of 0.08–0.8 mg per plate was established by the results of the cytotoxicity study using mouse fibroblasts. This innovative approach of microfluidically assisted synthesis provides a promising avenue for future research in the field of particle synthesis and drug delivery systems.

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Section: Methodsmentioning

confidence: 99%

Microfluidically Assisted Synthesis of Calcium Carbonate Submicron Particles with Improved Loading Properties

Ermakov,

Chapek,

Lengert

et al. 2023

Micromachines

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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%

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.

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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.

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Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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3D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters

Cited by 5 publications

References 101 publications

Microfluidically Assisted Synthesis of Calcium Carbonate Submicron Particles with Improved Loading Properties

Microfluidically Assisted Synthesis of Calcium Carbonate Submicron Particles with Improved Loading Properties

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

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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