A universal inverse design methodology for microfluidic mixers.

The intelligent design of microfluidic mixers encompasses both the automation of predicting fluid performance and the structural design of mixers. This article delves into the technical trajectory of computer-aided design for micromixers, leveraging artificial intelligence algorithms. We propose an automated micromixer design methodology rooted in cost-effective artificial neural network (ANN) models paired with inverse design algorithms. Initially, we introduce two inverse design methods for micromixers: one that combines ANN with multi-objective genetic algorithms, and another that fuses ANN with particle swarm optimization algorithms. Subsequently, using two benchmark micromixers as case studies, we demonstrate the automatic derivation of micromixer structural parameters. Finally, we automatically design and optimize 50 sets of micromixer structures using the proposed algorithms. The design accuracy is further enhanced by analyzing the inverse design algorithm from a statistical standpoint.

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics.

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.

Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator.

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.

Automated calibration of 3D-printed microfluidic devices based on computer vision.

With the development of 3D printing techniques, the application of it in microfluidic/Lab-on-a-Chip (LoC) fabrication is becoming more and more attractive. However, to achieve a satisfying printing quality of the target devices, researchers usually require quite an amount of work in calibration trials even for high-end 3D printers. To increase the calibration efficiency of the average priced printers and promote the application of 3D printing technology in the microfluidic community, this work has presented a computer vision (CV)-based method for rapid and precise 3D printing calibration with examples on cylindrical hole/post diameters of 0.2-2.4 mm and rectangular hole/post widths of 0.2-1.0 mm by a stereolithography-based 3D printer. Our method is fully automated, which contains five steps and only needs a camera at hand to provide photos for convolutional neural network recognition. The experimental results showed that our CV-based method could provide calibrated dimensions with just one print of the specific calibration ruler to meet user desire. The higher resolution of the photo provides a higher precision in calibration. Subsequently, only one more print for the target device is needed after the calibration process. Overall, this work has provided a quick and precise calibration tool for researchers to apply 3D printing in the fabrication of their microfluidic/LoC devices with average price printers. Besides, with our open source calibration software and calibration ruler design file, researchers can modify the specific setting based on customized needs and conduct calibration on any type of 3D printer.

Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks.

With the various applications of microfluidics, numerical simulation is highly recommended to verify its performance and reveal potential defects before fabrication. Among all the simulation parameters and simulation tools, the velocity field and concentration profile are the key parts and are generally simulated using finite element analysis (FEA). In our previous work [Wang et al., Lab Chip, 2016, 21, 4212-4219], automated design of microfluidic mixers by pre-generating a random library with the FEA was proposed. However, the duration of the simulation process is time-consuming, while the matching consistency between limited pre-generated designs and user desire is not stable. To address these issues, we inventively transformed the fluid mechanics problem into an image recognition problem and presented a convolutional neural network (CNN)-based technique to predict the fluid behavior of random microfluidic mixers. The pre-generated 10 513 candidate designs in the random library were used in the training process of the CNN, and then 30 757 brand new microfluidic mixer designs were randomly generated, whose performance was predicted by the CNN. Experimental results showed that the CNN method could complete all the predictions in just 10 seconds, which was around 51 600× faster than the previous FEA method. The CNN library was extended to contain 41 270 candidate designs, which has filled up those empty spaces in the fluid velocity versus solute concentration map of the random library, and able to provide more choices and possibilities for user desire. Besides, the quantitative analysis has confirmed the increased compatibility of the CNN library with user desire. In summary, our CNN method not only presents a much faster way of generating a more complete library with candidate mixer designs but also provides a solution for predicting fluid behavior using a machine learning technique.

Hydroxyapatite/silver electrospun fibers for anti-infection and osteoinduction.

Bone implant materials cause the most common complication of bone infections in orthopedic surgery, resulting in implant failure. Antibiotic treatment of bone infections leads to problems such as bacterial resistance and reduced osteogenic capacity. In this study, dopamine (DA) was self-polymerized on the surface of Polylactic acid (PLLA)/Hydroxyapatite (HA) nanowire composite fibers to form an adhesive polydopamine (PDA) membrane, and a stable silver-nanoparticles (Ag-NPs) coating layer was constructed on it by electrochemically driven Ag+ coordination and chelation through Polypyrrole (PPy) mediation, achieving steady and slow release of Ag-NPs. With optimized DA soaking time of 24 h and soaking concentration of 0.5 g·L-1, nanoparticles were uniformly distributed on PLLA/HA/PDA/PPy/Ag composite fibers and the hydrophilicity of the composite fibers was well-behaved. Besides, the composite fibers possessed good physiological stability and 100% antibacterial rate against Escherichia coli (E. coli) as well as Staphylococcus aureus (S. aureus). In addition, the composite fibers had promoted apatite nucleation and growth on surface and good cytocompatibility with osteoblasts, indicating ability of inducing osteogenic differentiation. In summary, a multi-functional PLLA/HA/PDA/PPy/Ag composite fiber with long-term antibacterial property, bioactivity and osteoinductivity was successfully constructed by electrospinning and electrochemical deposition.

Stable ZnO-doped hydroxyapatite nanocoating for anti-infection and osteogenic on titanium.

Titanium and titanium alloys have been widely used in orthopedics and related fields. However, their clinical applications are limited due to the lack of anti-infection, osteoinductivity and angiogenic ability. In the present study, we utilized pulse electrochemical deposition method to prepare polypyrrole (PPy) by the in-situ oxidative polymerization of pyrrole (Py), and through the coordination and doping of ions, the function of PPy as a dual regulator of hydroxyapatite nanoparticles (HA-NPs) and zinc oxide nanoparticles (ZnO-NPs) was achieved. Bioactivity test showed that the composite coating could induce the formation of apatite, and the apatite was in a neat arrangement preferentially grew along the (002) crystal plane, indicating good bioactivity. The release test showed that the dual regulation effect of PPy coordination and doping reduced the release rate of Ca2+ and Zn2+ from the composite coating. Antibacterial tests showed that the composite coating against Escherichia coli and Staphylococcus aureus. Besides, bone marrow-derived mesenchymal stem cells (BMSCs) exhibited good adhesion, proliferation and differentiation on the composite coating, and fluorescence staining experiments demonstrated good osteoinductivity of the composite coating. In this study, a multifunctional composite coating with anti-infection, angiogenic and osteoinductivity was successfully constructed on the titanium surface via pulse electrochemical deposition method.

Photo-assisted green synthesis of silver doped silk fibroin/carboxymethyl cellulose nanocomposite hydrogels for biomedical applications.

Silver nanoparticles (AgNPs) and regenerated silk fibroin (RSF) have recently attracted significant interests for their potential applications in preventing wound-related infections and in tissue engineering. Indeed, nano-silver has long been recognized as one of the most effective antimicrobial agents, and silk fibroin is well known for its capability of stimulating cell activities and facilitating tissue regeneration. In this study, a green synthesis approach was used to create a composite hydrogel (CoHy) of RSF stabilized with CarboxymethylCellulose-Na (CMC-Na) and loaded with AgNPs. Their swelling ratios were up to 59 g/g when tested in different physiologically relevant fluids. Material characterizations by Scanning electron microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS), and X-Ray Diffraction (XRD) confirmed the presence of AgNPs on the surface. Antimicrobial properties of the CoHy samples were evaluated using agar diffusion tests. The results showed distinct inhibition zones against major microorganisms found in wound infections, including Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), Staphylococcus epidermidis (S. epidermidis), Methicillin Resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa (P. aeruginosa), Candida albicans (C. albicans) and Fluconazole-resistant Candida albicans (FRCA). Cytocompatibility studies with rat bone marrow derived mesenchymal stem cells (BMSCs) in vitro showed that the adhesion density of BMScs on the CoHy loaded with 1 mg/mL was similar to the cell-only control group for the first 24 h of culture; moreover, higher cell proliferation was observed on the CoHy without AgNPs, indicating the regenerative potentials of the RSF/CMC composite hydrogels.

Finding the optimal design of a passive microfluidic mixer.

The ability to thoroughly mix two fluids is a fundamental need in microfluidics. While a variety of different microfluidic mixers have been designed by researchers, it remains unknown which (if any) of these mixers are optimal (that is, which designs provide the most thorough mixing with the smallest possible fluidic resistance across the mixer). In this work, we automatically designed and rationally optimized a microfluidic mixer. We accomplished this by first generating a library of thousands of different randomly designed mixers, then using the non-dominated sorting genetic algorithm II (NSGA-II) to optimize the random chips in order to achieve Pareto efficiency. Pareto efficiency is a state of allocation of resources (e.g. driving force) from which it is impossible to reallocate so as to make any one individual criterion better off (e.g. pressure drop) without making at least one individual criterion (e.g. mixing performance) worse off. After 200 generations of evolution, Pareto efficiency was achieved and the Pareto-optimal front was found. We examined designs at the Pareto-optimal front and found several design criteria that enhance the mixing performance of a mixer while minimizing its fluidic resistance; these observations provide new criteria on how to design optimal microfluidic mixers. Additionally, we compared the designs from NSGA-II with some popular microfluidic mixer designs from the literature and found that designs from NSGA-II have lower fluidic resistance with similar mixing performance. As a proof of concept, we fabricated three mixer designs from 200 generations of evolution and one conventional popular mixer design and tested the performance of these four mixers. Using this approach, an optimal design of a passive microfluidic mixer is found and the criteria of designing a passive microfluidic mixer are established.

Magnetic Nanocomposite Hydrogel for Potential Cartilage Tissue Engineering: Synthesis, Characterization, and Cytocompatibility with Bone Marrow Derived Mesenchymal Stem Cells.

Hydrogels possess high water content and closely mimic the microenvironment of extracellular matrix. In this study, we created a hybrid hydrogel containing type II collagen, hyaluronic acid (HA), and polyethylene glycol (PEG) and incorporated magnetic nanoparticles into the hybrid hydrogels of type II collagen-HA-PEG to produce a magnetic nanocomposite hydrogel (MagGel) for cartilage tissue engineering. The results showed that both the MagGel and hybrid gel (Gel) were successfully cross-linked and the MagGel responded to an external magnet while maintaining structural integrity. That is, the MagGel could travel to the tissue defect sites in physiological fluids under remote magnetic guidance. The adhesion density of bone marrow derived mesenchymal stem cells (BMSCs) on the MagGel group in vitro was similar to the control group and greater than the Gel group. The morphology of BMSCs was normal and consistent in all groups. We also found that BMSCs engulfed magnetic nanoparticles in culture and the presence of magnetic nanoparticles did not affect BMSC adhesion and morphology. We hypothesized that the ingested nanoparticles may be eventually broken down by lysosome and excreted through exocytosis; further studies are necessary to confirm this. This study reports a promising magnetic responsive nanocomposite hydrogel for potential cartilage tissue engineering applications, which should be further studied for its effects on cell functions when combined with electromagnetic stimulation.