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.

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.

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.