GNN-based Concentration Prediction with Variable Input Flow Rates for Microfluidic Mixers.

Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.

Microstructure and Corrosion Behavior of Zinc/Hydroxyapatite Multi-Layer Coating Prepared by Combining Cold Spraying and High-Velocity Suspension Flame Spraying.

The study aims to enhance the corrosion resistance and bioactivity of Mg alloy substrates through the development of a zinc/hydroxyapatite multi-layer (Zn/HA-ML) coating. The Zn/HA-ML coating was prepared by depositing a cold-sprayed (CS) Zn underlayer and a high-velocity suspension flame sprayed (HVSFS) Zn/HA multi-layer and was compared with the CS Zn coating and the Zn/HA dual-layer (Zn/HA-DL) coating. Phase, microstructure, and bonding strength were examined, respectively, by X-ray diffraction, scanning electron microscopy, and tensile bonding testing. Corrosion behavior and bioactivity were investigated using potentiodynamic polarization, electrochemical impedance spectroscopy, and immersion testing. Results show that the HVSFS Zn/HA composite layers were mainly composed of Zn, HA, and ZnO and were well bonded to the substrate. The HVSFS HA upper layer on the CS Zn underlayer in the Zn/HA-DL coating exhibited microcracks due to their mismatched thermal expansion coefficient (CTE). The Zn/HA-ML coating exhibited good bonding within different layers and showed a higher bonding strength of 27.3 ± 2.3 MPa than the Zn/HA-DL coating of 20.4 ± 2.7 MPa. The CS Zn coating, Zn/HA-DL coating, and Zn/HA-ML coating decreased the corrosion current density of the Mg alloy substrate by around two-fourfold from 3.12 ± 0.75 mA/cm2 to 1.41 ± 0.82mA/cm2, 1.06 ± 0.31 mA/cm2, and 0.88 ± 0.27 mA/cm2, respectively. The Zn/HA-ML coating showed a sixfold decrease in the corrosion current density and more improvements in the corrosion resistance by twofold after an immersion time of 14 days, which was mainly attributed to newly formed apatite and corrosion by-products of Zn particles. The Zn/HA-ML coating effectively combined the advantages of the corrosion resistance of CS Zn underlayer and the bioactivity of HVSFS Zn/HA multi-layers, which proposed a low-temperature strategy for improving corrosion resistance and bioactivity for implant metals.

Influence of Heat Treatment on Microstructure, Mechanical Property, and Corrosion Behavior of Cold-Sprayed Zn Coating on Mg Alloy Substrate.

The influence of post-process heat treatment on cold-sprayed Zn coatings on the Mg alloy substrate was investigated at different temperatures (150, 250, and 350 °C) and times (2, 8, and 16 h). Phase, microstructure, microhardness, and tensile strength of Zn coatings were analyzed before and after heat treatment. Corrosion properties of Zn coatings after heat treatment were investigated in simulated body fluid by using potentiodynamic polarization and immersion testing. Results show that although the heat treatment presented little effect on phase compositions of Zn coatings, the full width at half maxima of the Zn phase decreased with the heat temperature and time. Zn coatings presented comparable microstructures before and after heat treatment in addition to the inter-diffusion layers, and the inter-diffusion layer was dependent on the heat temperature and time. Both the thickness and the microhardness of inter-diffusion layers were increased with the heat temperature and time, with the largest thickness of 704.1 ± 32.4 µm and the largest microhardness of 323.7 ± 104.1 HV0.025 at 350 °C for 2 h. The microhardness of Zn coating was significantly decreased from 70.8 ± 5.6 HV0.025 to 43.9 ± 12.5 HV0.025, with the heat temperature from the ambient temperature to 350 °C, and was slightly decreased with the heat time at 250 °C. Although the tensile strength of Zn coating was slightly increased by heat treatment, with the highest value of 40.9 ± 3.9 MPa at 150 °C for 2 h, excessive heat temperature and time were detrimental to the tensile strength, with the lowest value of 6.6 ± 1.6 MPa at 350 °C for 2 h. The heat temperature and heat time presented limited effects on the corrosion current and corrosion ratio of the Zn coatings, and Zn coatings before and after heat treatment effectively hindered the simulated body fluid from penetrating into the substrate. The corrosion behavior of Zn coatings was discussed in terms of corrosion products and microstructures after immersion.

Three-Body Abrasive Wear Behavior of WC-10Cr3C2-12Ni Coating for Ball Mill Liner Application.

Carbide coatings are frequently used to improve the wear resistance of industrial components in various wear environments. In this research, aiming at the service characteristics of easy wear and short service life of ball mill liners, WC-10Cr3C2-12Ni coatings were prepared by supersonic flame spraying technology (HVOF). The reciprocating sliding tests were conducted under four different WC particle size conditions, and the differences in the tribological behavior of the coatings and three-body abrasive wear mechanism were obtained. The findings reveal that the average nanohardness of the WC-Cr3C2-Ni coating is nearly five times greater than that of the steel substance. The COF of tribo-pairs decreases and then increases as the particle size increases. In the case of no particles, the surface of the coating is slightly worn, with fatigue and oxidative wear being the primary wear mechanisms. Small particles (1.5 µm and 4 µm) are crushed and coated on the coating surface, in which the extremely fine particles are plasticized to form friction layers that have a protective effect on the coatings. The protective effect of the particles disappears as the particle size increases and is replaced by a powerful chiseling effect on the coatings, resulting in serious material loss. The particle size has a direct relationship with coating wear.

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.

Security analysis and secure channel-free certificateless searchable public key authenticated encryption for a cloud-based Internet of things.

With the rapid development of informatization, an increasing number of industries and organizations outsource their data to cloud servers, to avoid the cost of local data management and to share data. For example, industrial Internet of things systems and mobile healthcare systems rely on cloud computing's powerful data storage and processing capabilities to address the storage, provision, and maintenance of massive amounts of industrial and medical data. One of the major challenges facing cloud-based storage environments is how to ensure the confidentiality and security of outsourced sensitive data. To mitigate these issues, He et al. and Ma et al. have recently independently proposed two certificateless public key searchable encryption schemes. In this paper, we analyze the security of these two schemes and show that the reduction proof of He et al.'s CLPAEKS scheme is incorrect, and that Ma et al.'s CLPEKS scheme is not secure against keyword guessing attacks. We then propose a channel-free certificateless searchable public key authenticated encryption (dCLPAEKS) scheme and prove that it is secure against inside keyword guessing attacks under the enhanced security model. Compared with other certificateless public key searchable encryption schemes, this scheme has higher security and comparable efficiency.

A Comparative Study of Pressureless Sintered Nanostructured Hydroxyapatite/TiO2 Composites Prepared by Different TiO2 Addition Methods.

Two methods of TiO2 addition were applied to prepare hydroxyapatite/TiO2 (HA/TiO2) composite, i.e., in-situ hydrolysis TiO2 in HA powders (N-HA/TiO2) and mixing commercial nano-sized HA and TiO2 powder (C-HA/TiO2). Effects of TiO2 addition methods and sintering temperatures on phase, microstructure and microhardness were investigated for pressureless sintered HA/TiO2 composites, and pure HA was investigated for comparison. Results show that TiO2 from both in-situ hydrolysis and mixing commercial powder presented similar effects on phase structures and composition, and trended to chemically react with HA in the HA/TiO2 composites at high sintering temperature. Weight loss for different composites was investigated by thermal analysis. Sintering behavior for two different composite was also discussed. The TiO2 from in-situ hydrolysis can effectively enhance the TiO2 distribution and densification for the N-HA/TiO2 composites. Both two different composites showed typical grain growth and pore formation with the increase of sintering temperature. The N-HA/TiO2 composite had a lower porosity, higher shrinkage and microhardness than that of C-HA/TiO2 composite at sintering temperature from 700 °C to 1100 °C.

Effect of Particle Size and Impact Velocity on Collision Behaviors Between Nano-Scale TiN Particles: MD Simulation.

Inter-particle bonding formation which determines qualities of nano-scale ceramic coatings is influenced by particle collision behaviors during high velocity collision processes. In this study, collision behaviors between nano-scale TiN particles with different diameters were illuminated by using Molecular Dynamics simulation through controlling impact velocities. Results show that nano-scale TiN particles exhibit three states depending on particle sizes and impact velocities, i.e., bonding, bonding with localized fracturing, and rebounding. These TiN particles states are summarized into a parameter selection map providing an overview of the conditions in terms of particle sizes and velocities. Microstructure results show that localized atoms displacement and partial fracture around the impact region are main reasons for bonding formation of nano-scale ceramic particles, which shows differences from conventional particles refining and amorphization. A relationship between the adhesion energy and the rebound energy is established to understand bonding formation mechanism for nano-scale TiN particle collision. Results show that the energy relationship is depended on the particle sizes and impact velocities, and nano-scale ceramic particles can be bonded together as the adhesion energy being higher than the rebound energy.

MD Simulation on Collision Behavior Between Nano-Scale TiO2 Particles During Vacuum Cold Spraying.

Particle collision behavior influences significantly inter-nano particle bonding formation during the nano-ceramic coating deposition by vacuum cold spraying (or aerosol deposition method). In order to illuminate the collision behavior between nano-scale ceramic particles, molecular dynamic simulation was applied to explore impact process between nano-scale TiO2 particles through controlling impact velocities. Results show that the recoil efficiency of the nano-scale TiO2 particle is decreased with the increase of the impact velocity. Nano-scale TiO2 particle exhibits localized plastic deformation during collision at low velocities, while it is intensively deformed by collision at high velocities. This intensive deformation promotes the nano-particle adhesion rather than rebounding off. A relationship between the adhesion energy and the rebound energy is established for the bonding formation of the nano-scale TiO2 particle. The adhesion energy required to the bonding formation between nano-scale ceramic particles can be produced by high velocity collision.

Pressure-Aware Control Layer Optimization for Flow-Based Microfluidic Biochips.

Flow-based microfluidic biochips are attracting increasing attention with successful biomedical applications. One critical issue with flow-based microfluidic biochips is the large number of microvalves that require peripheral control pins. Even using the broadcasting addressing scheme, i.e., one control pin controls multiple microvalves simultaneously, thousands of microvalves would still require hundreds of control prins, which is unrealistic. To address this critical challenge in control scalability, the control-layer multiplexer is introduced to effectively reduce the number of control pins into log scale of the number of microvalves. There are two practical design issues with the control-layer multiplexer: (1) the reliability issue caused by the frequent control-valve switching, and (2) the pressure degradation problem caused by the control-valve switching without pressure refreshing from the pressure source. This paper addresses these two design issues by the proposed Hamming-distance-based switching sequence optimization method and the XOR-based pressure refreshing method. Simulation results demonstrate the effectiveness and efficiency of the proposed methods with an average 77.2% (maximum 89.6%) improvement in total pressure refreshing cost, and an average 88.5% (maximum 90.0%) improvement in pressure deviation.

Nano-porous TiO2 layer using ultrafine nano-particles for the blocking layer in dye-sensitized solar cells.

A nano-porous TiO2 layer was produced by spray-deposition using ultrafine anatase nano-particles for the blocking layer for the dye-sensitized solar cells (DSCs). The microstructure and the electrochemical properties of the spray-deposited TiO2 layer were examined. The results of electrochemical properties showed that the spray-deposited TiO2 layer was capable to suppress the I3- ions diffusion to FTO substrate, reducing the electron recombination between the electrons on FTO substrate and I3- ions in electrolyte. In addition, the connection between TiO2 film and FTO substrate was improved by the TiO2 layer. Therefore, the short circuit current density and thereby the photo-to-electric energy conversion efficiency were improved by this blocking layer. The blocking effect of the porous layer was attributed to both the complicated pore structure of the spray-deposited layer and the enhanced connections between TiO2 film and FTO substrate. The low temperature characteristic of spray deposition approach indicates that it is suitable to the flexible-based DSCs.