Microfluidic Emulsification Techniques for Controllable Emulsion Production and Functional Microparticle Synthesis

The preparation of metal organic frameworks (MOFs) has come to the forefront in recent years because of their outstanding physical and chemical properties. Many MOFs such as Zn, Co, Ni, Fe, and Ag, etc., have been successfully synthesized. In this work, we followed the solvothermal assisted route to synthesize Ag-MOF (abbreviated as AMOF) nanosheets and then applied them as a photocatalyst to remove different organic pollutants, namely methyl orange (MO), crystal violet (CV), and methylene blue (MB). Chemical composition, optical properties, morphology, and microstructural analysis were analyzed using XPS, UV-visible spectrophotometer, FESEM, TEM, and EDS, respectively. The structural properties of AMOF nanosheets were studied by X-ray diffraction (XRD). Nitrogen adsorption and desorption isotherm analysis were utilized to evaluate the specific surface area and pore size of the AMOF nanosheets. Further, AMOF nanosheets showed notable photocatalytic performance for various dye pollutants degradation. The results confirmed 74.5, 85.5, and 90.7% of MO, CV, and MB dye pollutants removal after 120 min of irradiation with the rate constants (k) of 0.0123, 0.0153, and 0.0158 min−1, respectively. The effect of superoxide radicals (O2−) and photogenerated holes (h+) on the organic dye pollutants removal was investigated using radical scavenger trapping studies. Moreover, the stability study also confirmed the recyclability of the photocatalyst. Therefore, the findings of this research present a realizable method to grow AMOF photocatalyst for successful degradation of various dye pollutants.

https://www.mdpi.com/2079-4991/13/2/336/pdf?version=1673605545

Development of MOF Based Recyclable Photocatalyst for the Removal of Different Organic Dye Pollutants

Organic dye contaminants pose increasing threats to water ecological systems and global sustainability. Rational development of functional particles with adsorption-catalysis synergy is envisioned as a powerful route for enhanced water remediation. Here, hierarchical porous MOF-integrated alginate particles with synergized adsorption and photocatalytic degradation is developed for effective water remediation. The particles exhibit unique mesh-like Ca-alginate (CaAlg) networks with incorporated macro-/micro-porous zeolitic imidazolate framework-8 (ZIF-8) nanoparticles, both modified with negatively-charged tannic acid (TA). The TA-modified mesh-like CaAlg (CaAlg@TA) networks serve as highly porous absorbents, while the TA-modified ZIF-8 (ZIF-8@TA) nanoparticles serve as both hierarchical porous nano-absorbents and photoactive nano-catalysts. This combination simultaneously harnesses the advantages of CaAlg@TA networks and ZIF-8@TA nanoparticles to achieve adsorption-photocatalysis synergy for efficient water remediation. As demonstrated by removal of toxic and carcinogenic dye methylene blue (MB), these particles show much higher performances with fast adsorption and high capacity (1277 mg g−1), as compared to control group particles without adsorption-photocatalysis synergy. Meanwhile, the adsorption-photocatalysis synergy largely suppresses undesired secondary water contamination from the MB-adsorbed particles, and benefits particle regeneration to achieve good reusability for repeated MB removal (>95.5 % for 10 cycles). This work provides a simple and flexible route for rational fabrication of MOF-integrated particles with adsorption-photocatalysis synergy, and thus may open up new horizons for developing advanced functional particles for effective water remediation. 

Hierarchical porous tannic-acid-modified MOFs/alginate particles with synergized adsorption-photocatalysis for water remediation

Abstract Metal foam flow field shows great potential for next‐generation proton exchange membrane (PEM) fuel cell of high power density due to its well‐connected pore structure, high thermal and electrical conductivity. However, the complicated pore structure makes it a challenge for water management. To tackle this issue, a novel design of metal foam flow field with hierarchical pore structure was proposed. Based on lattice Boltzmann method (LBM), the structure‐performance relationship between hierarchical pore structure and water discharge capability of flow field was explored by using breakthrough time. Furthermore, an optimal hierarchical pore structure for metal foam flow field that shows superior water discharge capability was obtained. Compared with metal foam with uniform coarse pore structure, breakthrough time can be reduced roughly by 17.6% in the one with optimal hierarchical pore structures. This finding provides a theoretical foundation and technical guidance for developing metal foam flow field. 

Liquid water discharge capability enhancement of hierarchical pore structure in metal foam flow field of proton exchange membrane fuel cell

The quality control of discretely manufactured parts typically involves defect recognition activities, which are time-consuming, repetitive tasks that must be performed by highly trained and/or experienced personnel. However, in the context of the fourth industrial revolution, the pertinent goal is to automate such procedures in order to improve their accuracy and consistency, while at the same time enabling their application in near real-time. In this light, the present paper examines the applicability of popular deep neural network types, which are widely employed for object detection tasks, in recognizing surface defects of parts that are produced through a die-casting process. The data used to train the networks belong to two different datasets consisting of images that contain various types of surface defects and for two different types of parts. The first dataset is freely available and concerns pump impellers, while the second dataset has been created during the present study and concerns an automotive part. For the first dataset, Faster R-CNN and YOLOv5 detection networks were employed yielding satisfactory detection of the various surface defects, with mean average precision (mAP) equal to 0.77 and 0.65, respectively. Subsequently, using transfer learning, two additional detection networks of the same type were trained for application on the second dataset, which included considerably fewer images, achieving sufficient detection capabilities. Specifically, Faster R-CNN achieved mAP equal to 0.70, outperforming the corresponding mAP of YOLOv5 that equalled 0.60. At the same time, experiments were carried out on four different computational resources so as to investigate their performance in terms of inference times and consumed power and draw conclusions regarding the feasibility of making predictions in real time. The results show that total inference time varied from 0.82 to 6.61 s per image, depending on the computational resource used, indicating that this methodology can be integrated in a real-life industrial manufacturing system.

https://www.mdpi.com/2075-4701/13/6/1104/pdf?version=1686811484

Defect Recognition in High-Pressure Die-Casting Parts Using Neural Networks and Transfer Learning

In the production process of metal castings, certain defects are easy to appear on the surface. Before the castings are put into use, it is necessary to detect whether there are defects on the surface. In this paper, the defect detection of metal casting surface is taken as the research object, and the Centernet deep learning model is used to recognize the defect of casting surface image. Centernet does not use Region Proposal Network (RPN) and Non-Maximum Suppression (NMS) in the training process. It has the advantages of simple model, good detection effect, fast running speed and easy integration and deployment. By predicting the category heat map, the length and width map of the detection frame and the center offset of the detection frame with the same image size as the input model, the defect target can be extracted. In this paper, the image dataset is expanded by cropping, rotating, changing brightness and contrast of the images. For the pit and scratch defects of metal castings, the Mean Average Precision (mAP) is higher than 0.9 for the metal casting defect image dataset, and the software integration of the model is completed.

Surface defect detection of metal castings based on Centernet

Hierarchical porous metal-organic frameworks/polymer microparticles for enhanced catalytic degradation of organic contaminants

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Surface defect detection of metal castings based on Centernet

Hierarchical porous metal-organic frameworks/polymer microparticles for enhanced catalytic degradation of organic contaminants