The effect of preliminary orientation on environmental crazing of high-density polyethylene films

Hydrophilization of Hydrophobic Mesoporous High-Density Polyethylene Membranes via Ozonation

This work addresses hydrophilization of hydrophobic mesoporous membranes based on high-density polyethylene (HDPE) via ozonation. Mesoporous HDPE membranes were prepared by intercrystallite environmental crazing. Porosity was 50%, and pore dimensions were below 10 nm. Contact angle of mesoporous membranes increases from 96° (pristine HDPE) to 120° due to the formation of nano/microscale surface relief and enhanced surface roughness. The membranes are impermeable to water (water entry threshold is 250 bar). The prepared membranes were exposed to ozonation and showed a high ozone uptake. After ozonation, the membranes were studied by different physicochemical methods, including DSC, AFM, FTIR spectroscopy, etc. Due to ozonation, wettability of the membranes was improved: their contact angle decreased from 120° down to 60°, and they became permeable to water. AFM micrographs revealed a marked smoothening of the surface relief, and the FTIR spectra indicated the development of new functionalities due to ozonolysis. Both factors contribute to hydrophilization and water permeability of the ozonated HDPE membranes. Hence, ozonation was proved to be a facile and efficient instrument for surface modification of hydrophobic mesoporous HDPE membranes and can also provide their efficient sterilization for biomedical purposes and water treatment.

Adaptive Peak Environmental Density Clustering Algorithm in Cloud Computing Technology

In order to get sparsity clustering ability of unbalanced cloud data set, combined with adaptive environment density screening, data clustering was carried out, and an improved adaptive environment density peak clustering algorithm under cloud computing technology was proposed. The storage structure model of grid sparse unbalanced cloud data set is constructed, and structure of grid sparse unbalanced cloud data set is reconstructed by combining feature space reconstruction technology. Rough feature quantity of grid sparse unbalanced cloud data set is extracted, and feature extraction and registration are carried out through strict feature registration method. Cloud fusion and peak feature clustering were carried out according to the grid block distribution of the data set. Peak feature quantities of the grid sparse unbalanced cloud data set were extracted, and binary semantic feature distributed detection of the data was carried out.

Mesoporous Membrane Materials Based on Ultra-High-Molecular-Weight Polyethylene: From Synthesis to Applied Aspects

The development of new porous polymeric materials with nanoscale pore dimensions and controlled morphology presents a challenging problem of modern materials and membrane science, which should be based on scientifically justified approaches with the emphasis on ecological issues. This work offers a facile and sustainable strategy allowing preparation of porous nanostructured materials based on ultra-high-molecular-weight polyethylene (UHMWPE) via the mechanism of environmental intercrystallite crazing and their detailed characterization by diverse physicochemical methods, including SEM, TEM, AFM, liquid and gas permeability, DSC, etc. The resultant porous UHMWPE materials are characterized by high porosity (up to ~45%), pore interconnectivity, nanoscale pore dimensions (below 10 nm), high water vapor permeability [1700 g/(m² × day)] and high gas permeability (the Gurley number ~300 s), selectivity, and good mechanical properties. The applied benefits of the advanced UHMWPE mesoporous materials as efficient membranes, breathable, waterproof, and insulating materials, light-weight materials with reduced density, gas capture and storage systems, porous substrates and scaffolds are discussed.