Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Study on the removal of heavy metal ions from industry waste by carbon nanotubes: Effect of the surface modification: a review

Abstract: Removal of noxious materials such as heavy metal ions (which are hazardous above certain ppm concentration) from wastewater is one of the biggest environmental challenges that suffers the economy nowadays. On the basis of their versatility, environmental friendliness, the adsorption was proved to be a most economical and efficient technology, which is used extensively for their removal from the aqueous media. Among the various developed adsorbents used so far, carbon nanotubes (CNTs) show a unique impact on the fast adsorption and rapid removal of noxious impurities from the aqueous source. CNTs festooned on the sources like activated carbon, nanoparticles, and nanocomposities enhanced the efficiency and potential of the adsorbent. Due to their unique structural, electronic, optoelectronic, semiconductor, as well as mechanical, chemical, and physical properties, they have been extensively used to remove heavy metals in wastewater treatment. The adsorption mechanisms are majorly contributed by the ...

CuO/Cu(OH) 2 hierarchical nanostructures as bactericidal photocatalysts

Abstract: Various morphologies of CuO/Cu(OH)2 nanostructures with different adsorbed –OH contents were synthesized on an acid-treated Cu foil through variation of NaOH concentration from 0 to 50 mM with an in situoxidation method. X-ray diffractometry and X-ray photoelectron spectroscopy (XPS) indicated formation of CuO on the Cu(OH)2 crystalline phase at a growth temperature of 60 °C for 20 h. Antibacterial activity of the nanostructures against Escherichia coli bacteria was studied in the dark and under light irradiation. The nanostructures grown at a NaOH concentration of 30 mM showed the highest surface area and the strongest antibacterial activity among the samples. After elimination of the contribution of the effective surface area of the nanostructures to the antibacterial activity, it was found that the surface morphology and chemical composition of the nanostructures were the other most important parameters in the antibacterial activity of the nanostructures. Using XPS analysis, the better photocatalytic activity per surface area of the nanostructures prepared at higher NaOH concentrations was substantially attributed to the amount of adsorbed OH bonds on the surface of the nanostructures.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.