scholarly journals Defect levels coupled with oscillations and frustrated spins by Fe+3 in co-precipitated Aluminum ferrites

2022 ◽  
Author(s):  
Ritambhara Dash ◽  
Kumar Gaurav ◽  
Neha Kumari ◽  
Prashant Kumar ◽  
Saurabh Ranjan ◽  
...  
Keyword(s):  
Magnetic Property ◽  
Physicochemical Properties ◽  
Metal Oxides ◽  
Photocatalytic Degradation ◽  
Significant Role ◽  
Large Scale ◽  
Organic Dyes ◽  
Reaction System ◽  
Fine Tuning ◽  
Ion Concentration

Abstract The degradation of organic dyes determines the suitability of a photocatalyst for wastewater treatments. Metal oxides like TiO2, ZnO, CuO, Fe2O3, Ce2O3, and Al-doped Ni Ferrites can degrade dyes. However, fine-tuning of physicochemical properties of the reaction system and characteristics of the reactor plays a significant role in making photocatalytic degradation a large-scale activity. The photoactivity gets altered by altering the Fe+3 ion concentration. The mechanism behind such changes has been addressed here, along with a unique magnetic property of frustrated spins observed.

scholarly journals Recent Advances on Plasmon-enhanced Titania Nanocatalysts for Photocatalytic Degradation of Organic Dyes

2020 ◽  
Vol 3 (3) ◽  
pp. 117
Author(s):  
Munawar Khalil ◽  
Rendy Muhamad Iqbal ◽  
Grandprix T.M. Kadja ◽  
Dede Djuhana
Keyword(s):  
Water Pollution ◽  
Photocatalytic Degradation ◽  
Industrial Wastewater ◽  
Large Scale ◽  
Recent Progress ◽  
Organic Dyes ◽  
Plasmonic Nanoparticles ◽  
The Past ◽  
Excellent Activity ◽  
Different Types

In the past several years, solar-driven photocatalytic degradation of organic dyes has been considered as one of the most promising and effective ways to address water pollution issues. Nevertheless, the implementation of such technology for large scale industrial wastewater application is still hampered by the limitation in currently used photocatalysts. Recently, plasmon-enhanced titania-based nanocatalyst has emerged as one of the promising photocatalytic materials for solar-driven wastewater treatment due to its excellent activity and ability to absorb a large portion of solar radiation. Therefore, this review highlights recent progress on applying such material for the photodegradation of organic dyes. In this review, the focus is placed on several mechanisms on how the surface plasmon resonance (SPR) phenomenon could enhance the photocatalytic activity of semiconductors, such as TiO2. Furthermore, the performance of several types of plasmon-enhanced titania nanocatalyst with different kinds of metal plasmonic nanoparticles, i.e., Au-TiO2, Ag-TiO2, and Pd-TiO2, is also compared and comprehensively discussed. Finally, a particular emphasis is also given to highlight the nanocatalysts' kinetics in facilitating the photocatalytic degradation of different types of organic dyes.

Structural transformation in Bi2O3 and its application for photocatalytic degradation of organic dyes

2020 ◽  
Author(s):  
Kaviyapriya Kirubanithy ◽  
Jayaraj Santhosh Kumar ◽  
Rosalin Beura ◽  
Paramasivam Thangadurai
Keyword(s):  
Photocatalytic Degradation ◽  
Structural Transformation ◽  
Organic Dyes

Rapid photocatalytic degradation of cationic organic dyes using Li-doped Ni/NiO nanocomposites and their electrochemical performance

2021 ◽  
Author(s):  
R. Ranjitha ◽  
K. N. Meghana ◽  
V. G. Dileep Kumar ◽  
Aarti S. Bhatt ◽  
B. K. Jayanna ◽  
...  
Keyword(s):  
Energy Storage ◽  
Water Treatment ◽  
Electrochemical Performance ◽  
Photocatalytic Degradation ◽  
Organic Dyes

This work reports novel bi-functional Li-doped Ni/NiO nanocomposites as potential candidates for energy storage and water treatment applications.

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

Photocatalytic degradation of organic dyes by Er3+: YAlO3/Co- and Fe-doped ZnO coated composites under solar irradiation

2016 ◽  
Vol 90 (13) ◽  
pp. 2654-2664 ◽  
Author(s):  
Yang Chen ◽  
Chunxiao Lu ◽  
Liang Tang ◽  
Yahui Song ◽  
Shengnan Wei ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Organic Dyes ◽  
Solar Irradiation ◽  
Doped Zno

Synthesis of N-doped ZnO/ZnCo2O4 composites for stable photocatalytic degradation of organic dyes

2021 ◽  
Vol 217 ◽  
pp. 411-421
Author(s):  
Lukai Liu ◽  
Guoqing Zhao ◽  
Caifeng Li ◽  
Shu Zhou ◽  
Yinke Wang ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Organic Dyes ◽  
Doped Zno
Sign in / Sign up

Export Citation Format

Share Document