The use of antibiotics and dyes has resulted in severe water pollution and health risks; therefore, it is urgent to remove them from water sources. Among the most common methods for removing harmful water contaminants, adsorption and photodegradation are the most economical, simple, and reusable. Due to their high porosity, adjustability, and crystal structure, metal-organic frameworks (MOFs) are one of the effective adsorbents and photocatalysts. A typical MOF material is zeolitic imidazolate framework-8/67 (ZIF-8 and ZIF-67), comprising essentially of the metal atoms Zn and 2-methylimidazole (2-MIM). ZIF-8 and ZIF-67 have unique properties that make them efficient in water treatment due to high adsorption capacities and being good hosts for photocatalytic materials. In this article, a review study of the design and methods of synthesis of ZIF-8 and ZIF-67 composites is presented. An introduction to the current research on the role of ZIF-8 and ZIF-67 compounds as adsorbents and photocatalysts for wastewater pollution removal is provided. In this review study, we aim to supply a mechanistic perspective on the use of ZIF-8/67 composites in wastewater purification and present novel visions for the development of extremely effective ZIF-8/67-based adsorbents and photocatalysts. To unlock the full potential of ZIF-8/67 composites in dye and antibiotic removal and water recycling, current difficulties will be discussed in detail.

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Effect of Metal Atom in Zeolitic Imidazolate Frameworks (ZIF-8 & 67) for Removal of Dyes and Antibiotics from Wastewater: A Review

The increase in pollutants such as hazardous refractory contaminants, organic dyes, pharmaceuticals, and pesticides entering water resources on a large scale due to global population growth and industrialization has become a significant health concern worldwide. The two-dimensional (2D) MXene material is a new type of transition metal carbide or carbonitride material, which has demonstrated the capability to adsorb various heavy contaminants, particularly metals such as chromium, copper, lead, and mercury. In addition, MXenes have a tunable band gap (0.92–1.75 eV) and exhibit good thermal stability and considerable damage resistance, which means that they are well suited as adsorbents for waste removal. In this review article, MXene nanocomposites are introduced for the removal of pollutants from water. The idea of water remediation, the applications of MXene-based nanocomposites, and the effects on the degradation of water and wastewater contaminants are reviewed. Future trends in MXene-based nanocomposites for water treatment and environmental applications will also be discussed.

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Recent Advances in MXene-Based Nanocomposites for Wastewater Purification and Water Treatment: A Review

Fine-grained classification remains a challenging task because distinguishing categories needs learning complex and local differences. Diversity in the pose, scale, and position of objects in an image makes the problem even more difficult. Although the recent Vision Transformer models achieve high performance, they need an extensive volume of input data. To encounter this problem, we made the best use of GAN-based data augmentation to generate extra dataset instances. Oxford-IIIT Pets was our dataset of choice for this experiment. It consists of 37 breeds of cats and dogs with variations in scale, poses, and lighting, which intensifies the difficulty of the classification task. Furthermore, we enhanced the performance of the recent Generative Adversarial Network (GAN), StyleGAN2-ADA model to generate more realistic images while preventing overfitting to the training set. We did this by training a customized version of MobileNetV2 to predict animal facial landmarks; then, we cropped images accordingly. Lastly, we combined the synthetic images with the original dataset and compared our proposed method with standard GANs augmentation and no augmentation with different subsets of training data. We validated our work by evaluating the accuracy of fine-grained image classification on the recent Vision Transformer (ViT) Model.

Hamid Bahador

Papers

Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection.