Traditional chemical, physical and biological processes for treating wastewater containing textile dye have such disadvantages as high cost, high energy requirement and generation of secondary pollution during treatment process. The advanced oxidation processes technology has been attracting growing attention for the decomposition of organic dyes. Such processes are based on the light-enhanced generation of highly reactive hydroxyl radicals, which oxidize the organic matter in solution and convert it completely into water, CO 2 and inorganic compounds. In this presentation, the photocatalytic degradation of dyes in aqueous solution using TiO 2 as photocatalyst under solar and UV irradiation has been reviewed. It is observed that the degradation of dyes depends on several parameters such as pH, catalyst concentration, substrate concentration and the presence of oxidants. Reaction temperature and the intensity of light also affect the degradation of dyes. Particle size, BETsurface area and different mineral forms of TiO 2 also have influence on the degradation rate.

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Abstract-The photocatalytic degradation of Methylene Blue (MB) dye has been investigated by locally available Magnetite with H 2 O 2 . Parameters such as Magnetite dosage, concentration of dye pH effect, light intensity and oxidizing agent (H 2 O 2 ) were used to study the degradation of MB. The degradation rates were found to be strongly influenced by all the above parameters. The Magnetite/ H 2 O 2 /UV process proved to be capable of decolorizing Methylene Blue.Index Terms-Photocatalytic degradation, magnetite, oxidizing agent, methylene blue. I. INTRODUCTIONAt present, 100000 different types of dyes with annual production rate of 7 × 10 5 tons are produced. Among them textile industries consume about 36000 ton/year dye. Up to 20% of the total world production of dyes is lost during the dyeing process and is released in the textile effluents [1], [2]. These synthetic dyes can cause considerable environmental pollution and are injurious to health due to their stability and toxicity. A wide range of physical methods has been developed for the removal of synthetic dyes. Conventional water treatment technologies such as physical methods mainly provide a phase transter of the contaminants from waste water to solid waste which require further treatments. On the other hand chemical oxidation methods include a family of processes that may be appropriate for treating dye pollutants. Advanced oxidation processes are promising technologies which aim at the decolorization and mineralization of a wide range of dyes and transform dyes into biodegradable or harmless products [3]. Photo-Fenton's oxidation is a homogeneous catalytic oxidation process using a mixture of hydrogen peroxide and ferrous ions. The addition of UV or artificial light to Fenton's process accelerate dye decolorization as it influences the direct formation of Magnetite (Fe 3 O 4 ) is a semimetal semiconductor with bandgap 0.14 eV. It contains both the Fe 2+ and Fe 3+ cations. So, it is expected that Magnitite will work as a photocatalyst. The possibility of using powder magnetite adsorption-Fenton oxidation as a method for removal of azo dye acid red B from water was studied by Rongcheng and Jiuhui [5]. Madrakian et al. [6] in their studies used magnetite nanoparticlesmodified low-cost activated carbon (MMAC) as an adsorbent for the removal of the anionic and cationic dyes Congo red, reactive blue 19, thionine, janus green B, methylthymol blue and mordant Blue 29, from aqueous solutions. Utilization of magnetite nanoparticles modified with Cetyltrimethylammonium bromide (CTAB) for removal of Nyloset Yellow E-RK dye from water and waste water by magnetic force was also investigated by Dalali et al. [7].The main objective of this study was to study the feasibility of Magnetite as a photocatalyst for decolorization of Methylene Blue dye. The influence of different operational parameters such as, Magnetite dosage, H 2 O 2 dosage, pH, dye concentration, temperature, light source and the presence of salt which affect the efficiency of Photo-Fenton's reaction...

Background Cellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach. Objectives The purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy. Materials and methods In this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22. Results A combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%. Conclusions The implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM). Keywords Cellulite. Artificial intelligence. Infrared thermography. Predictive preventive personalized medicine. Prediction and health monitoring Abbreviations ANN Artificial neural network AUC Area under the curve BRISK Binary robust invariant scalable keypoints Cont Contour-based feature DT Decision tree Electronic supplementary material The online version of this article (

Graphene-based polymer composites are gaining interest as a modish class of substance that holds promising angles on diverse applications. In this work, Graphene Oxide (GO) based Polyvinyl Alcohol (PVA) nanocomposites (PVA-GO) have been prepared by employing a facile solution casting method. Low concentrations of GO nanofiller (0.25%, 0.50%, 0.75%, and 1.0%) were used and the result of the use of them over the distinct substantial characteristics of the nanocomposites was evaluated. The different features of the as-synthesized nanocomposites such as optical, structural, chemical, and thermal properties were identified by UV-Vis spectroscopy, X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), and Thermo-gravimetric analysis (TGA), respectively. From the structural analysis of the crystallinity of the nanocomposite it is evident that a reduction in crystallinity caused by the amalgamation of the GO nanofiller. FTIR study shows improved interaction between the GO nanofiller and PVA matrix. The incorporation of GO was found to reduce the optical band gap of the nanocomposite both for the direct and indirect transition. The Urbach energy of the nanocomposite increases with the increase of the GO concentration suggests the formation of localized states causing a reduction in the optical band gap. PVA-GO nanocomposites with improved and tunable physical properties synthesized from a simple and economic route may pave a new horizon for polymer-based optoelectronic devices.

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