Photosonochemical degradation of butyl-paraben: Optimization, toxicity and kinetic studies

The objective of the present work is to evaluate the potential of a photosonolysis process for the degradation of butyl-paraben (BPB). After 120 min of treatment time, high removal of BPB was achieved by the photosonolysis (US/UV) process (88.0 ± 0.65%) compared to the photochemical (UV) and the conventional ultrasonication (US) processes. Several factors such as calorimetric power, treatment time, pH and initial concentration of BPB were investigated. Using a 24 factorial matrix, the treatment time and the calorimetric power are the main parameters influencing the degradation rate of BPB. Subsequently, a central composite design methodology has been investigated to determine the optimal experimental parameters for BPB degradation. The US/UV process applied under optimal operating conditions (at a calorimetric power of 40 W during 120 min and under pH 7) is able to oxidize around 99.2 ± 1.4% of BPB and to record 43.3% of mineralization. During the US/UV process, BPB was mainly transformed into 1 hydroxy BPB, dihydroxy BPB, hydroquinone and 4-hydroxybenzoic acid. Microtox biotests (Vibrio fisheri) showed that the treated effluent was not toxic. The pseudo-first order kinetic model (k = 0.0367 min− 1) described very well the oxidation of BPB

Coupling of the electrochemical oxidation (EO-BDD)/photocatalysis (TiO2-Fe-N) processes for degradation of acid blue BR dye

We report on the successful preparation of Fe-N codoped Titania powders, using TiO2Degussa P25, salt of Fe (II), and Urea. Modified Titania-based materials were characterized by SEM, EDS, BET, Raman, XRD diffraction and diffuse reflectance UV–vis spectroscopy measurements. The doping of TiO2 induced a shift in the absorption threshold toward the spectral range, obtaining catalysts with a greater photoactivity than the one of pure Degussa P25. The degradation of 200 mL of a solution with 50 mg L− 1acid blue BR dye in sulfate medium at pH 3.0 has been comparatively studied by electrochemical oxidation using a boron doped diamond anode (EO-BDD), Photocatalysis TiO2-Fe-N, and coupled material of EO-BDD/Photocatalysis TiO2-Fe-N. The solution was slowly degraded by EO-BDD (25%) and single Photocatalysis TiO2-Fe-N because of the low rate of dye degradation and its colored by-products with hydroxyl radicals generated at the BDD anode and catalyst surface from water oxidation (29%), whereas the solution was more rapidly degraded using coupled material of EO-BDD/Photocatalysis TiO2-Fe-N (82%), owing to the additional generation of hydroxyl radicals from the photocatalysis of TiO2-Fe-N and BDD anode.The authors thank the PRODEP Program (PRODEP-UGTO-PTC-472 and PRODEP 2015 UGTO-PTC-457) of UGTO under the Project 007/ 2015 (Convocatoria Institucional para Fortalecer la Excelencia Académica 2015), and the Project 778/2016 (Convocatoria Institucional de Apoyo a la Investigación Científica 2016-2017) is acknowledged. Authors thank Guanajuato University-CONACYT National Laboratory for SEM-EDX analysis. Financial support from the Spanish Ministry of Economy and Competitiveness in projects CTM2015-69845- R and CTQ2015-66078-R (MINECO/FEDER, UE) is gratefully acknowledged. C. J. Escudero thanks CONACYT-CONCYTEG for the postgraduate research grant (230713/383108) from Mexico

Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

International audienceMelanoma is considered as one of the fatal cancer in the world, this form of skin cancer may spread to other parts of the body in case that it has not been diagnosed in an early stage. Thus, the medical field has known a great evolution with the use of automated diagnosis systems that can help doctors and even normal people to determine a certain kind of disease. In this matter, we introduce a hybrid method for melanoma skin cancer detection that can be used to examine any suspicious lesion. Our proposed system rely on the prediction of three different methods: A convolutional neural network and two classical machine learning classifiers trained with a set of features describing the borders, texture and the color of a skin lesion. These methods are then combined to improve their performances using majority voting. The experiments have shown that using the three methods together, gives the highest accuracy level

Modified TiO₂ For Environmental Photocatalytic Applications: A Review

This paper summarizes recent research dealing with development of titanium dioxide (TiO₂) used for environmental applications. TiO₂ plays the most important role owing to its excellent chemical and physical properties. However, the TiO₂ band edge lies in the UV region that makes them inactive under visible irradiation. In this regard, considerable efforts have been made to increase the visible light activity of TiO₂ via the modification of its electronic and optical properties. Doping TiO₂ using either anions or cations is one of the typical approaches that has been largely applied. Coupling TiO₂ with a narrow bad gap semiconductor (M_<i>x</i>O_<i>y</i>/TiO₂ or M_<i>x</i>S_<i>y</i>/TiO₂) represents another approach. This work aims to encompass the new progress of TiO₂ for an efficient application in water and wastewater treatment under visible light, emphasizes the future trends of TiO₂ in the environment, and suggests new research directions, including preparation aspects for the development of this promising material