Application of Photocatalytic Processes for Water Treatment

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Facultad de Ciencias, Departamento de Ingeniería Química. Fecha de lectura: 05-07-2019El trabajo de esta Tesis ha sido financiado a través de los proyectos CTM2015-64895-R y CTM2016-76454-R del Ministerio de Economía y Competitividad

New trends on photoelectrocatalysis (PEC):nanomaterials, wastewater treatment and hydrogen generation

The need for novel water treatment technologies has been recently recognised as concerning contaminants (organics and pathogens) are resilient to standard technologies. Advanced oxidation processes degrade organics and inactivate microorganisms via generated reactive oxygen species (ROS). Among them, heterogeneous photocatalysis may have reduced efficiency due to, fast electron-hole pair recombination in the photoexcited semiconductor and reduced effective surface area of immobilised photocatalysts. To overcome these, the process can be electrically assisted by using an external bias, an electrically conductive support for the photocatalyst connected to a counter electrode, this is known as photoelectrocatalysis (PEC). Compared to photocatalysis, PEC increases the efficiency of the generation of ROS due to the prevention of charge recombination between photogenerated electron-hole pairs thanks the electrical bias applied. This review presents recent trends, challenges, nanomaterials and different water applications of PEC (degradation of organic pollutants, disinfection and generation of hydrogen from wastewater)

Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database with Automatic Labeling

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: $i$ ) a comprehensive survey of segmentation algorithms for AV; $ii$ ) an Egocentric Arm Segmentation Dataset (EgoArm), composed of more than 10, 000 images, demographically inclusive (variations of skin color, and gender), and open for research purposes. We also provide all details required for the automated generation of groundtruth and semi-synthetic images; $iii$ ) the proposal of a deep learning network to segment arms in AV; $iv$ ) a detailed quantitative and qualitative evaluation to showcase the usefulness of the deep network and EgoArm dataset, reporting results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches using color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvements up to 40% with respect to the baseline network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work under controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), deep learning is more robust in real AV application

Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database With Automatic Labeling

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: $i$ ) a comprehensive survey of segmentation algorithms for AV; $ii$ ) an Egocentric Arm Segmentation Dataset (EgoArm), composed of more than 10, 000 images, demographically inclusive (variations of skin color, and gender), and open for research purposes. We also provide all details required for the automated generation of groundtruth and semi-synthetic images; $iii$ ) the proposal of a deep learning network to segment arms in AV; $iv$ ) a detailed quantitative and qualitative evaluation to showcase the usefulness of the deep network and EgoArm dataset, reporting results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches using color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvements up to 40% with respect to the baseline network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work under controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), deep learning is more robust in real AV application

Solar photoelectrocatalytic oxidation of urea in water coupled to green hydrogen production

In past decades, the intensification of human activities has led to an increase in pollution and energy demand. Photoelectrochemical systems have emerged as an alternative for the decentralized management of domestic wastewater with the potential of recovering energy while degrading pollutants such as urea. Tungsten oxide (WO3) has been traditionally used for water splitting, but the use of this material for the removal of waste from water coupled to hydrogen production is not deeply known until now. This contribution shows an exhaustive and systematic investigation on WO3 photoanodes for the photoelectrochemical oxidation of urea and the generation of hydrogen, with insights on the reaction mechanism, detailed nitrogen balance investigation of the process, and analysis of the performance compared to well-accepted materials. The WO3 platelets were successfully synthesized in situ on fluorine doped tin oxide glass by a hydrothermal method. The performance of WO3 was compared to titanium dioxide (TiO2) as a benchmark. The photocurrent was enhanced for both electrodes when urea was added to the electrolyte, with WO3 showing one order of magnitude higher photocurrent than TiO2. The WO3 electrode showed a peak incident photon-to-current efficiency of 43% at 360 nm and a much greater rate constant for urea oxidation (1.47 × 10−2 min−1), compared to the TiO2 photoanode (16% at 340 nm and 1.1 × 10−3 min−1). The influence of different reactor configurations was also evaluated testing one- and two-compartment back-face irradiated photoelectrochemical cells. Hydrogen was generated with a Faradaic efficiency of 87.3% and a solar-to-hydrogen conversion efficiency of 1.1%. These findings aim to contribute to the development of technologies based on the photoelectrochemical production of hydrogen coupled with the oxidation of pollutants in wastewater

Dataset of paper "Solar photoelectrocatalytic oxidation of urea in water coupled to green hydrogen production"

Dataset of paper "Solar photoelectrocatalytic oxidation of urea in water coupled to green hydrogen production" Material characterization for TiO2 and WO3 electrodes. Photoelectrochemical characterization for TiO2 and WO3. One compartment cell characterization. Urea oxidation experiments for TiO2 and WO3 electrodes. Urea oxidation and products using one compartment cell. Production of NO2- during urea oxidation. Evolution of NO3− oxidation in time and conversion to NH4+. Two compartment cell characterization. Urea oxidation and products using two-compartment cell

Highly active and stable OER electrocatalysts derived from Sr2MIrO6 for proton exchange membrane water electrolyzers

Proton exchange membrane water electrolysis is a promising technology to produce green hydrogen from renewables, as it can efficiently achieve high current densities. Lowering iridium amount in oxygen evolution reaction electrocatalysts is critical for achieving cost-effective production of green hydrogen. In this work, we develop catalysts from Ir double perovskites. Sr2CaIrO6 achieves 10 mA cm−2 at only 1.48 V. The surface of the perovskite reconstructs when immersed in an acidic electrolyte and during the first catalytic cycles, resulting in a stable surface conformed by short-range order edge-sharing IrO6 octahedra arranged in an open structure responsible for the high performance. A proton exchange membrane water electrolysis cell is developed with Sr2CaIrO6 as anode and low Ir loading (0.4 mgIr cm−2). The cell achieves 2.40 V at 6 A cm−2 (overload) and no loss in performance at a constant 2 A cm−2 (nominal load). Thus, reducing Ir use without compromising efficiency and lifetime.Ministry of Education of Saudi Arabiay MCIN/ AEI/10.13039/501100011033MCIN/AEI/10.13039/501100011033Depto. de Química InorgánicaFac. de Ciencias QuímicasTRUEpu