Photo-fenton degradation of pentachlorophenol l: competition between additives and photolysis

[EN] In the present work, the photo-Fenton degradation of pentachlorophenol (PCP, 1 mg/L) has been studied under simulated and natural solar irradiation; moreover, the effect on the process efficiency of urban waste-derived soluble bio-based substances (SBO), structurally comparable to humic acids, has been investigated. Experiments showed a crucial role of PCP photolysis, present in the solar pilot plant and hindered by the Pyrex (R) filter present in the solar simulator. Indeed, the SBO screen negatively affects PCP degradation when working under natural solar light, where the photolysis of PCP is relevant. In contrast, in the absence of PCP photolysis, a significant improvement of the photo-Fenton process was observed when added to SBO. Furthermore, SBO were able to extend the application of the photo-Fenton process at circumneutral pH values, due to their ability to complex iron, avoiding its precipitation as oxides or hydroxides. This positive effect has been observed at higher concentration of Fe(II) (4 mg/L), whereas at 1 mg/L, the degradation rates of PCP were comparable in the presence and absence of SBO.This work was realized with the financial support of the academic interchange from the Marie Sklodowska-Curie Research and Innovation Staff Exchange project, funded by the European Commission H2020-MSCA-RISE-2014 within the framework of the research project Mat4treaT (Project number: 645551).Vergura, EP.; García-Ballesteros, S.; Vercher Pérez, RF.; Santos-Juanes Jordá, L.; Bianco Prevot, A.; Arqués Sanz, A. (2019). Photo-Fenton Degradation of Pentachlorophenol: Competition between Additives and Photolysis. Nanomaterials. 9(8):1-8. https://doi.org/10.3390/nano9081157S189

Role of molecular imaging in the management of patients affected by inflammatory bowel disease: State-of-the-art

To present the current state-of-the art of molecular imaging in the management of patients affected by inflammatory bowel disease (IBD)

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods