RESUMEN
This study aims to deepen knowledge of the biodegradation of plastics, focusing on polypropylene (PP) fabric from surgical masks and polystyrene (PS) by larvae of Zophobas atratus as well as of specialized bacterial consortia from their gut, which were obtained in different enrichment conditions (aerobic, anaerobic, presence or absence of combined nitrogen). Plastics ingested by larvae obtained in Spain did not show any signs of oxidation but only limited depolymerization, preferably from the lowest molecular weight chains. Gut microbiota composition changed as an effect of plastic feeding. Such differences were more evident in bacterial enrichment cultures, where the polymer type influenced the composition more than by culture conditions, with an increase in the presence of nitrogen-fixers in anaerobic conditions. PS and PP degradation by different enrichment cultures was confirmed under aerobic and anaerobic conditions by respirometry tests, with anaerobic conditions favouring a more active plastic degradation. In addition, exposure to selected bacterial consortia in aerobiosis induced limited surface oxidation of PS. This possibly indicates that different biochemical routes are being utilized in the anaerobic gut and in aerobic conditions to degrade the polymer.
RESUMEN
Until recently, recycling thermoset polyurethanes (PUs) was limited to degrading methods. The development of covalent adaptable networks (CANs), to which PUs can be assigned, has opened novel possibilities for actual recycling. Most efforts in this area have been directed toward inventing new materials that can benefit from CAN theory; presently, little or nothing has been applied to industrially producible materials. In this study, both an industrially available polyol (Sovermol780®) and isocyanate (Tolonate X FLO 100®) with percentages of bioderived components were employed, resulting in a potentially scalable and industrially producible material. The resultant network could be reworked up to three times, maintaining the crosslinked structure without significantly changing the thermal properties. Improvements in mechanical parameters were observed when comparing the pristine material to the material exposed to three rework processes, with gains of roughly 50% in elongation at break and 20% in tensile strength despite a 25% decrease in Young's modulus and crosslink density. Thus, it was demonstrated that theory may be profitably applied even to materials that are not designed including additional bonds but instead rely just on the dynamic urethane bond that is naturally present in the network.
RESUMEN
Maltodextrins are products of starch hydrolysis that can be processed into dry fibres through electrospinning and subsequently cured via mild thermal treatment to obtain nonwoven cross-linked polysaccharide-based mats. The sustainability of the process and the bioderived nature make this class of materials suitable candidates to be studied as renewable sorbents for the removal of contaminants from water. In this work, electrospinning of water solutions containing 50% wt. of commercial maltodextrin (Glucidex 2®) and 16.6% wt. of citric acid was carried out at 1.2 mL/h flow and 30 kV applied voltage, followed by thermal curing at 180 °C of the dry fibres produced to obtain cross-linked mats. Well-defined fibres with a mean diameter of 1.64 ± 0.35 µm were successfully obtained and characterised by scanning electron microscopy, thermogravimetric analysis, and attenuated total reflectance Fourier transform infrared spectroscopy. Afterwards, a series of sorption tests were conducted to evaluate the effectiveness of the mats in removing atenolol from water. The results of the batch tests followed by HPLC-UV/Vis showed high sorption rates, with over 90% of the atenolol removed, and a maximum removal capacity of 7 mg/g. Furthermore, continuous fixed-bed sorption tests proved the positive interaction between the polymers and atenolol.
RESUMEN
Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021-2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995-2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995-2020 based on historical data, and risk maps for each quarter of the period 2021-2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.
