Maria José Ferreira

Polymer single-screw extrusion is a major industrial processing technique used to obtain plastic products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficient extruder screws and decide whether a conventional screw will perform the job efficiently, or a barrier screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. The processing of two polymers, low-density polyethylene and polypropylene, is analyzed. A methodology based on the use of artificial intelligence (AI) techniques, namely, data mining, decision making, and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP, the use of both types of screws would be feasible.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

A methodology enabling the customization of shoes for comfort improvement is proposed and assessed. For this aim, 3D printed graded density inserts were placed in one of the critical plantar pressure zones of conventional insoles, the heel. A semi-automated routine was developed to design the 3D inserts ready for printing, which comprises three main stages: (i) the definition of the number of areas with different mesh density, (ii) the generation of 2D components with continuous graded mesh density, and (iii) the generation of a 3D component having the same 2D base mesh. The adequacy of the mesh densities used in the inserts was previously assessed through compression tests, using uniform mesh density samples. Slippers with different pairs of inserts embedded in their insoles were mechanically characterized, and their comfort was qualitatively assessed by a panel of users. All users found a particular pair, or a set, of prototype slippers more comfortable than the original ones, taken as reference, but their preferences were not consensual. This emphasizes the need for shoe customization, and the usefulness of the proposed methodology to achieve such a goal.

The aim of the present study was to investigate the eco-cytotoxicity of several forms of nanomaterials (NM), such as nano-CuO, nano-TiO₂, nano-SiO₂ and nano-ZnO, on different aquatic species (Raphidocelis subcapitata, Daphnia magna and Lemna minor) following standard protocols and on human cell lines (Caco-2, SV-80, HepG2 and HaCaT). Predicted no-effect concentrations (PNEC) or hazard concentrations for 5% of the species (HC₅) were also estimated based on the compilation of data available in the literature. Most of the NM agglomerated strongly in the selected culture media. For the ecotoxicity assays, nano-CuO and nano-ZnO even in particle agglomeration state were the most toxic NM to the freshwater organisms compared to nano-TiO₂ and nano-SiO₂. Nano-ZnO was the most toxic NM to R. subcapitata and D. magna, while nano-CuO was found to be very toxic to L. minor. Nano-CuO was very toxic to Caco-2 and HepG2 cells, particularly at the highest tested concentrations, while the other NM showed no toxicity to the different cell lines. The HC₅ and PNEC values are still highly protective, due to data limitations. However, the present study provides consistent evidence of the potential risks of both nano-CuO and nano-ZnO against aquatic organisms and also their effects on public health.