Polyaniline (PANI) has recently gained great attention due to its outstanding electrical properties and ease of processability; these characteristics make it ideal for the manufacturing of polymer blends. In this study, the processing and piezoresistive characterization of polymer composites resulting from the blend of PANI with ultra-high molecular weight polyethylene (UHMWPE) in different weight percentages (wt %) is reported. The PANI/UHMWPE composites were uniformly homogenized by mechanical mixing and the pellets were manufactured by compression molding. A total of four pellets were manufactured, with PANI percentages of 20, 25, 30 and 35 wt %. Fourier-transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), differential thermal analysis (DTA), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were used to confirm the effective distribution of PANI and UHMWPE particles in the pellets. A piezoresistive characterization was performed on the basis of compressive forces at different voltages; it was found that the error metrics of hysteresis and drift were influenced by the operating voltage. In general, larger voltages lowered the error metrics, but a reduction in sensor sensitivity came along with voltage increments. In an attempt to explain such a phenomenon, the authors developed a microscopic model for the piezoresistive response of PANI composites, aiming towards a broader usage of PANI composites in strain/stress sensing applications as an alternative to carbonaceous materials.

The sun represents a potential opportunity for sustainable energy production. The study of solar energy harvesting reinforces this promise, with studies simulating different operating environments by estimating the sun’s orientation toward the south or north, depending on the planet’s location. However, studies for latitudes between 23° South and 23° North are a little-explored area. This research investigates different energy harvesting scenarios in flat-plate collectors, combining different possibilities throughout the year, analyzing the amount of energy captured using algorithms and Monte Carlo simulation. The methodology allows predicting efficiencies above 36% simply by rotating the harvesting devices from north to south or vice versa just twice a year.