The crystallisation process of polyhydroxyalkanoates (PHA) polymers plays a key role on final properties of manufactured parts due to most PHA are highly sensitive to physical aging which leads to embrittlement. The secondary crystallisation associated with the aging process can be partially controlled by the cooling process during manufacturing or, even, by heat treatments such as annealing. A critical parameter in additive manufacturing is the difficulty to achieve good adhesion of the material to the printing bed. The bed temperature plays a key role on PHBH crystallisation, which leads to shrinkage having a negative effect on polymer-to-bed adhesion. In this work, a study of the effect of different processing parameters such as the printing temperature, the bed temperature, the cooling conditions, as well as raster direction on the final properties of PHBH 3D-printed parts is carried out.

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.

Lignocellulosic wood flour particles with three different sizes were used to reinforce Solanyl^® type bioplastic in three compositions (10, 20, and 30 wt.%) and further processed by melt-extrusion and injection molding to simulate industrial conditions. The wood flour particles were morphologically and granulometric analyzed to evaluate their use as reinforcing filler. The Fuller method on wood flour particles was successfully applied and the obtained results were subsequently corroborated by the mechanical characterization. The rheological studies allowed observing how the viscosity was affected by the addition of wood flour and to recover information about the processing conditions of the biocomposites. Results suggest that all particles can be employed in extrusion processes (shear rate less than 1000 s⁻¹). However, under injection molding conditions, biocomposites with high percentages of wood flour or excessively large particles may cause an increase in defective injected-parts due to obstruction of the gate in the mold. From a processing point of view and based on the biocomposites performance, the best combination resulted in Solanyl^® type biopolymer reinforced with wood flour particles loaded up to 20 wt.% of small and medium particles size. The obtained biocomposites are of interest for injected molding parts for several industrial applications.