RESUMO
Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted.
RESUMO
In this paper, we introduce a novel type of transdermal drug delivery device (TD3) with a micro-electro-mechanical system (MEMS) design using computer-aided design (CAD) techniques as well as computational fluid dynamics (CFD) simulations regarding the fluid interaction inside the device during the actuation process. For the actuation principles of the chamber and microvalve, both thermopneumatic and piezoelectric principles are employed respectively, originating that the design perfectly integrates those principles through two different components, such as a micropump with integrated microvalves and a microneedle array. The TD3 has shown to be capable of delivering a volumetric flow of 2.92 × 10-5 cm3/s with a 6.6 Hz membrane stroke frequency. The device only needs 116 Pa to complete the suction process and 2560 Pa to complete the discharge process. A 38-microneedle array with 450 µm in length fulfills the function of permeating skin, allowing that the fluid reaches the desired destination and avoiding any possible pain during the insertion.
