Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

Alternative drying techniques for the manufacturing of hydrogel-forming and dissolving microneedle arrays.

Dissolving polymeric microneedle arrays and hydrogel-forming microneedle arrays have attracted much attention during recent years due mainly to their biocompatibility and capacity for enhanced drug delivery. Nevertheless, for the production of this type of devices, typically, a drying step is required. Microneedles are prepared following a micromoulding technique using aqueous blends of Gantrez® S-97. Currently, production of microneedles arrays involves a long drying process of 48 hours. Therefore alternative drying methods were investigated including microwave radiation and hot air convection.

The Use of a Pressure-Indicating Sensor Film to Provide Feedback upon Hydrogel-Forming Microneedle Array Self-Application In Vivo

PURPOSE:
To evaluate the combination of a pressure-indicating sensor film with hydrogel-forming microneedle arrays, as a method of feedback to confirm MN insertion in vivo.
METHODS:
Pilot in vitro insertion studies were conducted using a Texture Analyser to insert MN arrays, coupled with a pressure-indicating sensor film, at varying forces into excised neonatal porcine skin. In vivo studies involved twenty human volunteers, who self-applied two hydrogel-forming MN arrays, one with a pressure-indicating sensor film incorporated and one without. Optical coherence tomography was employed to measure the resulting penetration depth and colorimetric analysis to investigate the associated colour change of the pressure-indicating sensor film.
RESULTS:
Microneedle insertion was achieved in vitro at three different forces, demonstrating the colour change of the pressure-indicating sensor film upon application of increasing pressure. When self-applied in vivo, there was no significant difference in the microneedle penetration depth resulting from each type of array, with a mean depth of 237 μm recorded. When the pressure-indicating sensor film was present, a colour change occurred upon each application, providing evidence of insertion.
CONCLUSIONS:
For the first time, this study shows how the incorporation of a simple, low-cost pressure-indicating sensor film can indicate microneedle insertion in vitro and in vivo, providing visual feedback to assure the user of correct application. Such a strategy may enhance usability of a microneedle device and, hence, assist in the future translation of the technology to widespread clinical use.