Supertough Hybrid Hydrogels Consisting of a Polymer Double‐Network and Mesoporous Silica Microrods for Mechanically Stimulated On‐Demand Drug Delivery

Despite their potential in various fields of bioapplications, such as drug/cell delivery, tissue engineering, and regenerative medicine, hydrogels have often suffered from their weak mechanical properties, which are attributed to their single network of polymers. Here, supertough composite hydrogels are proposed consisting of alginate/polyacrylamide double‐network hydrogels embedded with mesoporous silica particles (SBA‐15). The supertoughness is derived from efficient energy dissipation through the multiple bondings, such as ionic crosslinking of alginate, covalent crosslinking of polyacrylamide, and van der Waals interactions and hydrogen bondings between SBA‐15 and the polymers. The superior mechanical properties of these hybrid hydrogels make it possible to maintain the hydrogel structure for a long period of time in a physiological solution. Based on their high mechanical stability, these hybrid hydrogels are demonstrated to exhibit on‐demand drug release, which is controlled by an external mechanical stimulation (both in vitro and in vivo). Moreover, different types of drugs can be separately loaded into the hydrogel network and mesopores of SBA‐15 and can be released with different speeds, suggesting that these hydrogels can also be used for multiple drug release.     

Monthly flow forecast for Mississippi River basin using artificial neural networks

Long-term monthly flow forecasts are essential for decision making in a river basin system. Many studies have already been reported on monthly as well as seasonal forecast using artificial neural networks (ANNs). This study demonstrates that monthly forecasts can be significantly improved if the input variables in ANN models are chosen with due consideration, even if number of training patterns are less. Monthly forecast models up to 12-month lead-time have been developed for Mississippi River in USA. It is seen that direct forecast with only antecedent flows as inputs does not improve the result. It is better to develop individual models for each month separately with information from previous years for the same month. Further, the forecast is found to significantly improve if the difference in predicted and actual flows is also included as one of the input variables (i.e. error updating), particularly where there is a clearly observed pattern in the historical information.     

Twisted F-Shaped Slot Loaded UWB Printed Antenna for On-Body Application

Wireless Personal Communications - Compact UWB antenna manufactured in twisted F form with slots loaded is exhibited. An ultra-wideband 3.1 GHz to 10.6 GHz with modified ground...     

Load Based Key Generation for MANETs: A Comparative Study with DSR and AODV

Wireless Personal Communications - Automatic key establishment schemes are the root of secure communication in Mobile adhoc networks(MANETs). These schemes are not universal, their performance...     

Poly(arylene ether ketone) with pendant pyridinium groups for alkaline fuel cell membranes

Using the step-growth polycondensation reaction, poly(arylene ether ketone) (PAEK) and activated poly(arylene ether ketone)-NHS intermediates (PAEK-N) were synthesized. PAEK-NHS intermediates with pyridinium groups (PAEK-PYR) were obtained by adding different amounts of PYR groups. The successful syntheses of PAEK, PAEK-N, and PAEK-PYR were confirmed by nuclear magnetic resonance spectroscopy and Fourier transform infrared spectroscopy. Several important membrane properties (e.g., ionic exchange capacity (IEC), water uptake, anion conductivity, and thermal and mechanical stability) were investigated for their applications in alkaline fuel cells. Water uptake, swelling ratio, anion conductivity, and IEC increased with increasing PYR contents, while the mechanical properties decreased. Among a series of prepared membranes, the PAEK-PYR100 and PAEK-PYR125 membranes showed IEC and anion conductivity values that were higher than those of a commercial AHA membrane. Also, all of the prepared membranes were thermally stable up to 255 °C and show excellent chemical stability in alkaline conditions.