Poly(styrene-co-butyl acrylate)/mesoporous diatomaceous earth mineral nanocomposites by in situ AGET ATRP

Journal of Thermal Analysis and Calorimetry - Mesoporous diatomite platelets were used for in situ copolymerization of styrene and butyl acrylate by activators generated by electron transfer for...     

Silica aerogel-filled PMMA by in situ reverse atom transfer radical polymerization: kinetics and thermal studies

Hydrophobically modified silica aerogel nanoparticles (H-SiANp) were used for in situ polymerization of methyl methacrylate by reverse atom transfer radical polymerization to synthesize well-defined PMMA nanocomposites. Inherent characteristics of the prepared H-SiANp were evaluated by nitrogen adsorption/desorption isotherms, SEM, and TEM. Conversion and molecular weight determinations were carried out using GC and SEC, respectively. Addition of 3 mass% of the H-SiANp leads to decrement of conversion from 92 to 74%. Molecular weight of poly (methyl methacrylate) chains also decreases from 19,737 to 15,662 g mol^?1 by addition of only 3 mass% H-SiANp; however, PDI values increase from 1.36 to 1.82. Linear increase of ln(M₀/M) with time for all the samples shows that polymerization proceeds in a living manner. In addition, suitable agreement between theoretical and experimental molecular weight in combination with low PDI values can appropriately demonstrate the living nature of the polymerization. TG results indicate that by increasing H-SiANp content, improvements in thermal stability of the nanocomposites were obtained. DSC results show a decrease in glass transition temperature from 87.4 to 80.9 °C by addition of 3 mass% H-SiANp.

     

Exhaustive investigation of drug delivery systems to achieve optimal condition of drug release using non-linear generalized artificial neural network method: feedback from the loading step of drug

Drug delivery systems are potential systems with ability to release drugs with a variety of mechanisms. Some mechanisms may consist of multiple steps, where the release rate of each step can vary from the others. Moreover, a drug release is a kinetic process intrinsically and the initial amount of the loaded drug may influence the amount of the release. Therefore, to achieve the desirable release, the loading and release processes should be investigated simultaneously, while researchers have considered these processes independently so far. Considering the fact that functional dependence between loading and release is not obvious, we proposed the combination of experimental design and powerful non-linear generalized artificial neural network (G-ANN) methods for the simultaneous optimization of these processes. Here, the functionalized PEGylated KIT-6 ([β-CD@PEGylated KIT-6]) NPs and curcumin were selected as a nano-carrier and drug, respectively. The curcumin release was optimized by G-ANN by considering the the feedback from the loading step. The obtained optimal parameters were as follows: (in the release process) 1.80 of the weight ratio of drug to nano-carrier, 5.70 of pH and 120 h of release time; and (in the loading process) 43 h of loading time and 2.2 of weight ratio of drug/nano-carrier.