Linear-dendritic triblock copolymers of linear poly(ethylene glycol) and hyperbranched poly(citric acid) (PCA-PEG-PCA) were used as the reducing and capping agents to encapsulate gold and silver nanoparticles (AuNPs and AgNPs). PCA-PEG-PCA copolymers in four different molecular weights were synthesized using 2, 5, 10 and 20 citric acid/PEG molar ratios and were called A1, A2, A3 and A4, respectively. Nanoparticles were encapsulated simultaneously during the preparation process. AuNPs were simply synthesized and encapsulated by addition a boiling aqueous solution of HAuCl4 to aqueous solutions of A1, A2, A3 and A4. In the case of silver, an aqueous solution of AgNO3 was reduced using NaBH4 and AgNPs were encapsulated simultaneously by adding aqueous solutions of different PCA-PEG-PCA to protect the fabricated silver nanoparticles from aggregation. Encapsulated AuNPs and AgNPs were stable in water for several months and agglomeration did not occur. The synthesized silver and gold nanoparticles have been encapsulated within PCA-PEG-PCA macromolecules and have been studied using Transmission Electron Microscopy (TEM) and UV/Vis absorption spectroscopy. Studies reveal that there was a reverse relation between the size of synthesized AuNPs/AgNPs and the size of citric acid parts of PCA-PEG-PCA copolymers. For example, the prepared gold and silver nanoparticles by A3 copolymer are of an average size of 8 nm and 16 nm respectively. Finally, the loading capacity of A1, A2, A3 and A4 and the size of synthesized AuNPs and AgNPs were investigated using UV/Vis data and the corresponding calibration curve. It was found that the loading capacity of copolymers depends directly on the concentration of copolymers and their molecular weight.
In this work, the modified Flory-Huggins coupled with the free-volume concept and the artificial neural network models were used to obtain the osmotic pressure of aqueous poly(ethylene glycol) solutions. In the artificial neural network, the osmotic pressure of aqueous poly(ethylene glycol) solutions depends on temperature, molecular weight and the mole fractions of poly(ethylene glycol) in aqueous solution. The network topology is optimized and the (3-1-1) architecture is found using optimization of an objective function with batch back propagation (BBP) method for 134 experimental data points. The results obtained from the neural network in obtaining of the osmotic pressure of aqueous poly(ethylene glycol) were compared with those obtained from the free volume Flory-Huggins model (FV-FH). The results showed that the modified Flory-Huggins model and also the artificial neural network can accurately predict the osmotic pressure of aqueous poly(ethylene glycol) solutions but the accuracy of ANN is much better than the modified Flory-Huggins model.
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