Dissolution improvement of fenofibrate by melting inclusion in mesoporous silica

In this study,using mesoporous silica for the solubility enhancement of poorly watersoluble drug was investigated.Although the incorporating drug into mesoporous silica is generally performed through the solvent method,the new melting method was proposed in the present study.Fenofibrate,a poorly water-soluble drug,was incorporated into mesoporous silica by solvent method and melting method.The obtained samples were observed by SEM and their physicochemical properties were evaluated by PXRD and DSC measurement.The dissolution and supersaturated property were also investigated.The results from SEM,PXRD and DSC measurement showed that drug could be loaded into pore via the melting method as well as by the solvent method.The drug loaded quantity depended on the pore volume.Drug up to 33%could be incorporated into mesoporous silica and existed in amorphous state.When drug was overloaded or difficulty in incorporation into pore was found,recrystallization of drug occurred at the outer surface of mesoporous silica.From the dissolution test,samples prepared by solvent method and melting method gave the supersaturated drug concentration which sample from melting method showed superior dissolution to the one from solvent method.From this study,drug was efficiently incorporated into mesoporous silica by the melting method which is a simple and solvent-free process,and the aqueous solubility enhancement of poorly watersoluble drug was achieved.

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

A magnetic adsorbent based on salicylic acid-immobilized magnetite nano-particles for pre-concentration of Cd(II) ions

In this research,an eco-friendly magnetic adsorbent based on Fe_(3)O_(4)/salicylic acid nanocomposite was fabricated using a facile one-pot co-precipitation method.The crystalline and morphological characterization of the prepared nanocomposite was performed by field emission scanning electron microscopy,X-ray diffraction,and Fourier transform infrared spectroscopy.The nanocomposite was employed as a magnetic solid-phase extraction agent for separation of Cd(II)ions from synthetic solutions.Some experimental factors affecting the extraction efficiency were investigated and optimized.Following elution with acetic acid(pH 3.5),the pre-concentrated analyte was quantified by flame atomic absorption spectrometry.In optimal conditions,a linear calibration graph was achieved in the concentration range of 0.2–30 ng·mL^(−1) with a determination coefficient(R^(2))of 0.9953.The detection limit,the enhancement factor,inter-and intra-day relative standard deviations(for six consecutive extractions at the concentration level of 10 ng·mL^(−1))were 0.04 ng·mL^(−1),100,2.38%and 1.52%,respectively.To evaluate the accuracy of the method,a certified reference material(NIST SRM 1643e)was analyzed,and there was a good agreement between the certified and the measured values.It was successfully utilized to determine cadmium in industrial wastewater samples and the attained relative recovery values were between 96.8%and 103.2%.