An efficient and rapid method for the preparation of gold nanoparticles (AuNPs) within a few minutes has been developed by direct microwave irradiation of HAuCl4 and chitosan mixed solution in one pot. Herein, chitosan molecules acted as both the reducing and stabilizing agent for the preparation of AuNPs. The obtained AuNPs have different shapes, such as the spherical nanoparticles, triangular nanoplates and nanorods, which were characterized by ultraviolet-visible (UV-Vis) spectroscopy, transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and fourier transform infrared spectroscopy (FTIR). Additionally, the results showed that microwave power could affect the required time for preparing the AuNPs arising from the distinction of heating rate, and long irradiation time was favorable for complete reduction of HAuCl4 when a low microwave power was applied. 相似文献
BiFeO3 (BFO) and transition metal (Cu, Zn, Mn) doped BFO thin films were successfully fabricated on indium tin oxide (ITO)/glass substrate using sol–gel process, spin coating and layer by layer technique. Compared to the pure BFO thin film, improved ferroelectric and leakage current properties were observed in the transition metal doped BFO thin films. The transition metal (Cu, Zn, Mn) doped BFO thin films have varying degrees of lower leakage current compared with the pure BFO film. The substitution of Cu and Zn increase the remnant polarization of BFO thin films. The values of remnant polarization (2Pr) were 120.6 and 126.7 μC/cm2 at 933 kV/cm for Cu-doped and Zn-doped BFO thin film, respectively. 相似文献
We report the incorporation of Ga, Fe, and W, well-known activity and selectivity promoters in lower alkane activation, into Te-free Mo–V–O M1 phase in order to improve its stability under propane ammoxidation conditions. The Mo–V–M–O (M = W, Fe, Ga) M1 phases displayed improved stability as compared to the parent Mo–V–Te–O M1 phase due to higher Tammann temperatures of M oxides and activity for direct conversion of propane to propene and acrylonitrile. 相似文献
Road transportation is the largest and complex nonlinear entity of the traffic management system. Accurate prediction of traffic-related information is necessary for an effective functioning of Intelligent Transportation System (ITS). It is still a challenge for the departments of transportation to choose an appropriate prediction technique for the ITS applications. That is, a user must be able to utilize the disseminated information effectively by the forecasting models. This paper provides a detailed survey of the latest forecasting technologies and contributes to understand the key concept behind the prediction approaches. To provide guidelines to the decision-maker, this paper reviews multifaceted techniques developed by various authors for traffic prediction. We start classifying each technique into four categories namely, Machine Learning (ML), Computational Intelligence (CI), Deep Learning (DL), and hybrid algorithms. Many have conducted survey using model-driven or data-driven methods. We are the first to explore the area of traffic prediction based on the advances in multifaceted techniques proposing algorithmic approaches for key traffic characteristics in the forecasting process. The role of dependent factors in the prediction are analyzed thoroughly. We have analyzed each algorithm chronologically based on various traffic traits. The approaches are summarized based on the rational usage and performance of each technique. The analysis led to several research queries, and the appropriate responses are provided based on our detail survey. Finally, it is confirmed that currently, CI-MLs and DL hybrid techniques outperforms the rest in the field of traffic prediction. Ultimately suggested open challenges and future direction to explore the capability of DL and hybrid techniques further in the field of traffic prediction.