Mesoporous silica materials with high pore volume were successfully prepared by the chemical precipitation method, with water glass and a biodegradable nonionic surfactant polyethylene glycol (PEG). The obtained materials were characterized by trans-mission electron microscopy (TEM), scanning electron microscopy (SEM), thermo gravimetric analyzer and differential scanning calorimetry (TG-DSC), nitrogen adsorption-desorption measurements, and X-ray diffraction (XRD). The results showed that the changes of the pore parameters depended on both the surfactant content and heat treatment temperature. When the content of PEG was 10wt/ and the obtained PEG/SiO2 composite was heated at 600°C, the mesoporous silica with a pore volume of 2.2 cm3/g, a BET specific surface area of 361.55 m2/g, and a diameter of 2-4 μm could be obtained. The obtained mesoporous silica materials have po-tential applications in the fields of paint and plastic, as thickening, reinforcing, and flatting agents.
In this paper,zinc oxide nanoparticles were first prepared and surface-modified.A Pickering emulsion was then prepared,consisting of nitrobenzene(oil phase),water(water phase)and the modified zinc oxide nanoparticles located on the water-oil interface.The effects of different emulsions on the removal rate of nitrobenzene by photocatalytic degradation were studied.The results proved that use of a Pickering emulsion stabilized by surface-modified ZnO nanoparticles provides an effective and novel way to intensify the photocatalytic degradation of the organic contaminant.
分布式拒绝服务(distributed denial of service,DDoS)攻击能够在短时间内产生巨量的数据包耗尽目标主机或网络的资源,经过研究发现这些伪造的数据包在一个特定的时间内有着合法数据包所不具备的函数特点。因此,本文提出了行为分布的模型,一旦有可疑流流入服务器,则开始计算这些可疑流的行为分布差异,如果该差异小于一个设定的阈值,则判断有DDoS攻击发生;反之则为合法的数据访问。根据NS-3的模拟实验,证明该模型能够有效的从合法访问中区分出DDoS攻击流,对提前控制DDoS攻击的发生具有重要的意义。
