异质结型SnO2-TiO2纳米纤维光催化降解柴油机多环芳烃

采用同轴静电纺丝法制备了SnO_2-TiO_2纳米纤维,用X射线衍射仪(XRD)、扫描电子显微镜(SEM)、红外光谱仪(FTIR)和紫外-可见光谱能谱仪(UV-Vis),表征了SnO_2-TiO_2纳米纤维相组成和微观形貌,评价了催化剂表面化学形态和吸光特性,研究了SnO_2含量、光源条件、温度和催化稳定性对降解萘污染物的影响规律.结果表明,催化剂Sn25对萘降解效率最高.在暗光、可见光和紫外光条件下,萘降解效率分别为13.8%、37%和98%.Sn25对萘降解稳定率(标准差率)为0.36%.利用催化剂Sn25对柴油机尾气中多环芳烃(PAHs)排放污染物光催化降解应用发现,Sn25对低苯环PAHs、中苯环PAHs和高苯环PAHs的平均催化降解效率分别为80.2%、75.4%和60.2%.表明SnO_2-TiO_2纳米纤维具有较强光催化降解多环芳烃污染物能力.     

Water Quality Prediction of Water Sources Based on Meteorological Factors using the CA-NARX Approach

With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.

     

裂解GC-MS法检验家用小轿车轮胎橡胶

利用优化的裂解气相色谱-质谱法对家用小轿车常用的16种不同品牌轮胎胎面和胎侧胶进行检验,检出丰富的胶料特征裂解产物和添加剂成分.根据胶料的特征裂解产物,发现轮胎胎面胶多为天然胶、顺丁胶和丁苯胶的并用胶,而胎侧胶则为天然胶和顺丁胶的并用胶,利用检出成分进一步进行分析,可将不同品牌的轮胎橡胶有效鉴别.