A novel zirconocene/ultradispersed diamond black powder supported catalytic system for ethylene polymerization

A novel carrier of ultradispersed diamond black powder (UDDBP) was used to support metallocene catalyst. Al₂O₃ was also used as carrier in order to compare with UDDBP. Supported catalysts for ethylene polymerization were synthesized by two different reaction methods. One way was direct immobilization of the metallocene on the support, the other was adsorption of MAO onto the support followed by addition of the metallocene. Four supported catalysts Cp₂ZrCl₂/UDDBP, Cp₂ZrCl₂/Al₂O₃, Cp₂ZrCl₂/MAO/UDDBP and Cp₂ZrCl₂/Al₂O₃/MAO were obtained. The content of the zirconium in the supported catalyst was determined by UV spectroscopy. The activity of the ethylene polymerization catalyzed by supported catalyst was investigated. The influence of Al/Zr molar ratio and polymerization temperature on the activity was discussed. The polymerization rate was also observed.     

钛硅分子筛挤条成型催化剂热稳定性能的研究

采用在空气中程序升温焙烧的方式考察了钛硅分子筛挤条成型催化剂的热稳定性能．对不同温度焙烧得到的样品进行了XRD、IR、UV Raman和SEM表征．结果表明，在高温焙烧时，由于载体结构的改变引起载体与分子筛之问相互作用发生变化，导致成型催化剂中分子筛骨架破坏．随着焙烧温度的提高，IR谱图中960cm^-1处代表钛进入骨架的特征峰的强度迅速减弱，当焙烧温度达到1000℃时，960cm-1处的骨架钛特征峰移至948cm^-1．在紫外拉曼谱图中，骨架位钛物种的特征拉曼谱峰-1125cm^-1谱峰在加入SiO2载体后，高温下峰强度明显降低，这说明载体的加入降低了钛硅分子筛挤条成型催化剂的热稳定性能．     

配位负载金属卟啉的合成及催化氧化环已烷的性能研究

合成了三种与咪唑配体键合的新型载体，并通过其与四苯基卟啉铁和卟啉锰配位得到了改性的负载金属卟啉催化剂。讨论了它们在催化氧化环己烷反应中的稳定性、活性等，结果表明配位负载方式明显提高了金属卟啉的稳定性，其中硅胶配位负载锰卟啉还具有较高的活性。     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis, E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

Evaluation of SARs for the prediction of skin irritation/corrosion potential–structural inclusion rules in the BfR decision support system†

The German Federal Institute for Risk Assessment (BfR) has developed a Decision Support System (DSS) to assess certain hazardous properties of pure chemicals, including skin and eye irritation/corrosion. The BfR–DSS is a rule-based system that could be used for the regulatory classification of chemicals in the European Union. The system is based on the combined use of two predictive approaches: exclusion rules based on physicochemical cut-off values to identify chemicals that do not exhibit a certain hazard (e.g., skin irritation/corrosion), and inclusion rules based on structural alerts to identify chemicals that do show a particular toxic potential. The aim of the present study was to evaluate the structural inclusion rules implemented in the BfR–DSS for the prediction of skin irritation and corrosion. The following assessments were performed: (a) a confirmation of the structural rules by rederiving them from the original training set (1358 substances), and (b) an external validation by using a test set of 200 chemicals not used in the derivation of the rules. It was found as a result that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion, albeit some skin irritants were in the test set.     

Statistical discrimination of steroid profiles in doping control with support vector machines

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis,E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.