Tough hyperbranched epoxy/poly(amido‐amine) modified bentonite thermosetting nanocomposites

A tough and highly flexible hyperbranched epoxy and poly(amido‐amine) modified bentonite based thermosetting nanocomposite was demonstrated. The FTIR, XRD, and TGA analyses confirmed the modification of bentonite. The formation of partially exfoliated structure of the nanocomposite with good physicochemical interactions among the hyperbranched epoxy, poly(amido‐amine) hardener and modified clay was investigated by the FTIR, XRD, SEM, and TEM analyses. Significant improvements of 750% toughness, 300% elongation at break, 50% tensile strength, 300% modulus, and 250% adhesive strength of the pristine epoxy were achieved by the formation of nanocomposites with 3 wt % of modified clay. The experimental modulus values of the nanocomposites were compared with three theoretical models to account the interactions between filler and matrix. Thus, the studied epoxy nanocomposite has great potential to be used as an advanced epoxy thermoset. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40327.     

How Different Characterization Techniques Elucidate the Nature of the Gold Species in a Polycrystalline Au/TiO2 Catalyst

TiO₂‐supported gold species were prepared via the deposition‐precipitation route, with conservation of the initial speciation by freeze‐drying. The structural and electronic properties of the Au species were investigated by X‐ray absorption spectroscopy, electron microscopy, and IR spectroscopy of adsorbed CO in four states. Exclusively Au^III was deposited on the TiO₂ surface in patches ranging from isolated Au ions to three‐dimensional clusters. This paper illustrates in detail the unique contributions of all characterization techniques to this structural model.     

Regenerated cellulose nanocomposites reinforced with exfoliated graphite nanosheets using BMIMCL ionic liquid

Green nanocomposites of regenerated cellulose/exfoliated graphite nanosheets films with low nanofiller loadings were prepared using environmentally benign 1-butyl-3-methylimidazolium chloride (BMIMCl) ionic liquid. X-ray diffraction revealed well developed intercalated nanocomposites. The tensile strength and Young's modulus of the prepared nanocomposites were increased by 97.5% and 172% respectively when 0.75 wt.% and 1 wt.% exfoliated graphite nanosheets were added. The results were validated using the Halpin–Tsai model. The exfoliated graphite nanosheets were unidirectionally aligned in the regenerated cellulose parallel to the surface of the nanocomposites as revealed by transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM). Also, the TEM and FESEM revealed uniform dispersion of the exfoliated graphite nanosheets and good interaction between the nanofillers and the matrix. The addition of the exfoliated graphite nanosheets enhanced the thermal stability and reduced the water absorption and diffusivity of the nanocomposites.     

Adsorption‐concentration polarization model for ultrafiltration in mixed matrix membrane

Adsorption has been found to be significant in ultrafiltration by mixed matrix membrane. Removal of very low molecular weight solutes compared to the molecular weight cut off of the membrane is facilitated by adsorption. The modeling of the adsorption coupled with concentration polarization is presented based on the mathematical approach developed by Gekas et al. (Gekas et al. Chem Eng Sci. 1993;48:2753–2765), from the first principles. However, extensive modifications were included in theoretical development including those suggested by Ruiz‐Bevia et al. (Ruiz‐Bevia et al. Chem Eng Sci. 1997;52:2343–2352). The developed model captured the rejection dynamics with the help of retention factor. The model equations were solved under the framework of boundary layer analysis, using the integral approach. Effects of the adsorption isotherm and the different parameters affecting the system performance were also investigated. Further, experimental validation of the model results with two different mixed matrix ultrafiltration studies was also elucidated. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2354–2364, 2014     

高效液相色谱法同时测定多种杀虫剂的有效成分

建立了用高效液相色谱法测定农药市场上常见的8种杀虫剂及2种禁限用农药有效成分的分析方法。采用C18柱(150L×4.6mm),以乙腈-水为流动相,紫外检测器检测,外标法定量。结果表明,样品的加标回收率为90.0%¹02.0%,相对标准偏差为0.01%102.0%,相对标准偏差为0.01%¹.47%。该方法简单、快速、结果准确,可作为单剂、复配制剂同时测定其有效成分的方法。     

分析化学网络智能组卷系统的开发

为了进一步推动教学改革,促进教学质量的提高,我们开发了分析化学智能组卷系统,介绍了智能组卷系统开发的意义、开发过程、主要特色及使用效果,并对开发前景进行了展望。结果表明:该网络智能组卷系统具有客观性强、时间快、易于控制试卷难度等特点,深受师生和专家好评。     

无机化学实验教学绿色化探索与实践

基于绿色化学的理念,就如何实现无机化学实验的绿色化进行了探索和研究。提出了推行微型化学实验、多媒体仿真技术、"三废"处理、串联实验和现代检测技术应用于无机化学实验中。通过以上措施,加强和培养了师生的环境保护意识,树立了绿色化学的理念。同时,无机化学及实验的绿色化节约了化学试剂,减少有毒有害物质的排放,实现了环境友好化过程。     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.