Solar/UV-induced photocatalytic degradation of three commercial textile dyes.

The photocatalytic degradation of three commercial textile dyes with different structure has been investigated using TiO(2) (Degussa P25) photocatalyst in aqueous solution under solar irradiation. Experiments were conducted to optimise various parameters viz. amount of catalyst, concentration of dye, pH and solar light intensity. Degradation of all the dyes were examined by using chemical oxygen demand (COD) method. The degradation efficiency of the three dyes is as follows: Reactive Yellow 17(RY17) > Reactive Red 2(RR2) > Reactive Blue 4 (RB4), respectively. The experimental results indicate that TiO(2) (Degussa P25) is the best catalyst in comparison with other commercial photocatalysts such as, TiO(2) (Merck), ZnO, ZrO(2), WO(3) and CdS. Though the UV irradiation can efficiently degrade the dyes, naturally abundant solar irradiation is also very effective in the mineralisation of dyes. The comparison between thin-film coating and aqueous slurry method reveals that slurry method is more efficient than coating but the problems of leaching and the requirement of separation can be avoided by using coating technique. These observations indicate that all the three dyes could be degraded completely at different time intervals. Hence, it may be a viable technique for the safe disposal of textile wastewater into the water streams.     

Characterization of the cavity nucleation factor for life prediction under creep-fatigue interaction

It is understood that grain boundary cavitation is one of the detrimental processes for the degradation of materials that reduces the creep-fatigue life at high temperatures. In a previous investigation, a model for life prediction under creep-fatigue conditions was proposed in terms of cavity nucleation and growth. In that model, the cavity nucleation factor (P) was introduced to correlate between the number of cavities and the plastic strain range from which athermal vacancies are generated. It was considered to be a material specific constant which was independent of the experimental conditions. However, in this study, it is found that the cavity nucleation factor is a function of the plastic strain range but is independent of the testing temperature at near 0.5 T _m. In the light of this dependency, a new cavity nucleation factor (P'), is introduced. Using this new cavity nucleation factor (P'), a modified equation for life prediction is proposed, and it is shown that there is good agreement between predicted and experimental lives. Additionally, an interesting approach has been made to find the physical meaning of the new cavity nucleation factor (P'). According to this study, it is suggested that the new cavity nucleation factor, which is regarded as a material specific constant, is found to be strongly related to the density of the grain boundary precipitates with a linear relationship existing between them.     

Improved performances of a S-SEED using extremely shallowquantum-wells and asymmetric Fabry-Perot cavity structure

The authors report significantly improved performances of a symmetric self-electrooptic-effect device (S-SEED), with high on-off contrast ratio (>30:l) and large optical bistability loop widths (ΔP=44%) at an applied bias of V_a=0 V, i.e., with no power supply. The S-SEED is made of extremely shallow quantum wells (ESQWs) in an asymmetric Fabry-Perot (AFP) cavity structure. At V_a =5 V ΔP increased by up to 95%, preserving the high contrast ratio. The reflectivity changes at V_a=0 and 5 V were about 15% and 30%, respectively. These are believed to be the largest values ever reported for such structures     

A neural network-based power system stabilizer using power flowcharacteristics

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS     

Effects of various post-treatments on carbon nanotube films for reliable field emission

In this report, the FE characteristics of carbon nanotubes (CNTs) treated using both thermal annealing and mechanical coatings on the as-grown CNTs systematically studied. It was found that in the high temperature annealed samples, CNTs were attacked at its root during annealing due to a small amount of oxygen, and were pulled out of the substrate in places after FE measurements because of the contact resistance. However, for the mechanically coated samples both with spin on glass (SOG) and polymethyl methacrylate (PMMA), CNTs were found to be nearly intact after FE measurements and showed reliable FE characteristics over repeatable voltage scan. The reliability of CNTs during FE could be owing to the strong adhesion of CNTs to the substrate both by SOG and PMMA coatings.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.