Surface modification of ZnO nanorods with CdS quantum dots for application in inverted organic solar cells: effect of deposition duration

Incorporating cadmium sulfide quantum dots (CdS QDs) onto ZnO nanorod (ZNRs) has been investigated to be an efficient approach to enhance the photovoltaic performance of the inverted organic solar cell (IOSC) devices based on ZNRs/poly (3-hexylthiophene) (P3HT). To synthesize CdS/ZNRs, different durations of deposition per cycle from 1 to 9 min were used to deposit CdS via SILAR technique onto ZNRs surface grown via hydrothermal method at low temperature on FTO substrate. In typical procedures, P3HT as donor polymer were spun-coating onto CdS/ZNRs to fabricate IOSC devices, followed by Ag deposition as anode by magnetron sputtering technique. Incorporation of CdS QDs has modified the morphological, structural, and optical properties of ZNRs. Incorporation of CdS QDs onto ZNRs also led to higher open circuit voltage (V_oc) and short circuit current density (J_sc) of optimum ZNRs/CdS QDs devices due to the increased interfacial area between ZNRs and P3HT for more efficient exciton dissociation, reduced interfacial charge carrier recombination as a result of lower number of oxygen defects which act as electron traps in ZnO and prolonged carrier recombination lifetime. Therefore, the ZNRs/CdS QDs/P3HT device exhibited threefold higher PCE (0.55%) at 5 min in comparison to pristine ZNR constructed device (0.16%). Overall, our study highlights the potential of ZNRs/CdS QDs to be excellent electron acceptors for high efficiency hybrid optoelectronic devices.     

Prediction of scour below submerged pipeline crossing a river using ANN

The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.     

\mathcal{C}oo\mathcal{PS} – Towards a method for coordinating personalized services

This paper presents oo which is a method for oordinating ersonalized Services. These services are primarily offered to mobile users. The concept of services is the object of intense investigations from both academia and industry. However, very little has been accomplished so far regarding first, personalizing services for the benefit of mobile users, and second, providing the appropriate methodological support for those (i.e., designers) who will be specifying the operations of personalization. Various obstacles still exist such as lack of techniques for modeling and specifying the integration of personalization into services, and existing approaches for service composition typically facilitate orchestration only, while neglecting contexts of users and services. ooconsists of several steps ranging from service definition and personalization to service deployment. Each step has some representation techniques, which aim at facilitating the specification and validation of the operations of coordinating personalized services. Zakaria Maamar is an associate professor in computer sciences at Zayed University, Dubai, United Arab Emirates. His research interests include Web services, software agents, and context-aware computing. Maamar has a PhD in computer sciences from Laval University. Djamal Benslimane is a full professor in computer sciences at Claude Bernard Lyon 1 University and a member of the Laboratoire d'InfoRmatique en Images et Systèmes d'information- Centre National De la Recherche Scientifique (LIRIS-CNRS), both in Lyon, France. His research interests include interoperability, Web services, and ontologies. Benslimane has a PhD in computer sciences from Blaise Pascal University. Michael Mrissa is a Ph.D. candidate in computer sciences at Claude Bernard Lyon 1 University and a member of the Laboratoire d'InfoRmatique en Images et Systèmes d'information - Centre National De la Recherche Scientifique (LIRIS-CNRS), both in Lyon, France. His research interests include semantic Web services, interoperability and peer-to-peer networks. Chirine Ghedira is an associate professor in computer sciences at Claude Bernard Lyon 1 University and a member of the Laboratoire d'InfoRmatique en Images et Systèmes d'information- Centre National De la Recherche Scientifique (LIRIS-CNRS), both in Lyon, France. Her research interests include Web services and context-aware computing. Ghedira has a PhD in computer sciences from the National Institute for Applied Sciences (INSA).     

Fabrication of Polypropylene Nanofibers from Polypropylene/Polyvinyl Butyral Blend Films Using Laser-Assisted Melt-Electrospinning

Polypropylene (PP) nanofibers, a few hundred nanometers in diameter, are of immense importance in the fiber industry. This article reports the fabrication of delicate PP nanofibers. Polyvinyl butyral (PVB) was added to PP as a blend component, and a nozzle-free melt-electrospinning system with a line-like CO₂ laser melting device was used to manufacture PP nanofibers. We investigated the effect of PVB ratio on fiber diameter. The addition of PVB was found to be potentially very beneficial in PP/PVB blends, resulting in improved PP crystallinity and a steady decrease of fiber diameter with high productivity. The reduction of fiber diameter was attributed to the decline of viscosity, increase of surface adhesion properties, and polarity of blends due to the inclusion of PVB. To produce PP nanofiber, the PVB was removed from PP/PVB blend fibers with an ethanol treatment. A drastic drop of PP fiber diameter followed by fiber splitting was observed after PVB removal. We obtained PP nanofibers with a diameter as low as 181 ± 105 nm from the blend fiber with 90% PVB. Infrared spectroscopy of fibers demonstrated that PP fibers from pure polymer and blends showed the same characteristic peaks. POLYM. ENG. SCI., 60: 362–370, 2019. © 2019 Society of Plastics Engineers     

Improvement of thermal conductivity and dielectric constant of graphene-filled epoxy nanocomposites using colloidal polymerization approach

Polymer Bulletin - The effect of the polymeric cross-link density on the thermal conductivity of an oxidized graphene (OG)-filled epoxy nanocomposite was investigated by two different fabrication...     

Surface and bulk properties of Cu–ZSM-5 and Cu/Al2O3 solids during redox treatments. Correlation with the selective reduction of nitric oxide by hydrocarbons

This paper deals with the redox properties of Cu ions implanted in ZSM-5 and supported on Al₂O₃, catalysts active in the selective reduction of NO by hydrocarbons such as propane. Data on the reducibility of the Cu systems in various atmospheres (vacuum, CO, H₂, O₂) and on their DeNO_x activity are presented. The methods used to obtain informations on the surface and bulk transformations (and their link with catalytic behaviour) are complementary: UV–visible diffuse reflectance spectroscopy being useful to detect the presence of Cu²⁺ and Cu⁰, while Cu⁺ is detected indirectly by the analysis of the IR spectrum of CO bound selectively to this cation.

The main contributions to the previous knowledge are the following: it is possible to distinguish CO bound to isolated and non-isolated Cu⁺ ions; the isolated Cu²⁺ ions are reducible under vacuum without participation of organic impurities; the more active solids for the NO reduction into N₂ are characterized by the presence of isolated Cuⁿ⁺ ions beside the additional influence of the zeolitic framework; after the formation of Cu⁺ ions the redox cycles are reversible but, after the formation of Cu⁰, the reversibility or irreversibility of the redox cycles and the restoration of the SCR activity are function of the copper content; the activity decreases after agglomeration into bulk oxides; there is no formation of bulk CuO during the reaction and, with reducing and moderate oxidizing mixtures, part of the copper remains as cuprous ions.     

Generating Synthetic Rainfall Total Using Multivariate Skew-<Emphasis Type="Italic">t</Emphasis> and Checkerboard Copula of Maximum Entropy

This study aims to test the appropriateness of multivariate skew-t copula and checkerboard copula of maximum entropy in generating monthly rainfall total data. The generation of synthetic data is important, as it provides hypothetical data in areas for which data availability remains limited. Three selected meteorological stations in Kelantan, Malaysia, Stesen Pertanian Melor, Rumah Pam Salor, and Ladang Lepan Kabu, are considered in this study. Monthly rainfall total data for the driest and wettest months in the year are tested in this study. For these three stations, the identified month with the least total of rainfall received (driest) is May, while the month with the highest total of rainfall received (wettest) is November. The data is fitted to gamma distribution with the corresponding parameters estimated. The observed data will be transformed to be in unit uniform using the gamma marginal. The resulting data is compared to simulated uniform data generated using multivariate skew-t copula and checkerboard copula of maximum entropy models based on the correlation values of the observed and simulated data. Next, the Kolmogorov-Smirnov test is used to assess the fit between the observed and generated data. The results show that the values of simulated correlation coefficients do not differ much for gamma distribution, multivariate skew-t, and maximum entropy approaches. This implies that the multivariate skew-t and maximum entropy may be used to generate monthly rainfall total for cases in which actual data is unavailable.     

Distribution of linear alkylbenzenes (LABs) in riverine and coastal environments in South and Southeast Asia

This paper reports the result of sewage pollution monitoring conducted in South and Southeast Asia during 1998-2003 using linear alkylbenzenes (LABs) as molecular tracers of sewage contamination. Eighty-nine water samples collected from Malaysia, Vietnam, and Japan (Tokyo), and 161 surface sediment samples collected from Tokyo, Thailand, Malaysia, Philippines, Vietnam, Cambodia, Indonesia, and India were analyzed for alkylbenzenes. The concentration range of SigmaLABs in river water particles in Southeast Asia (<0.005-0.913 microg/L) was comparable to or higher than those found in Tokyo (<0.005-0.638 microg/L). I/E ratios (a ratio of internal to external isomers of LABs) in tropical Asian waters were close to the value of LABs in raw sewage ( approximately 1) and much lower than those in secondary effluents (3-5). This suggests that untreated or inadequately treated sewage is discharged into the water. SigmaLABs concentrations in sediments from South and Southeast Asia ranged from <0.002-42.6 microg/g-dry with the highest concentration occurring at several populous cities. Low I/E ratios of the sediments with high SigmaLABs concentrations suggest a heavy load of untreated sewage. Clearly in view of the current data and evidence of the implications of sewage pollution, this paper highlights the necessity of the continuation of water treatment system improvement in tropical Asia.     

Multiple Linear Regression Model for Total Bed Material Load Prediction

A new total bed material load equation that is applicable for rivers in Malaysia was developed using multiple linear regression analyses. A total of 346 hydraulic and sediment data were collected from nine natural and channelized rivers having diverse catchment characteristics in Malaysia. The governing parameters were carefully selected based on literature survey and field experiments, examined and grouped into five categories namely mobility, transport, sediment, shape, and flow resistance parameters. The most influential parameters from each group were selected by using all possible regression model method. The suitable model selection criteria namely the R-square, adjusted R-square, mean square error, and Mallow’s Cp statistics were employed. The accuracy of the derived model is determined using the discrepancy ratio, which is a ratio of the calculated values to the measured values. The best performing models that give the highest percentage of prediction from the validation data were chosen. In general, the newly derived model is best suited for rivers with uniform sediment size distribution with a d50 value within the range of 0.37–4.0 mm and performs better than the commonly used Graf, Yang, and Ackers–White total bed material load equations.