Nanostructured metal oxides as adsorbents and photocatalysts

This research underlines the extensive application of nanostructured metal oxides in environmental systems such as hazardous waste remediation and water purification. This study tries to forge a new understanding of the complexity of adsorption and photocatalysis in the process of water treatment. Sodium niobate doped with a different amount of tantalum, was prepared via a hydrothermal reaction and was observed to be able to adsorb highly hazardous bivalent radioactive isotopes such as Sr2+ and Ra2+ions. This study facilitates the preparation of Nb-based adsorbents for efficiently removing toxic radioactive ions from contaminated water and also identifies the importance of understanding the influence of heterovalent substitution in microporous frameworks. Clay adsorbents were prepared via a two-step method to remove anionic and non-ionic herbicides from water. Firstly, layered beidellite clay was treated with acid in a hydrothermal process; secondly, common silane coupling agents, 3-chloro-propyl trimethoxysilane or triethoxy silane, were grafted onto the acid treated samples to prepare the adsorption materials. In order to isolate the effect of the clay surface, we compared the adsorption property of clay adsorbents with ƒ×-Al2O3 nanofibres grafted with the same functional groups. Thin alumina (£^-Al2O3) nanofibres were modified by the grafting of two organosilane agents 3-chloropropyltriethoxysilane and octyl triethoxysilane onto the surface, for the adsorptive removal of alachlor and imazaquin herbicides from water. The formation of organic groups during the functionalisation process established super hydrophobic sites along the surfaces and those non-polar regions of the surfaces were able to make close contact with the organic pollutants. A new structure of anatase crystals linked to clay fragments was synthesised by the reaction of TiOSO4 with laponite clay for the degradation of pesticides. Based on the Ti/clay ratio, these new catalysts showed a high degradation rate when compared with P25. Moreover, immobilized TiO2 on laponite clay fragments could be readily separated out from a slurry system after the photocatalytic reaction. Using a series of partial phase transition methods, an effective catalyst with fibril morphology was prepared for the degradation of different types of phenols and trace amount of herbicides from water. Both H-titanate and TiO2-(B) fibres coated with anatase nanocrystal were studied. When compared with a laponite clay photocatalyst, it was found that anatase dotted TiO2-(B) fibres prepared by a 45 h hydrothermal treatment followed by calcination were not only superior in performance in photocatalysis but could also be readily separated from a slurry system after photocatalytic reactions. This study has laid the foundation for the development of the ability to fabricate highly efficient nanostructured solids for the removal of radioactive ions and organic pollutants from contaminated water. These results now seem set to contribute to the development of advanced water purification devices in the future. These modified nanostructured materials with unusual properties have broadened their application range beyond their traditional use as adsorbents, to also encompass the storage of nuclear waste after concentrating from contaminated water.

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime. The partially observable Markov decision process (POMDP) is a commonly used approach to derive optimal maintenance strategies when asset health inspections are imperfect. However, existing applications of the POMDP to maintenance decision-making largely adopt the discrete time and state assumptions. The discrete-time assumption requires the health state transitions and maintenance activities only happen at discrete epochs, which cannot model the failure time accurately and is not cost-effective. The discrete health state assumption, on the other hand, may not be elaborate enough to improve the effectiveness of maintenance. To address these limitations, this paper proposes a continuous state partially observable semi-Markov decision process (POSMDP). An algorithm that combines the Monte Carlo-based density projection method and the policy iteration is developed to solve the POSMDP. Different types of maintenance activities (i.e., inspections, replacement, and imperfect maintenance) are considered in this paper. The next maintenance action and the corresponding waiting durations are optimized jointly to minimize the long-run expected cost per unit time and availability. The result of simulation studies shows that the proposed maintenance optimization approach is more cost-effective than maintenance strategies derived by another two approximate methods, when regular inspection intervals are adopted. The simulation study also shows that the maintenance cost can be further reduced by developing maintenance strategies with state-dependent maintenance intervals using the POSMDP. In addition, during the simulation studies the proposed POSMDP shows the ability to adopt a cost-effective strategy structure when multiple types of maintenance activities are involved.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Nanowires of metal oxides for gas sensing applications

Nanowires of different metal oxides (SnO2, ZnO) have been grown by evaporation-condensation process. Their chemical composition has been investigated by using XPS. The standard XPS quantification through main photoelectron peaks, modified Auger parameter and valence band spectra were examined for the accurate determination of oxidation state of metals in the nanowires. Morphological investigation has been conducted by acquiring and analyzing the SEM images. For the simulation of working conditions of sensor, the samples were annealed in ultra high vacuum (UHV) up to 500°C and XPS analysis repeated after this treatment. Finally, the nanowires of SnO 2 have were used to produce a novel gas sensor based on Pt/oxide/SiC structure and operating as Schottky diode. Copyright © 2008 John Wiley & Sons, Ltd.

Analysis of efficiency and optimization of plasmon energy coupling into nanofocusing metal wedges

In this paper, we investigate theoretically and numerically the efficiency of energy coupling from a plasmon generated by a grating coupler at one of the interfaces of a metal wedge into the plasmonic eigenmode (i.e., symmetric or quasisymmetric plasmon) experiencing nanofocusing in the wedge. Thus the energy efficiency of energy coupling into metallic nanofocusing structure is analyzed. Two different nanofocusing structures with the metal wedge surrounded by a uniform dielectric (symmetric structure) and with the metal wedge enclosed between a substrate and a cladding with different dielectricpermittivities (asymmetric structure) are considered by means of the geometrical optics (adiabatic) approximation. It is demonstrated that the efficiency of the energy coupling from the plasmon generated by the grating into the symmetric or quasisymmetric plasmon experiencing nanofocusing may vary between ∼50% to ∼100%. In particular, even a very small difference (of ∼1%–2%) between the permittivities of the substrate and the cladding may result in a significant increase in the efficiency of the energy coupling (from ∼50% up to ∼100%) into the plasmon experiencing nanofocusing. Distinct beat patterns produced by the interference of the symmetric (quasisymmetric) and antisymmetric (quasiantisymmetric) plasmons are predicted and analyzed with significant oscillations of the magnetic and electric field amplitudes at both the metal wedge interfaces. Physical interpretations of the predicted effects are based upon the behavior, dispersion, and dissipation of the symmetric (quasisymmetric) and antisymmetric (quasiantisymmetric) filmplasmons in the nanofocusing metal wedge. The obtained results will be important for optimizing metallic nanofocusing structures and minimizing coupling and dissipative losses.