Nitrate reduction at Pt(1 0 0) single crystals and preferentially oriented nanoparticles in neutral media

The electroreduction of nitrate on Pt(1 0 0) electrodes in phosphate buffer neutral solution, pH 7.2, is reported. The sensitivity of the reaction to the crystallographic order of the surface is studied through the controlled introduction of defects by using stepped surfaces with (1 0 0) terraces of different length separated by monoatomic steps, either with (1 1 1) or (1 1 0) symmetry. The results of this study show that nitrate reduction occurs mainly on the well defined (1 0 0) terraces in the potential region where H adsorption starts to decrease, allowing the nitrate anion to access the surface. Adsorbed NO has been detected as a stable intermediate in this media. An oxidation process observed at 0.8 V has been identified as leading to the formation of adsorbed NO and being responsible for a secondary reduction process observed in the subsequent negative scan. Using in situ FTIRS, ammonium was found to be the main product of nitrate reduction. This species can be oxidized at high potentials resulting in adsorbed NO and nitrate (probably with nitrite as intermediate).

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Organotitanias: a versatile approach for band gap reduction in titania based materials

The reduction of the band gap of titania is critically important to fully utilize its photocatalytic properties. Two main strategies, i.e. doping and partial reduction of Ti(IV), are the main alternatives available to date. Herein, we report a new synthesis strategy based on one-pot co-condensation of in situ prepared polymetallic titanium-alkoxide complexes with titanium tetrabutoxide. Using this direct reaction, it is possible to introduce organic compounds in the anatase phase, causing site distortions in the crystalline structure of the network. By using this strategy, a yellow and a black titania have been produced, with the latter showing a remarkable photocatalytic activity under visible-light.