Methanol oxidation on PtRu/C electrocatalysts prepared by a microemulsion method

PtRu/C nanocatalysts were prepared by a microemulsion method using different values of water/surfactant molar ratio in order to get different particle sizes. Crystallite sizes and structural properties were determined by X-ray diffraction. Particle size and distribution were characterized by transmission electron microscopy and average composition was determined by energy dispersive X-ray analysis. Differential scanning calorimetry measurements indicated the presence of oxides in the as-prepared catalysts. The general electrochemical behavior was evaluated by cyclic voltammetry in 0.5 M sulfuric acid and the electrocatalytic activity towards the oxidation of methanol was studied in 0.5 M methanol acid solutions by potential sweeps and chronoamperometry. copyright The Electrochemical Society.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

Luminescent properties of hybrid materials prepared by the polymeric precursor method

Rare earth complexes (RE) can be incorporated in silica matrixes, originating organic/inorganic hybrid materials with good thermal stability and high rare earth emission lines. In this work, the hybrid material was obtained by the polymeric precursor method and ultrasonic dispersed with spherical silica particles prepared by the Stöber Method. The Raman spectra indicated that the Eu3+ ions are involved in a polymeric structure formed as consequence of the chelation and polyesterification reactions of this ion with citric acid and ethylene glycol. After the ultrasonic stirring, 2-hydroxynicotinic ligand will also compose this polymeric rigid structure. The TGA/DTA analysis showed that this polymeric material was thermal decomposed at 300 °C. Moreover, this process allows the chelating process of the 2-hydroxynicotinic acid ligand to the Eu3+ ions. The 29Si NMR showed that the ultrasonic dispersion of the reactants was not able to promote the functionalization of the silica particles with the 2-hydroxynicotinic acid ligand. Moreover, heat treatment promotes the [Eu(HnicO2)3] complex particles incorporation into silica pores. At this temperature, the TGA curve showed that only the thermal degradation of ethylene glycol and citric acid used during the experimental procedure occurs. The silica and hybrid materials are composed by spherical and aggregated particles with particle size of approximately 450 nm, which can be influenced by the heat treatment. These materials also present an absorption band located at 337 nm. The photoluminescent study showed that when the hybrid samples were excited at 337 nm wavelength, the ligand absorbs the excitation light. Part of this energy is transferred to the Eu3+ ion, which main emission, 5D0→ 7F2, is observed in the emission spectrum at 612 nm. As the heating temperature increases to 300 C, the energy transfer is more favorable. The lifetime values showed that the Eu3+ emission is enhanced due to the energy transfer process in the powders. © 2013 Elsevier B.V. All rights reserved.

Multiplicity and transverse momentum dependence of two- and four-particle correlations in pPb and PbPb collisions

Measurements of two- and four-particle angular correlations for charged particles emitted in pPb collisions are presented over a wide range in pseudorapidity and full azimuth. The data, corresponding to an integrated luminosity of approximately 31nb-1, were collected during the 2013 LHC pPb run at a nucleon-nucleon center-of-mass energy of 5.02 TeV by the CMS experiment. The results are compared to 2.76 TeV semi-peripheral PbPb collision data, collected during the 2011 PbPb run, covering a similar range of particle multiplicities. The observed correlations are characterized by the near-side (|δφ|≈0) associated pair yields and the azimuthal anisotropy Fourier harmonics (vn). The second-order (v2) and third-order (v3) anisotropy harmonics are extracted using the two-particle azimuthal correlation technique. A four-particle correlation method is also applied to obtain the value of v2 and further explore the multi-particle nature of the correlations. Both associated pair yields and anisotropy harmonics are studied as a function of particle multiplicity and transverse momentum. The associated pair yields, the four-particle v2, and the v3 become apparent at about the same multiplicity. A remarkable similarity in the v3 signal as a function of multiplicity is observed between the pPb and PbPb systems. Predictions based on the color glass condensate and hydrodynamic models are compared to the experimental results. © 2013 CERN.

Development of a new method to prepare nano-/microparticles loaded with extracts of azadirachta indica, their characterization and use in controlling plutella xylostella

Biodegradable nanoparticles have been widely explored as carriers for controlled delivery of therapeutic molecules; however, studies describing the development of nanoparticles as carriers for biopesticide products are few. In this work, a new method to prepare nanoparticles loaded with neem (Azadirachta indica) extracts is presented. In this study, nanoparticles were formulated as colloidal suspension and (spray-dried) powder and characterized by evaluating pH, particle size, zeta potential, morphology, absolute recovery, and entrapment efficiency. A high-performance liquid chromatography method was used for nanoparticle characterization. The best formulations presented absolute recovery and entrapment efficiencies of approximately 100% and a release profile based on swelling and relaxation of the polymer or polymer erosion. The biological data of the formulated products against Plutella xylostella showed 100% larval mortality. The nanoparticle information improved the stability of neem products against ultraviolet radiation and increased their dispersion in the aqueous phase. © 2013 American Chemical Society.

Determination of reactivity rates of silicate particle-size fractions

The efficiency of sources used for soil acidity correction depends on reactivity rate (RR) and neutralization power (NP), indicated by effective calcium carbonate (ECC). Few studies establish relative efficiency of reactivity (RER) for silicate particle-size fractions, therefore, the RER applied for lime are used. This study aimed to evaluate the reactivity of silicate materials affected by particle size throughout incubation periods in comparison to lime, and to calculate the RER for silicate particle-size fractions. Six correction sources were evaluated: three slags from distinct origins, dolomitic and calcitic lime separated into four particle-size fractions (2, 0.84, 0.30 and <0.30-mm sieves), and wollastonite, as an additional treatment. The treatments were applied to three soils with different texture classes. The dose of neutralizing material (calcium and magnesium oxides) was applied at equal quantities, and the only variation was the particle-size material. After a 90-day incubation period, the RER was calculated for each particle-size fraction, as well as the RR and ECC of each source. The neutralization of soil acidity of the same particle-size fraction for different sources showed distinct solubility and a distinct reaction between silicates and lime. The RER for slag were higher than the limits established by Brazilian legislation, indicating that the method used for limes should not be used for the slags studied here.

Synthesis and photocatalytic properties of bismuth titanate with different structures via oxidant peroxo method (OPM)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Quantification of Doxycycline Hyclate in Tablets by HPLC-UV Method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Non-destructive thermal wave method applied to study thermal properties of fast setting time endodontic cement

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

EVALUATION OF ROUGHNESS, WETTABILITY, AND MORPHOLOGY OF AN YTTRIA-STABILIZED TETRAGONAL ZIRCONIA POLYCRYSTAL CERAMIC AFTER DIFFERENT AIRBORNE-PARTICLE ABRASION PROTOCOL

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

CCDM model from quantum particle creation: constraints on dark matter mass

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

TiO2/CuO films obtained by citrate precursor method for photocatalytic application

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)