Monitoring of diclofenac with biomimetic sensor in batch and FIA systems

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

Estudo de filme fino modificado por eletropolimerização de fenotiazina em FTO (óxido de estanho dopado com flúor) para construção de sensor eletroquímico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An Electrochemical Sensor for Reducing Sugars Based on a Glassy Carbon Electrode Modified with Electropolymerized Molecularly Imprinted Poly-o-phenylenediamine Film

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

Electrochemical Properties of the Oxo-Manganese-Phenanthroline Complex Immobilized on Ion-Exchange Polymeric Film and Its Application as Biomimetic Sensor for Sulfite Ions

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

Highly Sensitive Electrochemical Sensor for Determination of Vitamin D in Mixtures of Water-Ethanol

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

Gas sensor applications of zinc oxide thin film grown by the polymeric precursor method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

DEVELOPMENT OF A BIOMIMETIC SENSOR MODIFIED WITH HEMIN AND GRAPHENE OXIDE FOR MONITORING OF CARBOFURAN IN FOOD

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

Synthesis and characterization of alpha-nickel (II) hydroxide particles on organic-inorganic matrix and its application in a sensitive electrochemical sensor for vitamin D determination

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

Determination of carbofuran and diuron in FIA system using electrochemical sensor modified with organometallic complexes and graphene oxide

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

Voltammetric sensor for simultaneous determination of p-phenylenediamine and resorcinol in permanent hair dyeing and tap water by composite carbon nanotubes/chitosan modified electrode

p-Phenylenediamine (PPD) and resorcinol (RSN) are hair dye precursors of permanent dyeing more used worldwide. The present work describes a simple and economic voltammetric sensor for simultaneous determination of both components in commercial hair dyeing and tap water at low concentrations. PPD and RSN are oxidized at + 0.17 and + 0.61 V vs. Ag/AgCl at glassy carbon electrode coated by composites of multiwall carbon nanotubes with chitosan (MWNTs-CHT/GCE), which anodic currents density normalized are 10% and 70% higher in relation to the unmodified electrode, respectively. The calibration curve for simultaneous determination of PPD and RSN showed linearity between 0.55 and 21.2 mg L-1 with detection limits of 0.79 and 0.58 mg L-1 to PPD and RSN, respectively. The relative standard deviations found for ten determinations were of 0.73 and 2.35% to 2.70 mg L-1, and 0.87 and 1.08% to 15.96 mg L-1 to PPD and RSN, respectively. The voltammetric sensor was applied to determination of PPD and RSN in tap water and commercial hair dyeing samples and the average recovery for these samples was around 97%. The products generated from PPD and RSN reaction such as was p-quinonediimine and bandrowski base were detected by LC-MS/MS and UV-vis spectrophotometry. (C) 2014 Published by Elsevier B.V.

Electroanalytical determination of bumetanide employing a biomimetic sensor for detection of doping in sports

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Controlled Synthesis of Layered Sn3O4 Nanobelts by Carbothermal Reduction Method and Their Gas Sensor Properties

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

INFLUENCE OF NOZZLE TYPE AND SPRAY RATES ON CORN AND WEED PLANTS DROP DEPOSITIONS

The objective of this study was to evaluate different nozzles and spray rates on drop deposition in corn (Zea mays), Euphorbia heterophylla and Brachiaria plantaginea, both weeds located at and between crop rows. The experimental design established was complete random blocks with treatments arranged at 2 x 2 factorial scheme (2 nozzles types: DG11002VS flat flan and medium droplets, TXVK08 cone and very fine droplets; and 2 rates: 100 and 200 L ha(-1)) with four replications. The spray applications occurred at 13 days after corn germination (3-5 expanded leaves), when E. heterophylla and B. plantaginea plants had 2-4 and 2-3 leaves, respectively. Solution of Brilliant Blue (FD&C-1) dye at 3,000 ppm was used as spray tracer. It was concluded that the greatest average deposits in corn plants was provided by TXVK08, independently of the spray rates used. The most uniform deposits occurred when the spray rates of 200 L ha(-1) was used. Spray deposits were most uniform in B. plantaginea compared to E. heterophylla when both weds were located at crop row, independently of nozzle or spray rates. However, the DG 11002VS spray nozzle provided the most uniform drop deposition on B. plantaginea located between the rows, while the most efficient deposition over E. heterophylla located between rows was TXVK08.

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.