Layer-by-Layer Technique as a New Approach to Produce Nanostructured Films Containing Phospholipids as Transducers in Sensing Applications

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

Development of a sensitive potentiometric sensor for determination of fumaric acid in powdered food products

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

Fluorometric fiber optic drop sensor for atmospheric hydrogen sulfide

A fluorometric technique based on a liquid drop excited from its interior by an optical fiber is described for the measurement of low concentrations of atmospheric hydrogen sulfide (H2S). A drop of alkaline fluorescein mercuric acetate (FMA) solution is suspended in a flowing air sample stream and serves as a renewable sensor. An optical fiber contained within the conduit that forms the drop, brings in the excitation beam; the fluorescence emission is measured by an inexpensive photodiode positioned close to the drop. As H2S in the sample is collected by the alkaline drop, it reacts rapidly with FMA resulting in a significant decrease in fluorescence intensity, proportional to the concentration of H2S sampled. The chemistry of this uniquely selective reaction has been well established for many years, the present technique permits a simple fast inexpensive near real-time measurement with very little reagent consumption. Even without prolonged sampling/preconcentration steps, limits of detection (LODs) in the double digit ppbv range is readily attainable. (C) 1997 Elsevier B.V. B.V.

An application of neural network technique to correct the dome temperature effects on pyrgeometer measurements

This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

Renewable drops electrochemical sensor for sulfide ions detection

The design and characteristics of a novel electrochemical system, which uses a drop as a renewable electroanalytical sensor, are described. This article describes the performance of the electrochemical system, the coupling of the experimental arrangement with flow injection technique and a demonstration of its applicability for the measurement of sulfide. The method is based on renewable drops of ferricyanide ions, buffered by borate. The ferrocyanide ions, product of the reaction between ferricyanide and sulfide ions, are oxidized on a platinum microelectrode and the current measured is related to sulfide concentration. The measurements can be done in continuous or static flow mode. In continuous mode, the detection limit is 5.0 x 10(-5) mol L-1.

Linear readout of dynamic phase modulation index in an optical voltage sensor using the new J(1) ... J(3) spectral method

The J(1)...J(3) is a recent optical method for linear readout of dynamic phase modulation index in homodyne interferometers. In this work, the J(1)... J(3) method is applied to measure voltage in an optical voltage sensor. Based on the classical J(1)...J(4) method, the J(1)... J(3) technique shows to be more stable to phase drift and simpler for implementation than the original one. The sensor dynamic range is enhanced. The agreement between theoretical and experimental results, based on 1/f noise, is demonstrated.

Fabrication of phytic acid sensor based on mixed phytase-lipid Langmuir-Blodgett films

This paper reports the surface activity of phytase at the air-water interface, its interaction with lipid monolayers, and the construction of a new phytic acid biosensor on the basis of the Langmuir-Blodgett (LB) technique. Phytase was inserted in the subphase solution of dipalmitoylphosphatidylglycerol (DPPG) Langmuir monolayers, and its incorporation to the air-water interface was monitored with surface pressure measurements. Phytase was able to incorporate into DPPG monolayers even at high surface pressures, ca. 30 mN/m, under controlled ionic strength, pH, and temperature. Mixed Langmuir monolayers of phytase and DPPG were characterized by surface pressure-area and surface potential-area isotherms, and the presence of the enzyme provided an expansion in the monolayers ( when compared to the pure lipid at the interface). The enzyme incorporation also led to significant changes in the equilibrium surface compressibility (in-plane elasticity), especially in liquid-expanded and liquid-condensed regions. The dynamic surface elasticity for phytase-containing interfaces was investigated using harmonic oscillation and axisymmetric drop shape analysis. The insertion of the enzyme at DPPG monolayers caused an increase in the dynamic surface elasticity at 30 mN m(-1), indicating a strong interaction between the enzyme and lipid molecules at a high-surface packing. Langmuir-Blodgett (LB) films containing 35 layers of mixed phytase-DPPG were characterized by ultraviolet-visible and fluorescence spectroscopy and crystal quartz microbalance nanogravimetry. The ability in detecting phytic acid was studied with voltammetric measurements.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.

Detection of buried pipes using a shear wave ground surface vibration technique

A major UK initiative, entitled 'Mapping the Underworld', is seeking to address the serious social, environmental and economic consequences arising from an inability to locate the buried utility service infrastructure without resorting to extensive excavations. Mapping the Underworld aims to develop and prove the efficacy of a multi-sensor device for accurate remote buried utility service detection, location and, where possible, identification. One of the technologies to be incorporated in the device is low-frequency vibro-acoustics, and the application of this technology for detecting buried infrastructure is currently being investigated. Here, a shear wave ground vibration technique for detecting buried pipes is described. For this technique, shear waves are generated at the ground surface, and the resulting ground surface vibrations measured, using geophones, along a line traversing the anticipated run of the pipe. Measurements were made at a test site with a single pressurized polyethylene mains water pipe. Time-extended signals were employed to generate the illuminating wave. Cross-correlation functions between the measured ground velocities and a reference measurement adjacent to the excitation were then calculated and summed using a stacking method to generate a cross-sectional image of the ground. The wide cross-correlation peaks caused by high ground attenuation were partially compensated for by using a generalized cross-correlation function called the smoothed coherence transform. To mitigate the effects of other potential sources of vibration in the vicinity, the excitation signal was used as an additional reference when calculating the generalized cross-correlation functions. For two out of three tests, the pipe was detected, indicating that this technique will be a valuable addition to the Mapping the Underworld armoury.

Sensor for diuron quantitation based on the P450 biomimetic catalyst nickel(II) 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine

A biomimetic sensor based on a carbon paste electrode modified with the nickel(II) 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine complex was developed as a reliable alternative technique for the sensitive and selective analysis of the herbicide diuron in environmental media. The sensor was evaluated using cyclic voltammetry and amperometric techniques. The best amperometric responses were obtained at 750 mV vs. Ag/AgCl (KClsat), using 0.1 mol L-1 phosphate buffer solution at pH 8.0. Under these conditions, the sensor showed a linear response for diuron concentrations between 9.9 × 10-6 and 1.5 × 10-4 mol L -1, a sensitivity of 22817 (±261) μA L mol-1, and detection and quantification limits of 6.14 × 10-6 and 2 × 10-5 mol L-1, respectively. The presence of the nickel complex in the carbon paste improved selectivity, stability, and sensitivity (which increased 700%), compared to unmodified paste. The applicability of the sensor was demonstrated using enriched environmental samples (river water and soil). © 2012 Elsevier B.V. All rights reserved.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Sensor em fibra óptica aplicado à caracterização de atuadores piezoelétricos flextensionais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An alternative method for the simultaneous determination of copper and lead for quality control of sugar cane spirit using a nanotube-based sensor

The development of an electroanalytical method for simultaneous determination of copper and lead ions in sugar cane spirit (cachaça) using carbon paste electrode modified with ascorbic acid and carbon nanotubes (CPE-AaCNT) is described. Squarewave voltammetry (SWV) with anodic stripping was employed, and this technique was optimized with respect to the following parameters: frequency (50 Hz), amplitude (100 mV) and scan increment (9 mV). The analytical curves were linear in the range from 0.0900 to 7.00 mg L - 1 for lead and copper. The limits of detection were 48.5 and 23.9 µg L - 1 for lead and copper, respectively. The developed method was applied to the simultaneous determination of copper and lead in five commercial samples of sugar cane spirit. The results were in good agreement with those obtained by F AAS/GF AAS (flame atomic absorption spectrometry/graphite furnace atomic absorption spectrometry) and showed that CPE-AaCNT can be successfully employed in the simultaneous determination of these metals in real sugar cane spirit samples.

Desenvolvimento de método analítico para determinação de ozônio residual em água utilizando a ténica de fluorescência e estudo da viabilidade da construção de um sensor fluorimétrico em uma gota de solução para determinação de ozônio em ar

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento de sensor elétrico de contato e correlações com atributos do solo visando à agricultura de precisão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)