Determination of low-aliphatic aldehydes indoors by micellar electrokinetic chromatography using sample dissolution manipulation for signal enhancement

This work describes a novel approach for the analysis of selected aldehydes (formaldehyde, acetaldehyde, propionaldehyde, and acrolein) and acetone in environmental samples using micellar electrokinetic chromatography (MEKC). The method is based on the reaction of carbonyl compounds with 3-methyl-2-benzothiazoline hydrazone (MBTH) that gives an azine intermediate with maximum absorbance at 216 nm. A systematic evaluation of sample dissolution medium was conducted as a means to enhancing sensitivity. In the best condition, samples were dissolved in 0.030 mol.L-1 tetraborate solution. This condition presented enhancement factors in the range of 35-54 for the aldehydes under investigation, computed as the improvement of the concentration limits of detection (LODs) with reference to the sample dissolved in pure water. The running buffer was 0.020 mol.L-1 tetraborate, pH 9.3, containing 0.050 mol-L-1 sodium dodecyly sulfate (SIDS). The overall methodology presented several advantages over established methods for aldehydes. Worthy mentioning that MBTH is available in high purity degree, dispensing laborious reagent purification procedures. A few method validation parameters were determined revealing good migration time repeatability (< 2.5% coefficient of variation, CV) and area repeatability (< 4% CV), excellent linearity (20-120 mug/L, r > 0.995) and adequate sensitivity for environmental applications. The LODs with respect to each single aldehyde were in the range of 0.54-4.0 mug.L-1 and 11 mug.L-1 for acetone. The methodology was applied to the determination of aldehydes indoors. Samples were collected in an impinger flask containing 0.05% MBTH solution, at a flow rate of 0.80 L.min(-1), during 2.5 h, at different times during the day. The most abundant carbonyls in the samples were acetone, followed by formaldehyde and acetaldehyde, with estimate peak concentrations of 452, 5.2 and 2.2 ppbv, respectively.

Determination of acetaldehyde in fuel ethanol by high-performance liquid chromatography with electrochemical detection

The cyclic voltammetric behavior of acetaldehyde and the derivatized product with 2,4-dinitrophenylhydrazine (DNPHi) has been studied at a glassy carbon electrode. This study was used to optimize the best experimental conditions for its determination by high-performance liquid chromatographic (HPLC) separation coupled with electrochemical detection. The acetaldehyde-2,4-dinitrophenyl.hydrazone (ADNPH) was eluted and separated by a reversed-phase column, C-18, under isocratic conditions with the mobile phase containing a binary mixture of methanol/LiCl(aq) at a concentration of 1.0 x 10(-3) M (80:20 v/v) and a flow rate of 1.0 mL min(-1). The optimum condition for the electrochemical detection of ADNPH was +1.0 V vs. Ag/AgCl as a reference electrode. The proposed method was simple, rapid (analysis time 7 min) and sensitive (detection limit 3.80 mu g L-1) at a signal-to-noise ratio of 3:1. It was also highly selective and reproducible [standard deviation 8.2% +/- 0.36 (n = 5)]. The analytical curve of ADNPH was linear over the range of 3-300 mg L-1 per injection (20 mu L), and the analytical recovery was > 99%.

Determination of rice herbicides, their transformation products and clofibric acid using on-line solid-phase extraction followed by liquid chromatography with diode array and atmospheric pressure chemical ionization mass spectrometric detection

A simultaneous method for the trace determination of acidic, neutral herbicides and their transformation products in estuarine waters has been developed through an on-line solid-phase extraction method followed by liquid chromatography with diode array and mass spectrometric detection. An atmospheric pressure chemical ionization (APCI) interface was used in the negative ionization mode after optimization of the main APCI parameters. Limits of detection ranged from 0.1 to 0.02 ng/ml for 50 mi of acidified estuarine waters preconcentrated into polymeric precolumns and using time-scheduled selected ion monitoring mode. Two degradation products of the acidic herbicides (4-chloro-2-methylphenol and 2,4-dichlorophenol) did not show good signal response using APCI-MS at the concentration studied due to the higher fragmentor voltage needed for their determination For molinate and the major degradation product of propanil, 3,4-dichloroaniline, positive ion mode was needed for APCI-MS detection. The proposed method was applied to the determination of herbicides in drainage waters from rice fields of the Delta del Ebro (Spain). During the S-month monitoring of the herbicides, 8-hydroxybentazone and 4-chloro-2-methylphenoxyacetic acid were successively found in those samples. (C) 2000 Elsevier B.V. B.V. All rights reserved.

A new technique for thermal neutron detection using pyroelectric ceramics

In this article a new technique for thermal neutron detection using pyroelectric ceramics is described. The detector system is basically constituted of a PZT (lead zirconate titanate) ceramic attached to an uranium disk. The energy released in the uranium fission gives rise to an electrical signal in the detector which is amplified by a lock-in system. The neutron beam impinging on the uranium disk was modulated with a cadmium chopper. Thermal neutron fluxes within the interval of 103 to 106 n/cm2 s have been detected using a U3O8 pellet with 20% enrichment in 235U. © 1992.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece `burn', the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.

In-process thermal damage detection in grinding with monitoring via Internet

This work aims the development of a dedicated system for detection of burning in surface grinding process, where the process will constantly be monitored through the acoustic emission and electric power of the induction motor drive. Acquired by an analog-digital converter, algorithms process the signals and a control signal is generated to inform the operator or interrupt the process in case of burning occurrence. Moreover, the system makes possible the process monitoring via Internet. Additionally, a comparative study between parameters DPO and FKS is carried through. In the experimental work one type of. steel (ABNT-1020 annealed) and one type of grinding wheel referred to as TARGA, model ART 3TG80.3 NVHB, were employed.

Automatic system for thermal damage detection in manufacturing process with Internet monitoring

This work involved the development of a smart system dedicated to surface burning detection in the grinding process through constant monitoring of the process by acoustic emission and electrical power signals. A program in Visual Basic® for Windows® was developed, which collects the signals through an analog-digital converter and further processes them using burning detection algorithms already known. Three other parameters are proposed here and a comparative study carried out. When burning occurs, the newly developed software program sends a control signal warning the operator or interrupting the process, and delivers process information via the Internet. Parallel to this, the user can also interfere in the process via Internet, changing parameters and/or monitoring the grinding process. The findings of a comparative study of the various parameters are also discussed here. Copyright © 2006 by ABCM.

Experimental damage detection using system norms

Nowadays there is great interest in structural damage detection in systems using nondestructive tests. Once the failure is detected, as for instance a crack, it is possible to take providences. There are several different approaches that can be used to obtain information about the existence, location and extension of the fault in the system by non-destructive tests. Among these methodologies, one can mention different optimization techniques, as for instance classical methods, genetic algorithms, neural networks, etc. Most of these techniques, which are based on element-byelement adjustments of a finite element (FE) model, take advantage of the dynamic behavior of the model. However, in practical situations, usually, is almost impossible to obtain an accuracy model. In this paper, it is proposed an experimental technique for damage location. This technique is based on H: norm to obtain the damage location. The dynamic properties of the structure were identified using experimental data by eigensystem realization algorithm (ERA). The experimental test was carried out in a beam structure through varying the mass of an element. For the output signal was used a piezoelectric sensor. The signal of input of sine form was generated through SignalCalc® software.

A methodology and a tool to design of mixed-signal technology

This paper presents a tool box developed to read files describing a SIMULINK® model and translates it into a structural VHDL-AMS description. In translation process, all files and directory structures to simulate the translated model on SystemVision™ environment is generate. The tool box named MS2SV was tested by three models of commercially available digital-to-analogue converters. All models use the R2R ladder network to conversion, but the functionality of these three components is different. The methodology of conversion of the model is presents together with sort theory about R-2R ladder network. In the evaluation of the translated models, we used a sine waveform input signal and the waveform generated by D/A conversion process was compared by FFT analysis. The results show the viability of this type of approach. This work considers some of challenges set by the electronic industry for the further development of simulation methodologies and tools in the field of mixed-signal technology. © 2007 IEEE.

Border detection in digital images: An approach by fuzzy numbers

The purpose of this paper is to introduce a new approach for edge detection in gray shaded images. The proposed approach is based on the fuzzy number theory. The idea is to deal with the uncertainties concerning the gray shades making up the image, and thus calculate the appropriateness of the pixels in relation to an homogeneous region around them. The pixels not belonging to the region are then classified as border pixels. The results have shown that the technique is simple, computationally efficient and with good results when compared with both the traditional border detectors and the fuzzy edge detectors. © 2007 IEEE.

CMOS-based active pixel for low-light-level detection: Analysis and measurements

An analysis of the active pixel sensor (APS), considering the doping profiles of the photodiode in an APS fabricated in a 0.18 μm standard CMOS technology, is presented. A simple and accurate model for the junction capacitance of the photodiode is proposed. An analytic expression for the output voltage of the APS obtained with this capacitance model is in good agreement with measurements and is more accurate than the models used previously. A different mode of operation for the APS based on the dc level of the output is suggested. This new mode has better low-light-level sensitivity than the conventional APS operating mode, and it has a slower temporal response to the change of the incident light power. At 1μW/cm2 and lower levels of light, the measured signal-to-noise ratio (SNR) of this new mode is more than 10 dB higher than the SNR of previously reported APS circuits. Also, with an output SNR of about 10 dB, the proposed dc level is capable of detecting light powers as low as 20 nW/cm2, which is about 30 times lower than the light power detected in recent reports by other groups. © 2007 IEEE.

Detection of highways in high resolution images using Mathematical Morphology techniques

This paper seeks to apply a routine for highways detection through the mathematical morphology tools in high resolution image. The Mathematical Morphology theory consists of describing structures geometric presents quantitatively in the image (targets or features). This explains the use of the Mathematical Morphology in this work. As high resolution images will be used, the largest difficulty in the highways detection process is the presence of trees and automobiles in the borders tracks. Like this, for the obtaining of good results through the use of morphologic tools was necessary to choose the structuring element appropriately to be used in the functions. Through the appropriate choice of the morphologic operators and structuring elements it was possible to detect the highways tracks. The linear feature detection using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating.

False alarms in damage detection approaches

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in structures in order to improve reliability and reduce life-cycle costs. The greatest challenge for designing a SHM system is knowing what changes to look for and how to classify them. Different approaches for SHM have been proposed for damage identification, each one with advantages and drawbacks. This paper presents a methodology for improvement in vibration signal analysis using statistics information involving the probability density. Generally, the presence of noises in input and output signals results in false alarms, then, it is important that the methodology can minimize this problem. In this paper, the proposed approach is experimentally tested in a flexible plate using a piezoelectric (PZT) actuator to provide the disturbance.