Non-destructive evaluation of carrier transport properties in CuInS2 and CuInSe2 thin films using photothermal deflection technique

Photothermal deflection technique (PTD) is a non-destructive tool for measuring the temperature distribution in and around a sample, due to various non-radiative decay processes occurring within the material. This tool was used to measure the carrier transport properties of CuInS2 and CuInSe2 thin films. Films with thickness <1 μm were prepared with different Cu/In ratios to vary the electrical properties. The surface recombination velocity was least for Cu-rich films (5×105 cm/s for CuInS2, 1×103 cm/s for CuInSe2), while stoichiometric films exhibited high mobility (0.6 cm2/V s for CuInS2, 32 cm2/V s for CuInSe2) and high minority carrier lifetime (0.35 μs for CuInS2, 12 μs for CuInSe2

Destructive and non-destructive measurements of residual crop residue and phosphorus effects on growth and composition of herbaceous fallow species in the Sahel

Little is known about the residual effects of crop residue (CR) and phosphorus (P) application on the fallow vegetation following repeated cultivation of pearl millet [Pennisetum glaucum (L.) R. Br.] in the Sahel. The objective of this study, therefore, was (i) to measure residual effects of CR, mulched at annual rates of 0, 500, 1000 and 2000 kg CR ha^-1, broadcast P at 0 and 13 kg P ha^-1 and P placement at 0, 1, 3, 5 and 7 kg P ha^-1 on the herbaceous dry matter (HDM) 2 years after the end of the experiment and (ii) to test a remote sensing method for the quantitative estimation of HDM. Compared with unmulched plots, a doubling of HDM was measured in plots that had received at least 500 kg CR ha^-1. Previous broadcast P application led to HDM increases of 14% compared with unfertilised control plots, whereas no residual effects of P placement were detected. Crop residue and P treatments caused significant shifts in flora composition. Digital analysis of colour photographs taken of the fallow vegetation and the bare soil revealed that the number of normalised green band pixels averaged per plot was highly correlated with HDM (r=0.86) and that red band pixels were related to differences in soil surface crusting. Given the traditional use of fallow vegetation as fodder, the results strongly suggest that for the integrated farming systems of the West African Sahel, residual effects of soil amendments on the fallow vegetation should be included in any comprehensive analysis of treatment effects on the agro-pastoral system.

A simple and non-destructive model for individual leaf area estimation in citrus

Introduction. Leaf area is often related to plant growth, development, physiology and yield. Many non-destructive models have been proposed for leaf area estimation of several plant genotypes, demonstrating that leaf length, leaf width and leaf area are closely correlated. Thus, the objective of our study was to develop a reliable model for leaf area estimation from linear measurements of leaf dimensions for citrus genotypes. Materials and methods. Leaves of citrus genotypes were harvested, and their dimensions (length, width and area) were measured. Values of leaf area were regressed against length, width, the square of length, the square of width and the product (length x width). The most accurate equations, either linear or second-order polynomial, were regressed again with a new data set; then the most reliable equation was defined. Results and discussion. The first analysis showed that the variables length, width and the square of length gave better results in second-order polynomial equations, while the linear equations were more suitable and accurate when the width and the product (length x width) were used. When these equations were regressed with the new data set, the coefficient of determination (R(2)) and the agreement index 'd' were higher for the one that used the variable product (length x width), while the Mean Absolute Percentage Error was lower. Conclusion. The product of the simple leaf dimensions (length x width) can provide a reliable and simple non-destructive model for leaf area estimation across citrus genotypes.

Non-destructive determination of photosynthetic pigments in the leaves of castor oil plants

Objetivou-se com o presente trabalho, estabelecer a relação entre os pigmentos fotossintéticos extraídos em DMSO e as leituras obtidas no clorofilômetro portátil ClorofiLOG® 1030, gerando modelos matemáticos capazes de predizer os teores de clorofila e de carotenóides em folhas de mamoneira. O trabalho foi conduzido na Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Algodão, situada em Campina Grande, Estado da Paraíba, em outubro de 2010. Para a análise indireta, foi utilizado um equipamento portátil, sendo realizada a leitura em discos foliares com diferentes tonalidades de verde, sendo feita, nesses mesmos discos, a determinação da clorofila pelo método clássico. Para a extração da clorofila, utilizaram-se 5 mL de dimetilsulfóxido (DMSO), a qual foi mantida em banho-maria a 70ºC, por 30 minutos, e retirou-se 3 mL da alíquota para leitura em espectrofotômetro nos comprimentos de onda de 470, 646 e 663 nm. Os dados foram submetidos à análise da variância e regressão polinomial. A leitura obtida no clorofilômetro portátil foi a variável dependente, e os pigmentos fotossintéticos determinados pelo método clássico foi a variável independente. Os resultados indicaram que o clorofilômetro portátil ClorofiLOG® 1030, associado a modelos matemáticos, permitiu estimar a concentração dos pigmentos fotossintéticos, exceto a clorofila b, com alta precisão, com economia de tempo e com reagentes normalmente utilizados nos procedimentos convencionais.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Non-destructive genetic sampling in fish. An improved method for DNA extraction from fish fins and scales

DNA-based studies have been one of the major interests in conservation biology of endangered species and in population genetics. As species and population genetic assessment requires a source of biological material, the sampling strategy can be overcome by non-destructive procedures for DNA isolation. An improved method for obtaining DNA from fish fins and scales with the use of an extraction buffer containing urea and further DNA purification with phenol-chloroform is described. The methodology combines the benefits of a non-destructive DNA sampling and its high efficiency. In addition, comparisons with other methodologies for isolating DNA from fish demonstrated that the present procedure also becomes a very attractive alternative to obtain large amounts of high-quality DNA for use in different molecular analyses. The DNA samples, isolated from different fish species, have been successfully used on random amplified polymorphic DNA (RAPD) experiments, as well as on amplification of specific ribosomal and mitochondrial DNA sequences. The present DNA extraction procedure represents an alternative for population approaches and genetic studies on rare or endangered taxa.

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes

This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Classification of lumber of Eucalyptus using non-destructive tests

Classification and standardization of the sawn wood is a usual activity, developed by countries that come as great consumers of this material. Brazil does not practice the classification of sawn wood. This work had the main objective of evaluating the sensibility of most common non-destructive tests in the classification of dimension lumber from fast grown Eucalyptus plantation. Wood was obtained from genetic material cultivated at Minas Gerais State, Brazil. 296 beams of structural dimensions (6 cm × 12 cm × 280 cm) from 10 different clones of Eucalyptus were sampled. Beams were non-destructively (stress wave, ultrasound and transverse vibration) and destructively (static bending and compression parallel to grain) tested. Non-destructive results showed sensibility in the classification of structural dimension lumber, being possible to establish wave velocity intervals that attend to the main strength classes reported by Wooden Structures Brazilian Code.

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)

Non-destructive equations to estimate the leaf area of Styrax pohlii and Styrax ferrugineus

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

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

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

In vitro mucus transportability, cytogenotoxicity, and hematological changes as non-destructive physiological biomarkers in fish chronically exposed to metals

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

High-throughput non-destructive nuclear magnetic resonance method to measure intramuscular fat content in beef

High intake of saturated fat from meats has been associated with cardiovascular disease, cancer, diabetes, and others diseases. In this paper, we are introducing a simple, high-throughput, and non-destructive low-resolution nuclear magnetic resonance method that has the potential to analyze the intramuscular fat content (IMF) in more than 1,000 beef portions per hour. The results can be used in nutritional fact labels, replacing the currently used average value. The method is based on longitudinal (T(1)) and transverse (T(2)) relaxation time information obtained by a continuous wave-free precession (CWFP) sequence. CWFP yields a higher correlation coefficient (r=0.9) than the conventional Carr-Purcell-Meiboom- Gill (CPMG) method (r=-0.25) for IMF in beef and is just as fast and a simpler pulse sequence than CPMG. The method can also be applied to other meat products.