Pozzolanic behavior of bamboo leaf ash: Characterization and determination of the kinetic parameters

The paper presents a characterization and study of the pozzolanic behavior between calcium hydroxide (CH) and bamboo leaf ash (BLAsh), which was obtained by calcining bamboo leaves at 600 degrees C for 2 h in a laboratory electric furnace. To evaluate the pozzolanic behavior the conductometric method was used, which is based on the measurement of the electrical conductivity in a BLAsh/CH solution with the reaction time. Later, the kinetic parameters are quantified by applying a kinetic-diffusive model. The kinetic parameters that characterize the process (in particular, the reaction rate constant and free energy of activation) were determined with relative accuracy in the fitting process of the model. The pozzolanic activity is quantitatively evaluated according to the values obtained of the kinetic parameters. Other experimental techniques, such as X-ray diffraction (XRD) and scanning electron microscopy (SEM), were also employed. The results show that this kind of ash is formed by silica with a completely amorphous nature and a high pozzolanic activity. The correlation between the values of free energy of activation (Delta G(#)) and the reaction rate constants (K) are in correspondence with the theoretical studies about the rate processes reported in the literature. (C) 2010 Elsevier Ltd. All rights reserved.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.