In order to maintain the pipeline better and remove the dirt more effectively, it was necessary to analyze the contents of elements in dirt. Mg in soil outside of the pipe and the dirt inside of the pipe was quantitatively analyzed and compared by using the laser-induced breakdown spectroscopy (LIBS). Firstly, Mg was quantitatively analyzed on the basis of Mg I 285.213 nm by calibration curve for integrated intensity and peak intensity of the spectrum before and after subtracting noise, respectively. Then calibration curves on the basis of Mg II 279.553 nm and Mg II 280.270 nm were analyzed. The results indicated that it is better to use integrated intensity after subtracting noise of the spectrum line with high relative intensity to make the calibration curve.
Metallic nanopowders have an increasing application in magnetic materials, catalysts and chemical and metallic industries. In this research, a novel bulk synthesis method for preparing high pure intermetallic Fe 3 Al nanoparticles was developed by flow-levitation (FL) method. The Fe and Al vapors ascending from the high-temperature levitated droplet were condensed by cryogenic argon gas under atmospheric pressure. X-ray diffraction (XRD) and selected-area electron diffraction (SAED) were used to identify and characterize the prepared nanopowders exhibiting a Fe 3 Al phase. Measurement of transmission electron microscopy (TEM) indicated that the Fe 3 Al particles are nearly spherical, and the particle size of the compound ranges from 10 nm to 200 nm in diameter. The chemical composition of the nanoparticles were determined with energy dispersive spectrometer. The magnetic properties of the nanopowder indicate that Fe 3 Al intermetallic compound is a soft magnet at room temperature, with coercivity of 24.2 Oe and saturation magnetization of 173.2 emu/g. The production rate of Fe 3 Al nanoparticles was estimated to be about 4 g/h in a continuous manner, by using the FL method. This method has great potential in mass production of Fe 3 Al nanoparticles.
The LIBS (Laser induced-breakdown spectroscopy) combined with BPNN (Back propagation neural network) was applied in rock sorting and distinguishing for 26 rock samples of 6 types. According to contents of major elements in samples, we selected lines of Si, Al, Fe, K, Ca, Mg, Na, Ti and Mn. These lines of 9 elements composed three characteristic spectral models which were the WSLM (Wide spectral line model), the PM (Peak model) and the PRM (Peak ratio model). The first and the second characteristic spectral model were divided into 9 kinds, as follows: the characteristic spectrum with 1 element, the characteristic spectrum with 2 elements, we can deduce the rest from this and the last one has 9 elements. The third model was divided into 8 kinds which were using Al as reference element. We analysed spectrums of the three models by BPNN. Experimental results shown that whether sorting or distinguishing these samples, identification accuracies of the PM were more than that of the PRM overall, the same as the WSLM did to the PM. While the selected number of elements was 5, 6 or 7, the identification accuracy of the WSLM could reach more than 90%. Continuing to add the number of elements to improve identification accuracy was not very obvious.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.