An investigation was conducted into the effects of double-pulsed laser excitation on the spectra emitted by steel targets in air at atmosphere pressure. Double-pulsed excitation was found to produce an average factor of seven increase in spectral line intensity over single-pulsed excitation of the same total laser energy. The emitted spectral line intensity and plasma electron temperature were strong functions of the laser pulse energy ratio, with both values maximised when the metal was excited by a low-energy first pulse, followed by a high-energy second pulse. The variation of laser pulse separation over the range 7.6s to 30s was found to produce no measurable variation in electron temperature. A model is proposed to explain the effects of double-pulsed laser excitation, based on the re-generation of the primary plasma by UV radiation, energetic ions and shock wave produced by the secondary plasma within the atomised remnants of the primary plasma. The resolution of chromium in a range of steels using single-pulsed and double-pulsed Laser-Induced Breakdown Spectroscopy (LIBS) was investigated. Optical and laser parameters were optimised for each excitation regime in order to maximise the electron temperature of the laser-induced plasma. The slope of the calibration curve produced by double-pulsed LIBS was in the order of 0.55 (depending on line-pair combination), compared with approximately 0.75 for single-pulsed LIBS. The improvement in the correlation coefficient of the calibration curves from an average of 0.984 for single-pulse to 0.996 for double-pulse was attributed to the increase in S/N ratio produced by double-pulsed excitation. Sorting of the spectra into electron temperature-defined subsets resulted in a minimum average standard deviation in measured intensity ratio for narrowest temperature band. The correlation coefficients of the calibration curves were not improved by this process, through the resultant severe reduction in sample size.

This paper presents the results of the spectrochemical analysis of a suite of rocks and rock minerals from the White Mountain magma series of New Hampshire and of some Finnish granites. The analysis elements are gallium, tin, lead, lithium, copper, and silver. / Thesis / Master of Science (MS)

Made available in DSpace on 2016-12-23T14:07:27Z (GMT). No. of bitstreams: 1 Cleber F Depizzol bloqueada.pdf: 851798 bytes, checksum: 2a03d60f2abf5d40c1d90eca3f9e20d9 (MD5) Previous issue date: 2011-12-12 / O comportamento oscilatório de malhas de controle pode ser causado por variados motivos que são inerentes ao processo. Tal comportamento aumenta o custo e reduz a qualidade do produto final. Este assunto tem sido amplamente estudado, entretanto até mesmo algoritmos robustos tem apresentado falhas quando o sinal analisado tem forte presença de ruído, distúrbios não periódicos, e=ou possui baixa freqüência de oscilação. Este trabalho discute o algoritmo Detecção e Caracterização de Oscilação (ODC), um algorimo conhecido da literatura o qual baseia-se na medição dos períodos do sinal autocorrelacionado obtido pela inversa da transformada de Fourier aplicada sobre a densidade espectral de potência, a fim de que a presença do comportamento oscilatório seja caracterizado. Vários problemas verificados no algoritmo são minimizados através da aplicação das propostas deste trabalho. A análise da causa raiz também é considerada, comparando a energia dos sinais oscilatórios calculados usando o gráfico da densidade espectral de potência. As contribuições deste trabalho são destacadas através de sua aplicação a dados de quatro diferentes processos industriais / The oscillatory control loop behavior may be caused by different reasons, which are inherent to the industrial processes. Such behavior increases the costs and decreases the quality of the final products. This subject has being widely studied, however even robust algorithms present false results when the analyzed signals are highly influenced by noise, nonperiodic perturbations, or very low frequencies. This work discusses the algorithm Oscilation Detection and Caracterization, a well-known algorithm used to detect and characterize oscillations based on the measurement of the period of the autocorrelated signal obtained from the inverse Fourier transform of the power spectral density. Several drawbacks of the algorithm are overcome through the application of our proposals. The root cause problem is also addressed, comparing the energy of the oscillatory signals calculated in the power spectral density plot. The contributions of this work are highlighted through its application to data of four different industrial processes