Optical Emission Diagnostics Of Laser Produced Plasma From Graphite And Yba2cu3o7

This thesis is entitled “OPTICAL EMISSION DIAGNOSTICS OF LASER PRODUCED PLASMA FROM GRAPHITE AND YBa2Cu3O7. The work presented in this thesis covers the experimental results on the plasma produced with moderately high power laser with irradiance range in between 10 GW cm 2 to 100 GW cm -2. The characterization of laser produced plasma from solid targets viz. graphite and high temperature superconducting material like YBa2Cu3O7 have been carried out. The fundamental frequency from a Q - switched Nd: YAG laser with 9 ns pulse duration is used for the present studies. Various optical emission emission diagnostic techniques were employed for the the characterization of the LPP which include emission spectroscopy, time resolved studies, line broadening method etc. In order to understand the physical nature of the LPP like recombination, collisional excitation and the laser interaction with plasma, the time resolved studies offer the most logical approach

Characterization and Imaging Diagnostics of Laser Induced Plasma from Solid Targets

Laser induced plasma (LIP) emissions from some metal oxide targets were studied with corresponding metal targets of pure quality as a reference. Atomic emissions in the visible region were used in the spectroscopic procedures of LIP characterization. The studies were meant to throw light into LIP dynamics and they provided many experimental results which improved the general awareness of plasma state.When target materials were photo-ablated with an energetically suitable laser pulse, they developed electric charges in them.An electrical signal which was delivered from the target served as an alternative probe signal for the diagnostics of LIP and to track different charged states in the plasma. The signal showed a double peak distribution with positive polarity and a modified time of flight with various voltage levels of a given polarity.The expansion dynamics of LIP in magnetic field were also investigated by monitoring the voltage transients generated at the target.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.