Plasma Spectroscopic Techniques Applied to Biological and Environmental Matrices

The purpose of this research was to apply the use of direct ablation plasma spectroscopic techniques, including spark-induced breakdown spectroscopy (SIBS) and laser-induced breakdown spectroscopy (LIBS), to a variety of environmental matrices. These were applied to two different analytical problems. SIBS instrumentation was adapted in order to develop a fieldable monitor for the measurement of carbon in soil. SIBS spectra in the 200 nm to 400 nm region of several soils were collected, and the neutral carbon line (247.85 nm) was compared to total carbon concentration determined by standard dry combustion analysis. Additionally, Fe and Si were evaluated in a multivariate model in order to determine their impacts on the model's predictive power for total carbon concentrations. The results indicate that SIBS is a viable method to quantify total carbon levels in soils; obtaining a good correlation between measured and predicated carbon in soils. These results indicate that multivariate analysis can be used to construct a calibration model for SIBS soil spectra, and SIBS is a promising method for the determination of total soil carbon. SIBS was also applied to the study of biological warfare agent simulants. Elemental compositions (determined independently) of bioaerosol samples were compared to the SIBS atomic (Ca, Al, Fe and Si) and molecular (CN, N2 and OH) emission signals. Results indicate a linear relationship between the temporally integrated emission strength and the concentration of the associated element. Finally, LIBS signals of hematite were analyzed under low pressures of pure CO2 and compared with signals acquired with a mixture of CO2, N2 and Ar, which is representative of the Martian atmosphere. This research was in response to the potential use of LIBS instrumentation on the Martian surface and to the challenges associated with these measurements. Changes in Ca, Fe and Al lineshapes observed in the LIBS spectra at different gas compositions and pressures were studied. It was observed that the size of the plasma formed on the hematite changed in a non-linear way as a function of decreasing pressure in a CO2 atmosphere and a simulated Martian atmosphere.

Validation of the Item-Attribute Matrix in TIMSS-Mathematics Using Multiple Regression and the LSDM

The purposes of this study were (1) to validate of the item-attribute matrix using two levels of attributes (Level 1 attributes and Level 2 sub-attributes), and (2) through retrofitting the diagnostic models to the mathematics test of the Trends in International Mathematics and Science Study (TIMSS), to evaluate the construct validity of TIMSS mathematics assessment by comparing the results of two assessment booklets. Item data were extracted from Booklets 2 and 3 for the 8th grade in TIMSS 2007, which included a total of 49 mathematics items and every student's response to every item. The study developed three categories of attributes at two levels: content, cognitive process (TIMSS or new), and comprehensive cognitive process (or IT) based on the TIMSS assessment framework, cognitive procedures, and item type. At level one, there were 4 content attributes (number, algebra, geometry, and data and chance), 3 TIMSS process attributes (knowing, applying, and reasoning), and 4 new process attributes (identifying, computing, judging, and reasoning). At level two, the level 1 attributes were further divided into 32 sub-attributes. There was only one level of IT attributes (multiple steps/responses, complexity, and constructed-response). Twelve Q-matrices (4 originally specified, 4 random, and 4 revised) were investigated with eleven Q-matrix models (QM1 ~ QM11) using multiple regression and the least squares distance method (LSDM). Comprehensive analyses indicated that the proposed Q-matrices explained most of the variance in item difficulty (i.e., 64% to 81%). The cognitive process attributes contributed to the item difficulties more than the content attributes, and the IT attributes contributed much more than both the content and process attributes. The new retrofitted process attributes explained the items better than the TIMSS process attributes. Results generated from the level 1 attributes and the level 2 attributes were consistent. Most attributes could be used to recover students' performance, but some attributes' probabilities showed unreasonable patterns. The analysis approaches could not demonstrate if the same construct validity was supported across booklets. The proposed attributes and Q-matrices explained the items of Booklet 2 better than the items of Booklet 3. The specified Q-matrices explained the items better than the random Q-matrices.