Indiana University-Purdue University Indianapolis (IUPUI) / Analyses of tobacco products and their usage are well-researched and have implications in analytical chemistry, forensic science, toxicology, and medicine. As such, analytical methods must be developed to extract compounds of interest from tobacco products and biological specimens in order to determine tobacco exposure. In 2009, R.J. Reynolds Tobacco Co. released a line of dissolvable tobacco products that are marketed as a smoking alternative. The dissolvables were extracted and prepared by ultrasonic extractions, derivatization, and headspace solid phase microextraction (SPME) with analysis by gas chromatography-mass spectrometry (GC-MS). The results show that the compounds present are nicotine, flavoring compounds, humectants and binders. Humectant concentrations vary among different tobacco types depending on the intended use. Humectants were quantified in various tobacco types by GC and “splitting” the column flow between a flame ionization detector (FID) and an MS using a microfluidic splitter in order to gain advantage from the MS’s selectivity. The results demonstrated excellent correlation between FID and MS and show that MS provides a higher level of selectivity and ensures peak purity. Chemometrics was also used to distinguish products by tobacco type. Hair is a common type of evidence in forensic investigations, and it is often subjected to mitochondrial DNA (mtDNA) analysis. Preliminary data was gathered on potential “lifestyle” markers for smoking status as well as any indications of subject age, gender, or race by investigating the organic “waste” produced during a mtDNA extraction procedure. The normally discarded organic fractions were analyzed by GC-MS and various lipids and fatty acids were detected. At this point, a total vaporization-SPME (TV-SPME) method was theorized, developed, and optimized for the specific determination of nicotine and its metabolite, cotinine. The theory of TV-SPME is to completely vaporize an organic extract which will eliminate the partitioning between the sample and the headspace, thereby simplifying the thermodynamic equilibrium. Parameters such as sample volume, incubation temperature, and extraction time were optimized to achieve the maximum analyte signal. Response surface methodology (RSM) is a statistical model that is very useful in predicting and determining optimum values for variables to ensure the ideal response. RSM was used to optimize the technique of TV-SPME for the analysis of nicotine and cotinine. Lastly, quantitation of nicotine and cotinine in human hair typically requires large sample sizes and extensive extraction procedures. Hence, a method using small sample sizes and a simple alkaline digestion followed by TV-SPME-GC-MS has been developed. Hair samples were collected from anonymous volunteers and nicotine and cotinine were identified and quantitated in the hair of tobacco users.

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AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.