Application of thermal ion-molecule reactions in tackling spectroscopic inferences in inductively coupled plasma mass spectrometry

Part I: Ultra-trace determination of vanadium in lake sediments: a performance comparison using O2, N20, and NH3 as reaction gases in ICP-DRC-MS Thermal ion-molecule reactions, targeting removal of specific spectroscopic interference problems, have become a powerful tool for method development in quadrupole based inductively coupled plasma mass spectrometry (ICP-MS) applications. A study was conducted to develop an accurate method for the determination of vanadium in lake sediment samples by ICP-MS, coupled with a dynamic reaction cell (DRC), using two differenvchemical resolution strategies: a) direct removal of interfering C10+ and b) vanadium oxidation to VO+. The performance of three reaction gases that are suitable for handling vanadium interference in the dynamic reaction cell was systematically studied and evaluated: ammonia for C10+ removal and oxygen and nitrous oxide for oxidation. Although it was able to produce comparable results for vanadium to those using oxygen and nitrous oxide, NH3 did not completely eliminate a matrix effect, caused by the presence of chloride, and required large scale dilutions (and a concomitant increase in variance) when the sample and/or the digestion medium contained large amounts of chloride. Among the three candidate reaction gases at their optimized Eonditions, creation of VO+ with oxygen gas delivered the best analyte sensitivity and the lowest detection limit (2.7 ng L-1). Vanadium results obtained from fourteen lake sediment samples and a certified reference material (CRM031-040-1), using two different analytelinterference separation strategies, suggested that the vanadium mono-oxidation offers advantageous performance over the conventional method using NH3 for ultra-trace vanadium determination by ICP-DRC-MS and can be readily employed in relevant environmental chemistry applications that deal with ultra-trace contaminants.Part II: Validation of a modified oxidation approach for the quantification of total arsenic and selenium in complex environmental matrices Spectroscopic interference problems of arsenic and selenium in ICP-MS practices were investigated in detail. Preliminary literature review suggested that oxygen could serve as an effective candidate reaction gas for analysis of the two elements in dynamic reaction cell coupled ICP-MS. An accurate method was developed for the determination of As and Se in complex environmental samples, based on a series of modifications on an oxidation approach for As and Se previously reported. Rhodium was used as internal standard in this study to help minimize non-spectral interferences such as instrumental drift. Using an oxygen gas flow slightly higher than 0.5 mL min-I, arsenic is converted to 75 AS160+ ion in an efficient manner whereas a potentially interfering ion, 91Zr+, is completely removed. Instead of using the most abundant Se isotope, 80Se, selenium was determined by a second most abundant isotope, 78Se, in the form of 78Se160. Upon careful selection of oxygen gas flow rate and optimization ofRPq value, previous isobaric threats caused by Zr and Mo were reduced to background levels whereas another potential atomic isobar, 96Ru+, became completely harmless to the new selenium analyte. The new method underwent a strict validation procedure where the recovery of a suitable certified reference material was examined and the obtained sample data were compared with those produced by a credible external laboratory who analyzed the same set of samples using a standardized HG-ICP-AES method. The validation results were satisfactory. The resultant limits of detection for arsenic and selenium were 5 ng L-1 and 60 ng L-1, respectively.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.