Analytical chemistry

Analytical chemistry studies and uses instruments and methods to separate, identify, and quantify matter. In practice, separation, identification or quantification may constitute the entire analysis or be combined with another method. Separation isolates analytes. Qualitative analysis identifies analytes, while quantitative analysis determines the numerical amount or concentration. Analytical chemistry consists of classical, wet chemical methods and modern, instrumental methods. Classical qualitative methods use separations such as precipitation, extraction, and distillation. Identification may be based on differences in color, odor, melting point, boiling point, solubility, radioactivity or reactivity. Classical quantitative analysis uses mass or volume changes to quantify amount. Instrumental methods may be used to separate samples using chromatography, electrophoresis or field flow fractionation. Then qualitative and quantitative analysis can be performed, often with the same ins

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Chemistry

Chemistry is the scientific study of the properties and behavior of matter. It is a physical science under natural sciences that covers the elements that make up matter to the compounds made of atoms,

Spectroscopy

Spectroscopy is the field of study that measures and interprets the electromagnetic spectra that result from the interaction between electromagnetic radiation and matter as a function of the wavele

Gas chromatography

Gas chromatography (GC) is a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition. Typical uses of GC include t

Statistical Analysis of Chao’s Immune System Simulation

Eric Winnington

Modeling the immune system (IS) means putting together a set of assumptions about its components (cells and organs) and their interactions. Simulations of a model show joint behavior of the components, which for complex realistic models is often impossible to find analytically. Simulations allow us to experiment on how initial concentrations and properties of the immune cells and viruses impact the IS behavior, and gain better quantitative and qualitative insight into how the IS works and why different behavior patterns occur. A simulation, once it has been created, must be reviewed both statistically and analytically as well as validated from the biological point of view. We analyzed Chao’s immune system simulation [1][2] from a statistical and analytical point. We explicited both the Markov chain which was simulated and the underlying process on which Chao’s stage-structured approach was built. Furthermore, we established a test protocol for timestep validation which Chao’s simulator passed. We evaluated Chao’s simulator’s dependence on the random number generator, which was shown to be negligible. Finally, we evaluated the simulator output and our major result is the discovery of a secondary response to a primary infection, an occurrence is not shown in Chao’s dissertation. A tertiary response to the infection is never possible due to the size of the secondary response caused by memory cells.

2005

Evaluation des incertitudes dans la mesure des micropolluants

Sylvain Gorgerat

Occurrence and fate of micropollutants in surface water are of growing concern. To take adequate actions, it is essential to rely on representative data. Therefore, it is necessary to assess uncertainties related to the different steps of the analytical procedure. In this work, Pierre Gy's theory of sampling is applied to micropollutants concentrations in surface water for three case studies. These cases are: a large river (Rhône River, discharge of 182.5 m3/s), a large wastewater treatment plant (WWTP, discharge of 0.5 m3/s) and a small hospital sewage network (CHUV, discharge of 0.006 m3/s). Micropollutants studied are atrazine, benzotriazol, carbamazepine, diclofenac, mecoprop, mepivacaïne, paracetamol, pymetrozin and sulfamethoxazol. For dissolved micropollutants the global sampling uncertainty is about 45% for the Rhône River and about 35% for the WWTP and the CHUV. The difference is explained by lower concentration in the river leading to greater analytical uncertainties. Sampling frequency and sampling volume have little effects on these uncertainties. They are rather related to discharge measurement (weighting error), instruments and their use (materialization error) and analytical error. For sorbed micropollutants, uncertainties of 2'000%, 140% and 160 % were calculated for the Rhône River, the WWTP and the CHUV respectively. These uncertainties are related to sampling volumes not large enough. They are easily reduced by increasing these volumes. Discharge is expected to affect uncertainties mainly if it affects micropollutant concentrations. In some cases, measured concentrations are not representative of the true concentration and can lead to misleading conclusions. To improve data reliability, sampling system could be enhanced. Moreover, sampling dissolved and sorbed pollutants separately could be beneficial.

2011

Identification of Isomeric Biomolecules by Infrared Spectroscopy of Solvent-Tagged Ions

Andrei Zviagin

The difference in functionality of many isomeric biomolecules requires their analytical identification for life science studies. We present a universal approach for quantitative identification of different small- to medium-sized isomeric biomolecules that can be brought to the gas phase from solution by electrospray ionization (ESI). The method involves infrared (IR) fragment cold ion spectroscopy of analyte molecules that are incompletely desolvated by soft ESI. The use of solvent molecules as natural tags removes a need for adding to solutions any special compounds, which may interfere with liquid chromatography or mass spectrometric measurements. The tested peptides and especially monosaccharides and lipids exhibit highly isomerspecific IR fragment spectra of such noncovalent complexes, which were produced from water, methanol, acetonitrile, and 2butanol solutions. The relative concentrations in solution mixtures of, for instance, two isomeric dipeptides can be quantified with the accuracy of 1.6% and 2.9% for the acquisition time of 25 min and, potentially, 5 s, respectively; for three isomeric phosphooctapeptides, the accuracy becomes 4.1% and 11% for 17 min and, potentially, 10 s measurements, respectively.

AMER CHEMICAL SOC2022

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