Analytical Methods for Industrial- and Environmental Monitoring

This is an advanced course dealing with practical and theoretical aspects of chemical methods, sensor technology and multivariable analysis. Electrochemical, optical, and other types of sensors with important applications in industry and environmental monitoring are especially emphasized. Examples of trace level methods, and methods to investigate different chemical forms (speciation) will be reviewed. Reaction mechanisms and surface chemistry connected to sensor technology will be discussed, as well as constructions of sensors including nano-based sensor technology. Within chemometrics, focus will be analysis based on multicomponent methods, including iterative target transformation factor analysis (ITTFA), key set factor analysis, evolving factor analysis, fixed size moving evolving factor analysis, self-modeling multivariate analysis (SIMPLISMA) and heuristic evolving latent projection (HELP). Further, methods for base line correction, impact and correction of noise, and extracting information from overlapping peaks will be covered.

After examination the student should have / be able to:
- Be able to explain the basic ideas behind the most central methods in chemometric multicomponent analysis
- Be able to discuss the strengths and weaknesses of the different methods.
- Be able to analyze data from "hyphenated methods" (such as LC-IR) using the presented chemometric multicomponent methods.
- knowledge to and be able to explain current and new analytical methods with relevance to industrial and environmental monitoring with a focus on electroanalytical sensors and biosensors/immunosensorer.
- give a detailed explanation of the theoretical principles related to various sensor systems, including mechanisms for the sensor system operation, important factors related to surface chemistry that might affect the sensor response (e.g. drift), as well as detailed knowledge about the structure and design of various types of sensors and signal conditioning.
- knowledge about the practical aspects and consequences of interferences and other factors that might affect the sensor response / measuring results, sensitivity, maintenance and quality assurance.

Lectures (20 hours), data lab (60 hours). The course is given concentrated in two weeks, two hours with lectures every day. Mandatory data lab can be carried out during and/or between the teaching periods. Between the two teaching periods, each with duration of one week, it is self study with supervision (40 hours). Examination: Oral examination at the end of the semester. English teaching by request.