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Ph. D. ThesisPh. D. Thesis 3. Theory  Quantification of the Refrigerants R22 and R134a: Part I3. Theory Quantification of the Refrigerants R22 and R134a: Part I 3.3. Sensitivities3.3. Sensitivities
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Ph. D. Thesis
  Abstract
  Table of Contents
  1. Introduction
  2. Theory Fundamentals of the Multivariate Data Analysis
  3. Theory Quantification of the Refrigerants R22 and R134a: Part I
    3.1. Experimental
    3.2. Single Analytes
    3.3. Sensitivities
    3.4. Calibrations of the Mixtures
    3.5. Variable Selection by Brute Force
    3.6. Conclusions
  4. Experiments, Setups and Data Sets
  5. Results Kinetic Measurements
  6. Results Multivariate Calibrations
  7. Results Genetic Algorithm Framework
  8. Results Growing Neural Network Framework
  9. Results All Data Sets
  10. Results Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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3.3.   Sensitivities

For this study, primarily the differences of the sensitivities of the polymers for the two analytes are of interest to select the two most suitable polymers for the low-cost 4l setup. The sensitivities can be calculated as the slopes of the calibration curves. For the Henry sorption, a constant sensitivity can be easily specified for the examined concentration range, whereas the sensitivity for the Langmuir sorption is a function of the concentrations. Thus, the sensitivities for the sorption of R22 in the polymers UE 2010, Makrolon, and HBP were calculated using the derivative of the calibration function at zero concentration.

In figure 8 the sensitivities for both analytes and all 6 sensors are shown. As already mentioned the microporous polymers show a very different swelling for the 2 analytes whereas the sorption process of both analytes into the polar polymers is very similar. Consequently, the sensitivities of M 2400 and UE 2010 for both analytes are very different whereas the sensitivities of PUT and PDMS for both analytes are similar. For a quantification of binary mixtures with only 2 sensors, 2 polymers should be chosen, which show the most possible different sensitivity patterns for the 2 analytes (with the extreme of 2 selective sensors). Consequently, for the quantification of the 2 analytes, 1 microporous polymer and 1 polar polymer should be chosen. The polymers UE 2010 and PDMS, which show the most different sensitivity patterns, would be the best choice. Due to the technical limitation of the thickness and consistence of the layers [178], the combination UE 2010 / PUT was chosen for the 4l setup.

figure 8: Sensitivities of the different polymers.

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