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Ph. D. ThesisPh. D. Thesis 5. Results  Kinetic Measurements5. Results Kinetic Measurements 5.4. Conclusions5.4. Conclusions
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Ph. D. Thesis
  Table of Contents
  1. Introduction
  2. Theory Fundamentals of the Multivariate Data Analysis
  3. Theory Quantification of the Refrigerants R22 and R134a: Part I
  4. Experiments, Setups and Data Sets
  5. Results Kinetic Measurements
    5.1. Static Sensor Measurements
    5.2. Time-resolved Sensor Measurements
    5.3. Makrolon A Polymer for Time-resolved Measurements
    5.4. Conclusions
  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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5.4.   Conclusions

In this chapter, the principle of time-resolved measurements has been introduced as a new approach in chemical sensing. Thereby the time-resolved measurements performed in this work are based on Makrolon as sensitive polymer layer. The microporous structure of this polymer allows the discrimination of different analytes based on the size of the molecules. The combination of the polymer and a time-resolved recording of the sensor responses during sorption and desorption of the analytes allows a simultaneous quantification of a virtually unlimited number of analytes. The analytes to be quantified are only limited by too similar kinetics and by too slow kinetics whereby the kinetics can be modified by the type of carrier gas and by the thickness of the sensitive layer. The new time-resolved approach allows reducing the number of sensors to be used for an analytical problem. In this work, several analytical tasks are solved by the use of a single sensor setup rendering sensor arrays unnecessary, which would have been used for these analytical problems in the common approaches.

Additional research on other microporous polymers should allow the application of this approach to a broader spectrum of analytes. Yet, the time-resolved measurements are not limited to a size sensitive detection but can also be applied to other interaction principles. The time-resolved approach with a discrimination of the analytes based on exploiting the different shapes of the time-resolved sensor responses also dramatically changes the search for suitable sensitive layers for an analytical problem. Optimal sensitive layers of the common static sensor evaluation show most different sensitivity patterns for the analytes (like in section 3.3) whereas an optimal sensitive layer for the time-resolved sensor evaluation shows different shapes of the sensor responses for the different analytes.

In contrast to the sparse reports of time-resolved measurements in chemical sensing found in literature, the properties and interactions of the sensitive layer with the analytes is systematically investigated in this study allowing the tweaking of the kinetics described above. In the next chapters additional systematic investigations follow, which concern the exposure time, the recording speed of the of the sensor responses, the parallelization of the sensors and last but not least the multivariate data analysis rendering this work unique in respect to time-resolved measurements in chemical sensing.

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