Chemical tongues and noses based upon conjugated polymers
We report the uses of conjugated polymers in multisensory applications and in chemical and optoelectronic tongues. We look at the potential of single polymers to discriminate multiple analytes and into small libraries of conjugated polymers that represent sensors. These small libraries combine sever...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
19 June 2017
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| In: |
Topics in current chemistry
Year: 2017, Jahrgang: 375 |
| ISSN: | 2364-8961 |
| DOI: | 10.1007/s41061-017-0155-2 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/s41061-017-0155-2 Verlag, Volltext: https://link.springer.com/article/10.1007/s41061-017-0155-2 |
| Verfasserangaben: | Jan Freudenberg, Felix Hinkel, Daniel Jänsch, Uwe H. F. Bunz |
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| 520 | |a We report the uses of conjugated polymers in multisensory applications and in chemical and optoelectronic tongues. We look at the potential of single polymers to discriminate multiple analytes and into small libraries of conjugated polymers that represent sensors. These small libraries combine several barely selective, promiscuous sensor elements and react with the analytes in a fairly non-selective fashion by change of color, emission wavelength, or emission intensity. In such optoelectronic noses and tongues, response of a single element is not specific or particularly useful at all, but the response pattern after the combination of several sensor elements is often specific for an analyte and allows discrimination and identification without any problem. These types of tongues and noses are well suited for quality control of foodstuff, beverages, and biological species such as proteins or cells. The discriminative process is often not well understood but it is powerful, particularly if the obtained data are analyzed by sophisticated statistical methods, i.e., linear discriminant analysis and/or principal component analysis. This added layer of analysis extracts the hidden information/patterns out of the data and allows visualization of the results. | ||
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