The present thesis work represents a novel and reliable strategy to develop highly sensitive, highly selective, and low-cost graphene-based gas sensors towards inorganic gases detection (NH3, PH3) and volatile organic compounds (VOCs) sensing at room temperature. The developed strategy may allow for gas detection, odor recognition of a wide spectrum of odor molecules, as well as detection of volatile organic compounds (VOC) in an extensive variety of domains, e.g., environmental monitoring, public security, smart farming, or disease diagnosis (e.g., lung cancer, COVID-19).

Dihydro-1,2,4,5-tetrazine-3,6-dicarboxylate was introduced into the chemically stable UiO-66 structure by a postsynthetic linker exchange reaction to create an optical sensor material for the detection of oxidative agents such as nitrous gases. The incorporated tetrazine unit can be reversibly oxidized and reduced, which is accompanied by a drastic colour change from yellow to pink and vice versa. The high stability of the framework during redox reaction was proven by powder X-ray diffraction and nitrogen physisorption measurements.

1. Zielstellung der Arbeit war die Entwicklung von Musterkennungsmethoden zur automatischen Klassifizierung von Gasen. Um dieses Ziel zu erreichen, wurde die Reduktionsmethode Parameterabschätzung mittels Adaptive-Simulated-Annealing (ASA-PE) und eine Committee machine (CM) zur Klassifikation entwickelt. 2. Mittels PEDOT:PSS-Sensoren wurden mit Hilfe der Impedanzspektroskopie NH3 und NO2 in unterschiedlichen Konzentrationen gemessen. Die aufgenommenen Messdaten wurden durch die ASA-PE, die Komplexe Haupt-komponentenanalyse (CPCA) und die Discriminant analyses via Support Vector (SVDA) reduziert. 3. Der Vergleich der Merkmalsextraktionsmethoden zeigt: Die in dieser Arbeit neu entwickelte Methode ASA-PE liefert im Vergleich dazu ein sicheres Segmentierungs-Ergebnis. 4. Der Vergleich zwischen ASA-PE und ZView zeigt, dass die ASA-PE eine sichere Methode für die automatisierte Gasanalyse ist. Aber bei zweidimensionalen Merkmalen gibt es einen Bereich, in dem sich eine gemeinsame Häufung einstellt, welche zu einer Irritation in der Auswertung von CPCA und SVDA führen kann. Dieses Problem kann durch eine Erhöhung der Anzahl von Merkmalen gelöst werden. 5. Es wurden sechs die Klassifikationsmethoden: Abstandsgewichtete k-Nächste-Nachbarn-Klassifikation (DW-kNN), das mehrlagige Perzeptron (MLP), Support Vector Machine (SVM), CM, CM ohne MLP und CM mit Abstandskontrolle und AAi-Filter untersucht und miteinander verglichen. Um die Klassifikationsmethoden anzulernen wurden alle Merkmalsreduktions-ergebnisse der CPCA, SVDA und der ASA-PE in Trainings- und Testdaten eingeteilt. 6. Die Ergebnisse zeigen, dass die Kombination aus One-Against-All-SVM (OAA-SVM) und ASA-PE die besten Erkennungsraten liefert. Bei 200 Trainingsdatensätzen wird eine Erkennungsrate von bis zu 99.5% erzielt. Durch diese Kombination können jedoch nur 8 Typen ohne Identifikation von unbekannten Typen ermittelt werden. 7. Wenn das MLP aus CM entfernt wird, werden die Resultate von CM leicht verbessert. Mit Hilfe von 6-Sigma zeigt CM ohne MLP eine gute Erkennungsrate für unbekannte Gase und gleichzeitig bleibt die Erkennungsrate auf einem befriedigenden Niveau. 8. Die Streuung der ASA-PE führt zu einer schlechten Abgrenzung zwischen bekannten und unbekannten Gasen. Stattdessen zeigt die Kombination von CM ohne MLP und CPCA in diesem Fall eine gute Abgrenzung.:Abstract II Danksagung III Inhaltsverzeichnis IV Abkürzungen VII 1 Einführung 1.1 Einleitung 1.2 Entwicklungen bei Gassensoren 1.2.1 Fortschritte bei Material und Messmethode 1.2.2 Fortschritte bei Mustererkennungsmethoden 1.3 Motivation 1.4 Struktur der Arbeit 2 Verfahren zur Gasanalyse 2.1 Messverfahren 2.1.1 Impedanzspektroskopie als Detektionsmethode 2.1.1.1 Definition der Impedanz 2.1.1.2 Bauelemente des elektrischen Modells 2.1.2 Optische Verfahren 2.1.3 Elektrochemische Verfahren 2.2 Merkmalerkennung 2.2.1 Merkmalsreduktion 2.2.1.1 Komplexe Hauptkomponentenanalyse (Engl. Complex Principal Component Analysis) 2.2.1.2 Kernel-Diskriminanzanalyse mittels Support Vektoren (engl. kernel Discriminant Analysis via Support Vector) 2.2.2 Klassifikationsverfahren 2.2.2.1 Abstands-gewichtete k-Nächste-Nachbarn-Klassifikation (engl. Distance weighted k-Nearest-Neighbor-Algorithms, DW-kNN) 2.2.2.2 Mehrlagiges Perzeptron (MLP) 2.2.2.3 Support Vektor Maschine (SVM) 3 Eigene Mustererkennungsverfahren 3.1 Parameterschätzung mittels Adaptive-Simulated-Annealing (ASA-PE) 3.1.1 Allgemeines Impedanzspektroskopiemodell eines Gassensors 3.1.2 Parameterschätzung 3.1.3 Die Optimierungsverfahren 3.2 Committee machine 4 Anwendungsbeispiel 4.1 Experiment mit einem Gassensor aus PEDOT:PSS 4.1.1 Sensoraufbau und vereinfachtes Sensormodell 4.2 Experimentelle Ergebnisse 4.2.1 Messaufbau und Versuchsdurchführung 4.2.2 Vorbereitung zur Messung 4.2.3 Durchführung der Messung 4.2.4 Fehlerbetrachtung 4.2.5 Messergebnisse des Gassensors 4.3 Ergebnisse der Merkmalreduktion 4.3.1 CPCA und SVDA 4.3.2 Parameterschätzung mittels Adaptive-Simulated-Annealing (ASA-PE) 4.4 Ergebnisse der Klassifikationen 4.4.1 Ergebnisse der Gasbestimmung mittels Trainingssatz und Testsatz 4.4.1.1 DW-kNN 4.4.1.2 MLP 4.4.1.3 OAO-SVM 4.4.1.4 OAA-SVM 4.4.1.5 Committee machine 4.4.1.6 CM ohne MLP 4.4.1.7 CM mit AAi-Filter 4.4.2 Abhängigkeit der Klassifikationsergebnisse von der Anzahl der Trainingsdaten 5 Zusammenfassung und Ausblick 5.1 Zusammenfassung 5.2 Ausblick Abbildungsverzeichnis Formelverzeichnis Literaturverzeichnis

In this work the influence of solid electrolyte additives on the gas sensing properties of tin oxide layers was investigated systematically for the first time. NASICON (NAtrium, Super Ionic CONductor, Na(1+x)Zr2SixP(3-x)O12; 0 <= x <= 3) was used as a model for solid electrolyte additives. The structure of that material is ideally suitable for studies of the correlation between material parameters and the gas sensitivity of the layers. In the NASICON structure the content of mobile Na+-ions can be varied by a factor of four resulting in a simultaneous change of the ionic conductivity sigma(Na+) by approximately three orders of magnitude without considerable structural alterations. Powders of SnO2 and NASICON (x = 0; 2.2; 3) were prepared separately by means of sol-gel routes and mixed in a volume ratio of 80/20. Pastes were prepared from these powders with different compositions and screen printed on alumina substrates with a fourfold structure of thin film gold electrode combs. Four different compositions were characterised simultaneously at elevated temperatures in various gas atmospheres. The conductivity of the layers, when measured in air, decreases considerably with increasing Na+-content in the NASICON additive. This is correlated with enhanced activation energy of the electronic conductivity. The sensitivity of the layers to polar organic molecules like R-OH (alcohols), R-HO (aldehydes) and ROOH (carboxylic acids) is highly enhanced by the NASICON additive. This is observed especially on the admixtures with NASICON of high Na+-content (x = 2.2 and x = 3). On the other hand, the sensitivity to substances with mid-standing functional groups like 2-propanol or propanone can not be enhanced by NASICON additives. Furthermore the sensitivity of these composite layers to CO, H2, NH3, methane, propane, propene and toluene (all exposed as admixtures with air) is lower than the sensitivity of pure SnO2-layers. These observations are well correlated with the results of gas consumption measurements on SnO2/NASICON powders by means of FTIR spectroscopy. In spite of the lack of surface analytical data, a model of surface chemical gas reactions based on a triple phase boundary (SnO2/NASICON/gas atmosphere) was developed, which explains the experimental observations qualitatively. It is assumed that the decrease of the electronic conductivity as observed in the presence of NASICON additives with increasing Na+-content is due to an enhanced electron depletion layer. This is formed in the SnO2 grains by Na+/e- interactions across the SnO2/NASICON-interface. The enormous enhancement of the sensitivity to polar organic molecules may be due to specific nucleophilic interactions with the Na+-ions and coupled Na+/e--interactions at the triple phase reaction sites.

In this work the influence of solid electrolyte additives on the gas sensing properties of tin oxide layers was investigated systematically for the first time. NASICON (NAtrium, Super Ionic CONductor, Na(1+x)Zr2SixP(3-x)O12; 0 <= x <= 3) was used as a model for solid electrolyte additives. The structure of that material is ideally suitable for studies of the correlation between material parameters and the gas sensitivity of the layers. In the NASICON structure the content of mobile Na+-ions can be varied by a factor of four resulting in a simultaneous change of the ionic conductivity sigma(Na+) by approximately three orders of magnitude without considerable structural alterations. Powders of SnO2 and NASICON (x = 0; 2.2; 3) were prepared separately by means of sol-gel routes and mixed in a volume ratio of 80/20. Pastes were prepared from these powders with different compositions and screen printed on alumina substrates with a fourfold structure of thin film gold electrode combs. Four different compositions were characterised simultaneously at elevated temperatures in various gas atmospheres. The conductivity of the layers, when measured in air, decreases considerably with increasing Na+-content in the NASICON additive. This is correlated with enhanced activation energy of the electronic conductivity. The sensitivity of the layers to polar organic molecules like R-OH (alcohols), R-HO (aldehydes) and ROOH (carboxylic acids) is highly enhanced by the NASICON additive. This is observed especially on the admixtures with NASICON of high Na+-content (x = 2.2 and x = 3). On the other hand, the sensitivity to substances with mid-standing functional groups like 2-propanol or propanone can not be enhanced by NASICON additives. Furthermore the sensitivity of these composite layers to CO, H2, NH3, methane, propane, propene and toluene (all exposed as admixtures with air) is lower than the sensitivity of pure SnO2-layers. These observations are well correlated with the results of gas consumption measurements on SnO2/NASICON powders by means of FTIR spectroscopy. In spite of the lack of surface analytical data, a model of surface chemical gas reactions based on a triple phase boundary (SnO2/NASICON/gas atmosphere) was developed, which explains the experimental observations qualitatively. It is assumed that the decrease of the electronic conductivity as observed in the presence of NASICON additives with increasing Na+-content is due to an enhanced electron depletion layer. This is formed in the SnO2 grains by Na+/e- interactions across the SnO2/NASICON-interface. The enormous enhancement of the sensitivity to polar organic molecules may be due to specific nucleophilic interactions with the Na+-ions and coupled Na+/e--interactions at the triple phase reaction sites.