Les protéines fluorescentes, comme la GFP (green fluorescent protein), sont des protéines naturellement fluorescentes qui sont utilisées pour leur rôle de marqueur, permettant de localiser des protéines dans les cellules et d'en suivre les déplacements. De nombreuses études expérimentales et théoriques ont été menées ces dix dernières années sur les protéines fluorescentes. De là, se forge une compréhension essentiellement qualitative du rôle de la protéine vis-à-vis de l’obtention ou non d’une émission radiative : il apparaît que la protéine permet la fluorescence en bloquant les processus qui la désactivent ; ces processus de désactivation sont très rapides et efficaces (à l'échelle de la picoseconde) dans le cas du chromophore seul, et ils sont bien identifiés comme étant des torsions autour des liaisons intercycles (tau et phi). Dans la protéine, la sensibilité des temps de vie de fluorescence à des mutations proches ou non du chromophore, à des modifications de pH ou de température laisse supposer un contrôle de la dynamique du chromophore par différents paramètres, sans qu’ils soient pour autant identifiés et mis en relation.Une étude de la dynamique de la protéine permettrait de faire la lumière sur les mécanismes responsables de ces phénomènes photophysiques pour lesquels une analyse structurale ne suffit pas. Cependant l'étude de la dynamique est limitée par la taille du système (>30 000 atomes), par l'échelle de temps des phénomènes photophysiques considérés (dizaine de nanosecondes) et par le fait que les deux torsions tau et phi sont fortement couplées dans l'état excité du chromophore. Ces trois facteurs excluent les méthodes de dynamique existantes aujourd'hui ; dynamique quantique (AIMD), dynamique mixte classique-quantique (QM/MD) et dynamique moléculaire classique (MD).Nous avons surmonté le problème par la modélisation de la surface d’énergie potentielle de torsion du chromophore à l’état excité basée sur des calculs quantiques de haute précision, par une interpolation des valeurs obtenues par une expression analytique appropriée en fonction des angles de torsion tau et phi et avec une précision suffisante pour reproduire des barrières de l’ordre de la kcal/mol, et enfin, par l’implémentation de cette expression analytique dans le programme parallèle AMBER. Une deuxième difficulté théorique concerne la simulation et l’analyse statistique d’événements peu fréquents à l’échelle de la nanoseconde, et dont on ne connait pas le chemin de réaction, ici les déformations de la protéine et du chromophore conduisant aux géométries favorables à la conversion interne. Grâce à ces développements et aux simulations qu'ils ont permises, nous avons réalisé la première modélisation de la désactivation non-radiative par conversion interne à l’échelle de la nanoseconde dans trois protéines fluorescentes différentes. L’analyse des dynamiques moléculaires classiques nous donne une évaluation quantitative des temps de vie de l’extinction de fluorescence, en accord avec les données expérimentales. Par ailleurs elle nous a permis d'identifier les mouvements moléculaires concertés de la protéine et du chromophore conduisant à cette extinction. De ces résultats, émerge une représentation plus complète du mécanisme qui libère la torsion du chromophore ou qui la déclenche : il peut venir d’un mouvement spécifique de la protéine, qui se produit à l’échelle de la nanoseconde, ou bien de plusieurs mouvements spécifiques, plus fréquents (rupture de liaisons hydrogène, rotation de chaînes latérales, dynamique d'agrégats d’eau), mais qui coïncident seulement à l’échelle de la nanoseconde. Ces mouvements spécifiques n’ont pas un coût énergétique important mais la nécessité de leur coïncidence crée un délai de l’ordre de quelques nanosecondes alors que dans le vide la torsion se produit en quelques picosecondes. Dans le cas des protéines étudiées, on a identifié en grande partie les mécanismes et les acides aminés qui sont impliqués. / Fluorescent proteins, like GFP (green fluorescent protein), are efficient sensors for a variety of physical-chemical properties and they are extensively used as markers in living cells imaging. These proteins have been widely studied both experimentally and theoretically the last decade. The comprehension of the protein's role in the regulation of the radiative emission is today essentially qualitative: it appears that the protein enables the fluorescence by blocking the processes that deactivates it; the deactivating processes are very quick and efficient (on the picosecond time scale) when the chromophore is isolated, and they are identified as being the torsions around the central bonds of the chromophore (tau and phi). The fluorescence lifetimes of a protein is very sensitive to mutations in the vicinity of the chromophore, to modifications in pH or in temperature. This seems to indicate a control of the dynamics of the chromophore by different parameters, that are not necessarily identified.A study of the dynamics of the protein would allow a deeper understanding of the mechanisms that are responsible for the fluorescence quenching. From a theoretical point of view, one is faced with three difficulties in this type of study: the size of the system (>30 000 atoms including a water box), the required time scale (tens of nanoseconds) and the fact that the torsions tau and phi are strongly coupled in the excited state of the chromophore. We must thus rule out the already existing dynamics methods: quantum dynamics (AIMD), mixed classical-quantum dynamics (QM/MD) and classical molecular dynamics (MD).We have overcome this problem by modeling the torsional potential energy surface of the chromophore in the first excited state trough high precision quantum calculations, by interpolating the energy values with an analytical fitting expression depending on the torsions tau and phi and with a precision high enough to reproduce barriers of the order of 1 kcal/mol, and lastly, by implementing this fitting expression in a parallelized version of the MD program AMBER. Another theoretical difficulty concerns the simulation and the statistical analysis of rare events on the nanosecond time scale without knowing the reaction path in advance, i.e. the deformations of the protein and of the chromophore leading to geometries where the internal conversion is favored. As a result of these developments and of the simulations they have enabled, we have been able to model, for the first time, the non-radiative deactivation by internal conversion at the nanosecond time scale in three different fluorescent proteins. The analysis of the classical molecular dynamics gives us a quantitative evaluation of the lifetime of the fluorescence extinction, in agreement with experimental results. In addition, it has allowed us to identify the concerted molecular movements between the protein and the chromophore leading to this extinction. A more complete representation of the mechanism that liberates or provokes the chromophore torsion emerges from these results: it could be a specific movement of the protein, that occurs on the nanosecond timescale, or several specific movements that occur more frequently (breakage of a hydrogen bond, rotation of side chains, dynamics of a water cluster), but that coincide only on the nanosecond time scale. These specific movements do not have a high energy cost but the need for them to coincide creates a delay of several nanoseconds compared to the chromophore torsion in vacuo which occurs after a few picoseconds. In the proteins we have studied (GFP, YFP and Padron), we have identified the principle components of the mechanisms and the amino acids that are implicated in this chromophore-protein interplay.

La métrologie laser basée sur l'analyse de la fluorescence de traceurs moléculaires est devenue l'un des outils clefs pour l'étude expérimentale de la dynamique des fluides réactifs. Une étude spectroscopique des propriétés photophysiques de fluorescence dans le domaine spectral UV-visible de plusieurs molécules fluorescentes appartenant aux cétones aliphatiques et aux aromatiques mono- et bicycliques a permis d'approfondir la compréhension de l'influence de la température, de la pression et de la concentration d'oxygène sur leur fluorescence. Les résultats expérimentaux obtenus ont ensuite permis le développement d'un modèle de simulation du rendement de fluorescence pour les espèces aromatiques (naphtalène et toluène), qui fournit des résultats très proches de ceux mesurés.De ces résultats, le développement de la technique d'imagerie de fluorescence (PLIF) sur la phase vapeur d'un carburant multi-composant a conduit à étendre cette analyse spectrale de fluorescence au cas du kérosène (Jet A-1). La comparaison entre les propriétés de fluorescence du kérosène et des traceurs aromatiques étudiés a notamment permis d'établir une stratégie de mesure de la concentration de la phase vapeur du kérosène dans des environnements où la teneur en oxygène est variable. Les signaux de fluorescence provenant des espèces mono- et di-aromatiques contenues dans le kérosène soulignent des évolutions différentes avec les conditions de température et teneur en oxygène. L'utilisation de filtres optiques appropriés associés à deux caméras ICCD permet alors une mesure bidimensionnelle de la température et de la concentration de kérosène en phase vapeur. La thèse débouche finalement sur l'application de cette technique PLIF-kérosène en combinaison avec la technique PLIF du radical OH en sortie d'un système d'injection industriel multi-point de nouvelle génération intégré dans une chambre de combustion haute pression.

Hochtemperatur-Konversionsprozesse sind ein wesentlicher Bestandteil von industriellen Produktionsprozessen, die maßgeblich den Prozesswirkungsgrad und die Produktionskosten beeinflussen. Die modellgestützte Optimierung ermöglicht eine gezielte Verbesserung verschiedener Parameter unter Berücksichtigung von prozesstechnischen, ökonomischen und ökologischen Aspekten. Bisher existiert in der Literatur kein Vergleich der Einsatzmöglichkeiten verschiedener Modellierungsmethoden zur modellgestützten, multikriteriellen Optimierung von Hochtemperatur-Konversionsprozessen. Daher werden in dieser Arbeit drei exemplarische Konversionsprozesse mit unterschiedlichen Modellierungsmethoden optimiert und anhand der Ergebnisse die Potenziale und Einsatzgrenzen für die modellgestützte Optimierung bewertet. Die Modellierung eines Wirbelschichtvergasers zeigt, dass detaillierte CFD-Modelle für komplexe mehrphasige Prozesse zu rechenaufwändig sind. Hingegen ist für einfache einphasige Prozesse wie ein Quench-Reaktor die Optimierung mit reduzierten CFD-Modellen realisierbar. Die Integration von Ersatzmodellen beschleunigt das Optimierungskonzept bei gleicher Ergebnisqualität, was die Optimierung von komplexen Prozessen für einfache Optimierungsprobleme ermöglicht. Die Optimierung der Partialoxidation von flüssigen Einsatzstoffen zur Methanolproduktion zeigt, dass sich Fließbildmodelle gut zur Optimierung von vollständigen Produktionsprozessen und komplexen Optimierungsproblemen eignen. Die Ergebnisse dieser Arbeit können als Basis für die Erstellung von Modellierungs- und Optimierungskonzepten für weitere Hochtemperatur-Konversionsprozesse genutzt werden.

Trace component analysis in gasification processes are important part of elemental component balances in order to understand the fate of these participating compounds in the feedstock. Residual traces in the raw synthesis gas after quench could bring about the poisoning of catalysts and corrosion effects on plant facilities. The objective of this work is to investigate the effects of quenching operation on the trace components during test campaigns of the autothermal non-catalytic reforming of natural gas (Gas-POX) mode in the HP POX (high pressure partial oxidation) test plant. In order to achieve this, Aspen Plus simulation model of the quench chamber of the HP POX test plant was developed to re-calculate the quench chamber input amount of different trace compounds from their output amount measured during test points of the Gas-POX campaigns. Variation in quench water temperatures from 130 °C to 220 °C and pH value of quench water as well as the resulting variation in Henry´s and Dissociation constant of the traces (CO2, H2S, NH3 and HCN) changed the distribution of traces calculated in the quench water. The formation of traces of organic acid (formic acid and acetic acid) and traces of BTEX, PAHs and soot in the quench water effluent were discussed. The discrepancies between equilibrium constant and reaction quotient (non-equilibrium or real) for the formation of NH3 and HCN at the exit of the gasifier were discussed. The assessment of the results in this work should lead to the improvement in the understanding of trace components and concepts that could be employed to influence their formation and reduction.

Trace component analysis in gasification processes are important part of elemental component balances in order to understand the fate of these participating compounds in the feedstock. Residual traces in the raw synthesis gas after quench could bring about the poisoning of catalysts and corrosion effects on plant facilities. The objective of this work is to investigate the effects of quenching operation on the trace components during test campaigns of the autothermal non-catalytic reforming of natural gas (Gas-POX) mode in the HP POX (high pressure partial oxidation) test plant. In order to achieve this, Aspen Plus simulation model of the quench chamber of the HP POX test plant was developed to re-calculate the quench chamber input amount of different trace compounds from their output amount measured during test points of the Gas-POX campaigns. Variation in quench water temperatures from 130 °C to 220 °C and pH value of quench water as well as the resulting variation in Henry´s and Dissociation constant of the traces (CO2, H2S, NH3 and HCN) changed the distribution of traces calculated in the quench water. The formation of traces of organic acid (formic acid and acetic acid) and traces of BTEX, PAHs and soot in the quench water effluent were discussed. The discrepancies between equilibrium constant and reaction quotient (non-equilibrium or real) for the formation of NH3 and HCN at the exit of the gasifier were discussed. The assessment of the results in this work should lead to the improvement in the understanding of trace components and concepts that could be employed to influence their formation and reduction.:List of Figures vii List of Tables xii List of Abbreviations and Symbols xiii 1 Introduction 1 1.1 Background 1 1.2 Objective of the Work 4 1.3 Overview of the Work 5 2 Process and test conditions 6 2.1 HP POX test plant 6 2.2 Test campaign procedure 8 2.2.1 Gas-POX operating parameter range 8 2.2.2 Gas-POX experiments 9 2.2.3 Net reactions of partial oxidation 9 2.3 Gaseous feedstock characterization 11 2.3.1 Natural gas feedstock composition 11 2.4 Analytical methods for gaseous products 12 2.4.1 Hot gas sampling 12 2.4.2 Raw synthesis gas analysis after quench 13 2.5 Aqueous phase product analysis 14 2.5.1 Molecularly dissolved trace compounds and their ions trace analysis 14 2.5.2 Other trace analysis 15 2.6 Limit of accuracy in measurement systems 15 2.7 Summary 17 3 Simulation and methods 18 3.1 Test points calculation of the HP POX test campaign 18 3.1.1 Aspen Plus model for HP POX quench water system 19 3.2 Gas-POX 201 VP1 quench water system model simulation by Aspen Plus 23 3.2.1 Measured and calculated input parameters 23 3.2.2 Calculated sensitivity studies of species and their distribution for test point (VP1) 24 3.3 Used calculation tools related to the work 25 3.3.1 VBA in Excel 25 3.3.2 Python as interface between Aspen Plus and Microsoft Excel 26 3.3.3 Aspen Simulation Workbook 27 3.4 Summary 29 4 Trace components in quench water system 30 4.1 Physico-chemical parameters of quench water 31 4.1.1 Quench water pH adjustment 32 4.1.2 Henry constant 34 4.1.3 Dissociation constant 35 4.1.4 Organic acids in quench water 38 4.2 Carbon dioxide (CO2) 39 4.2.1 Results of sensitivity study: quench water temperature variation effects on CO2 41 4.2.2 Results of sensitivity study: quench water pH variation influence on CO2 42 4.3 Nitrogen compounds 43 4.3.1 Ammonia (NH3) 44 4.3.2 Results of sensitivity study: quench water temperature variation effects on NH3 46 4.3.3 Results of sensitivity study: quench water pH variation influence on NH3 47 4.3.4 Hydrogen Cyanide (HCN) 48 4.3.5 Results of sensitivity study: quench water temperature variation effects on HCN 50 4.3.6 Results of sensitivity study: quench water pH variation influence on HCN 50 4.4 Sulphur compounds: H2S 51 4.4.1 Results of sensitivity study: quench water temperature variation effects on H2S 53 4.4.2 Results of sensitivity study: quench water pH variation influence on H2S 54 4.5 Summary 55 5 Organic acids trace studies in quench water 57 5.1 Organic acids interaction with ammonia compounds in the quench water 57 5.2 Formic acid 62 5.2.1 Trace of formic acid in quench water 64 5.3 Acetic acid 67 5.3.1 Trace of acetic acid in quench water 69 5.4 Summary 72 6 Temperature approach studies for NH3 and HCN formation in gasifier 74 6.1 Nitrogen compounds: NH3 and HCN 74 6.2 Ammonia (NH3) formation in the gasifer 77 6.3 Hydrogen cyanide (HCN) formation in the gasifier 79 6.4 Discrepancies between back-calculated reaction quotients and equilibrium constants of the NH3 formation 81 6.4.1 Case 1: calculated equilibrium distribution between N2, NH3 and HCN 81 6.4.2 Case 2: calculated equilibrium distribution between NH3 and HCN 83 6.5 Summary 84 7 Traces of BTEX, PAHs and soot in quench water 86 7.1 Quench water behaviour 87 7.2 BTEX compounds 88 7.2.1 BTEX in quench water effluent 90 7.3 PAH compounds 93 7.3.1 PAHs in quench water effluent 95 7.4 Soot formation 99 7.4.1 Soots in quench water effluent 101 7.5 Summary 102 8 Summary and outlook 103 Bibliography 106 9 Appendix 135 List of Figures Figure 2.1: HP POX test plant main facility components and material flow courtesy of [Lurgi GmbH, 2008] 6 Figure 2.2: Simplified scheme of HP POX plant (including quench system) [Lurgi GmbH, 2008] 7 Figure 2.3: Overview of reactions of methane 10 Figure 3.1: Simplified scheme for HP POX quench water system 18 Figure 3.2: Aspen Plus flow diagrams of simulated HP POX quench water system 19 Figure 3.3: Integration of information and functions in VBA via Microsoft Excel to Aspen Plus model 25 Figure 3.4: Integration of information and functions in Python via Microsoft Excel to Aspen Plus model 26 Figure 3.5: ASW enables Excel users to rapidly run scenarios using the underlying rigorous models to analyze plant data, monitor performance, and make better decisions. 27 Figure 4.1: Vapour-liquid equilibria system of CO2, H2S, NH3, HCN and organic acids in the quench water and extended mechanisms according to [Kamps et al., 2001], [Alvaro et al., 2000], [Kuranov et al., 1996], [Xia et al., 1999] and [Edwards et al., 1978]. 30 Figure 4.2: HP POX quench water system with pH regulator for sensitivity studies 34 Figure 4.3: Henry´s constant for CO2, H2S, NH3 and HCN derived from [Edwards et al., 1978] for CO2, [Alvaro et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN 35 Figure 4.4: Dissociation constants for CO2, H2S, NH3, HCN and H2O derived from [Alvaro et al., 2000], [Kamps et al., 2001], and [Edwards et al., 1978] 37 Figure 4.5: The flow of CO2 in the quench water cycle (test point VP1). 40 Figure 4.6: Calculated quench water temperature variation and effects on CO2 distribution 42 Figure 4.7: Calculated influence of pH regulation and effects on CO2 distribution 43 Figure 4.8: The flow of NH3 in the quench water cycle (test point VP1). 46 Figure 4.9: Calculated quench water temperature variation and effects on NH3 distribution 47 Figure 4.10: Calculated influence of pH regulation and effects on NH3 distribution 48 Figure 4.11: The flow of HCN in the quench water cycle (test point VP1). 49 Figure 4.12: Calculated quench water temperature variation and effects on HCN distribution 50 Figure 4.13: Calculated influence of pH regulation and effects on HCN distribution 51 Figure 4.14: The flow of H2S in the quench water cycle (test point VP1) 53 Figure 4.15: Calculated quench water temperature variation and effects on H2S distribution 54 Figure 4.16: Calculated influence of pH regulation and effects on H2S distribution 55 Figure 5.1: Aspen Plus back-calculated (real) formic acid concentration, quench water temperature and the calculated equilibrium formic acid concentration against back-calculated (real) ammonia concentration for the 47 test points (using amongst others sampled HCOO- and NH4+ values according to Table 2.6). 59 Figure 5.2: Aspen plus back-calculated (real) formic acid concentration, back-calculated (real) ammonia concentration and the calculated equilibrium formic acid concentration against quench water temperature for the 47 test points (using amongst others sampled HCOO- and NH4+ values according to Table 2.6). 60 Figure 5.3: Aspen plus back-calculated (real) acetic acid concentration, quench water temperature and the calculated equilibrium acetic acid concentration against back-calculated (real) ammonia concentration for the 47 test points. 61 Figure 5.4: Aspen plus back-calculated (real) acetic acid concentration, back-calculated (real) ammonia concentration and the calculated equilibrium acetic acid concentration against quench water temperature for the 47 test points. 62 Figure 5.5: Concentration of formic acid (Aspen plus calculated m_eq and back-calculted m_real) formation in the quench and quench water temperature for the 47 test points. 64 Figure 5.6: Concentration of formic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench against quench water temperature for the 47 test points (as in Fig.5.2). 65 Figure 5.7: Comparison between formic acid equilibrium constant (Keq), reaction quotient (Kreal) and the quench water temperature for the 47 test points. 66 Figure 5.8: Comparison between formic acid equilibrium constant (Keq) and reaction quotient (Kreal) against quench water temperatures for the 47 test points. 67 Figure 5.9: Concentration of acetic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench and quench water temperature for the 47 test points. 69 Figure 5.10: Concentration of acetic acid (Aspen plus calculated m_eq and back-calculted m_real) in the quench against quench water temperature for the 47 test points (as in Fig.5.4). 70 Figure 5.11: Comparison between acetic acid equilibrium constant (Keq), reaction quotient (Kreal) and the quench water temperature for the 47 test points. 71 Figure 5.12: Comparison between acetic acid equilibrium constant (Keq) and reaction quotient (Kreal) against quench water temperatures for the 47 test points. 72 Figure 6.1: Mole fraction of gas compoents in the hot gas outlet out of gasifier against hot gas temperature for the 47 test points 76 Figure 6.2: Calculated reaction quotient (Q) and equlibrium constant (Keq) for NH3 against hot gas temperature for the 47 test points (see Fig. 9.10 in Appendix) 77 Figure 6.3: NH3 temperature approach against hot gas temperature for the 47 test points (see Fig. 9.11 in Appendix) 78 Figure 6.4: Calculated reaction quotient (Q) and equlibrium constant (Keq) for HCN against hot gas temperature for the 47 test points (see Fig. 9.13 in Appendix) 79 Figure 6.5: HCN temperature approach against hot gas temperature for the 47 test points (see Fig. 9.14 in Appendix) 80 Figure 6.6: Comparison between calculated real and equilibrium hot gas N2, NH3 and HCN mol fractions against their respective hot gas temperature (case 1). 82 Figure 6.7: Relations between back-calculated real and equilibrium hot gas N2, NH3 and HCN mol fractions (for chemical equilibrium according to equations (6.1) and (6.4)) against their respective hot gas temperature (see Case 1, Section 6.4.1, and Fig. 6.6) 82 Figure 6.8: Comparison between calculated real and equilibrium hot gas HCN mol fraction against their respective hot gas temperature (case 2). 83 Figure 6.9: Relations between back-calculated real and equilibrium hot gas HCN mol fractions, and change in NH3 mol fractions (for chemical equilibrium according to equation (6.4)), against their respective hot gas temperature (see. Case 2, Section 6.4.2 and Fig. 6.7) 84 Figure 6.10 Comparison between NH3 and HCN formation (mole fraction) calculated equilibrium constant (Keq) and calculated reaction quotient (Q), N2 consumption and hot gas temperatures for the 47 test points (case 1 and case 2). 85 Figure 7.1: HP POX test plant quench water system 88 Figure 7.2: Traces of BTEX measured in the Gas-POX 203 – 207 quench water effluent sample. 91 Figure 7.3: Individual component of BTEX measured in the Gas-POX 203 – 207 quench water effluent sample. 92 Figure 7.4: (a) Alkyl radical decomposition and (b) C1 and C2 hydrocarbons oxidation mechanism [Warnatz et al., 2000] 93 Figure 7.5: Recombination of C3H3 to form benzene 94 Figure 7.6: The Diels - Alder reaction for the formation of PAHs 95 Figure 7.7: Amount of PAHs that were detected in Gas-POX 203 – 207 test points quench water effluent samples. 97 Figure 7.8: Distribution of PAH compounds in Gas-POX 203 – 207 quench water effluent samples. 98 Figure 7.9: Some steps in soot formation [McEnally et al., 2006]. 99 Figure 7.10: Illustration of soot formation path in homogenous mixture [Bockhorn et al., 1994] 100 Figure 9.1: Aspen flow sheet set up for HP POX quench system GasPOX 201 VP1 (simplified and extension of Fig. 3.2, organic acids not taken into account). Tabulated values are given in Table 9.11. 135 Figure 9.2: Comparison between the Henry´s constant profiles: Aspen Plus (markers) and Literatures (solid lines) ([Edwards et al., 1978] for CO2, [Alvaro et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN as it can be seen in Fig. 4.3) 137 Figure 9.3: Henry´s constant profiles derived from literatures ([Edwards et al., 1978] for CO2, [Alvaro Pérez-Salado et al., 2000] for NH3, [Kamps et al., 2001] for H2S, and [Rumpf et al., 1992] for HCN as it can be seen in Fig. 4.3) 137 Figure 9.4: Comparison between the dissociation constant profiles: Aspen Plus (markers) and Literatures (solid or dashed lines) [Alvaro et al., 2000], [Kamps et al., 2001], and [Edwards et al., 1978] as in Fig.4.4. 138 Figure 9.5: Dissociation constant profiles derived from literatures [Kamps et al., 2001], and [Edwards et al., 1978] as in Fig.4.4. 138 Figure 9.6: Calculated pH values, temperature range and species 139 Figure 9.7: Aspen Plus flow sheet setup for organic acid compounds calculations (GasPOX 201 VP1, see also Table 9.12) 142 Figure 9.8: Aspen Plus flow sheet setup for nitrogen compounds calculations (GasPOX 201 VP1, see also Table 9.12, organic acids are taken into account in the aqueous streams of the quench system) 145 Figure 9.9: Yield of ammonia in gasifier (calculated real) and hot gas temperature against the 47 test points 146 Figure 9.10: Kreal or reaction quotient for ammonia formation in the gasifier against the 47 test points. 146 Figure 9.11: Temperature approach studies for ammonia and the 47 test points 147 Figure 9.12: Yield of HCN from the gasifier (calculated real and equilibrium) and hot gas temperature and the 47 test points 147 Figure 9.13: Comparison between equilibrium constant and reaction quotient for HCN and 47 test points 148 Figure 9.14: Temperature approach studies for HCN and the 47 test points 148 Figure 9.15: Comparison among equilibrium constants of reactions against temperature, T [°C] 149 Figure 9.16: Comparison among equilibrium constants of reactions against temperature, 1/T [1/K] 150 List of Tables Table 2.1: Outline of Gas-POX mode operating parameter range 8 Table 2.2: Outline of test runs operating mode and parameters of chosen test campaigns 9 Table 2.3: Natural gas feedstock compositions 12 Table 2.4: Product synthesis gas analysis method (hot gas before quench) [Brüggemann, 2010] 12 Table 2.5: Analysis methods for raw synthesis gas [Brüggemann, 2010] 13 Table 2.6: Analysis methods for aqueous phase products [Brüggemann, 2010] 14 Table 2.7: Relative accuracy for the measured value for temperature, pressure and flow of each feed and product stream [Meyer, 2007] and [Brüggemann, 2010] 17 Table 3.1: Description of blocks used in Aspen Plus simulation. 20 Table 3.2: HP POX test plant quench water cycle parameters Gas-POX 201 VP1* 23 Table 3.3: pH regulator parameters 24 Table 4.1: Organic acids distribution in streams for VP1 based on calculation from Aspen Plus. 38 Table 4.2: The distribution of CO2 and its ions in all the streams 40 Table 4.3: The distribution of NH3 and its ions in all the streams 45 Table 4.4: The distribution of HCN and its ions in all the streams 49 Table 4.5: The distribution of H2S and its ions in all the streams 52 Table 7.1: Relative sooting tendency [Tesner et al., 2010] 101 Table 9.1: Natural gas feed analysis method [Brüggemann, 2010] 135 Table 9.2: pH scale with examples of solution [NALCO 2008] 136 Table 9.3: Gas-POX test campaigns and with designated serial numbers 140 Table 9.4: Summary of correlation coefficient (r) from Figures in Chapter 5 144 Table 9.5: Comparison among reactions temperatures and heat of reactions 149 Table 9.6: Content of BTEX compounds in Gas-POX quench water samples 151 Table 9.7: BTEX in quench water effluent samples results 152 Table 9.8: Content of PAH compounds in Gas-POX quench water samples 157 Table 9.9: PAHs in quench water effluent samples results 160 Table 9.10: Soot in quench water effluent samples results 169 Table 9.11: Aspen Plus flow sheet setup stream details (GasPOX 201 VP1, according to Fig.3.2 and Fig.9.1, organic acids not taken into account) 170 Table 9.12: Aspen Plus flow sheet setup for organic acid and nitrogen compounds calculations for GasPOX 201 VP1 (according to Figures 9.7 and 9.8, organic acids are taken into account) 174

Aufkonvertierungs-Nanokristalle (UCNPs), wie NaYF4 Kristalle, welche mit Yb3+ and Er3+ Ionen dotiert sind, emittieren höher energetisches Licht im ultravioletten/sichtbaren und nahinfraroten Bereich, nachdem sie mit weniger energiereichem nahinfraroten Licht angeregt wurden. Damit besitzen sie einzigartige optische Eigenschaften, wie verschiedenfarbige Emissionsbanden, verringerte Hintergrundfluoreszenz, größere Eindringtiefen in organisches Probenmaterial und eine hohe Lichtstabilität. Diese Eigenschaften sind besonders in der optischen Bioanalyse, in medizinischen und technischen Anwendungen von Vorteil. In dieser Arbeit werden die photophysikalischen und spektralen Eigenschaften von UCNPs im Ensemble und an Einzelpartikeln untersucht. Ein dafür entwickeltes konfokales Mikroskop ermöglicht Einzelpartikelmessungen bis in den Sättigungsbereich der UCNPs bei hohen Laser Anregungsleistungsdichten (P). Die erste Studie dieser Arbeit umfasst Ensemble- und Einzelpartikelmessungen an Kern und Kern-Schale 𝛽-NaYF4 Kristallen, welche mit 20% Yb3+ und 1% bis 3% Er3+ Ionen dotiert sind, wobei die optischen Eigenschaften P-abhängig über sechs Größenordnungen untersucht wurden. Die zweite Studie diskutiert die Einflüsse bei starker Änderung der Yb3+/Er3+ Ionen Dotierung anhand von drei verschiedenen Probensystemen. Diese unterscheiden sich sowohl in der Partikelgröße als auch in der Synthesevorschrift. Bei der dritten Studie wurde die direkte Anregung von Yb3+ mit der von Nd3+ Ionen an Nd/Yb/Er dotierten NaYF4 Partikeln bezüglich des aufkonvertierten Lumineszenz Verhaltens in Wasser verglichen. In weiteren Messungen wurde sowohl der Lumineszenz Resonanz Energie Transfer (LRET) ausgehend von einem UCNP zu dem Farbstoff Sulforhodamine B, als auch plasmonische Wechselwirkungen von Au-Schale UCNPs bei Einzelpartikelmessungen untersucht. / Upconversion nanoparticles (UCNPs), such as, NaYF4 crystals co-doped with Yb3+ and Er3+ ions, emit higher energetic light in the UV/vis and NIR range under lower energetic NIR excitation. This generates unique optical properties, for example, multi-colour band emissions, reduced background fluorescence, deeper tissue penetration depths and high photostability rendering UCNPs attractive options for bioimaging, medicinal and engineering applications. In this thesis the influence of multi-factor parameters on the photo-physical and spectroscopic properties of UCNPs are investigated under ensemble and single particle (SP) condition. For this purpose, a confocal laser scanning microscope was constructed to enable the characterisation of individual UCNPs up to their saturation conditions at high laser power densities (P). At first, ensemble and SP studies of core- and core-shell 𝛽-NaYF4 crystals co-doped with 20% Yb3+ and 1% to 3% Er3+ are performed over a P-range of six orders of magnitude. The second part of this thesis discusses influences in a wide variation in Yb3+/Er3+ ion doping concentration. Thereby, three different sample sets of varying size have been studied, using different synthesis approaches. A comparison of the Nd- and Yb-excitation of Nd/Yb/Er triple-doped NaYF4 UCNPs regarding their upconversion luminescence performance in water is provided in the third section of the thesis. In further studies, the process of luminescence resonance energy transfer (LRET) from an UCNP to the sulforhodamine B dye and the plasmonic interaction of an Au-shelled UCNP have been examined at the SP level.