We present an overview of the current and future adaptive optics systems at the LBTO along with the current and planned science instruments they feed. All the AO systems make use of the two 672 actuator adaptive secondary mirrors. They are (1) FLAO (NGS/SCAO) feeding the LUCI NIR imagers/spectrographs; (2) LBTI/AO (NGS/SCAO) feeding the NIR/MIR imagers and LBTI beam combiner; (3) the ARGOS LGS GLAO system feeding LUCIs; and (4) LINO-NIRVANA - an NGS/MCAO imager and interferometer system. AO performance of the current systems is presented along with proposed performances for the newer systems taking into account the future instrumentation.

Les polymères de coordination (PCs) à base de thiolates de métaux monétaires sont bien connus pour leurs propriétés luminescentes. Cependant, leurs structures sont sous-explorées. Dans cette thèse, nous présentons une étude pionnière visant la compréhension de la formation de la structure et de la corrélation ‘structure-propriétés’ des PCs homoleptiques neutres, [M(SR)]n, M = Cu(I), Ag(I), Au(I). Les composés avec les dérivés du thiophénolate étudiés dans ce travail, illustrent comment l'utilisation de certains ligands organiques fonctionnalisés conduit à la formation de réseaux 2D étendus ou de colonnes 1D par l'addition d'un encombrement stérique. De plus, la première étude structurelle comparative des PCs thiolées amorphes a été réalisée par analyse PDF. Les études photophysiques ont montré la diversité des propriétés luminescentes des PCs à base de thiolates de métaux monétaires. Des pics d'émission doubles ou multiples, un rendement quantique élevé, des émetteurs orange à proche infrarouge, des variations significatives de durée de vie en fonction de la température... toutes ces propriétés intrinsèques révèlent le potentiel élevé de ces composés pour diverses applications optiques / The coordination polymers (CPs) based on thiolates of coinage metals are well known for their luminescence properties. However, their structures stayed underexplored. In the thesis we present a pioneering study targeting the understanding of the structure formation and the ‘structure-properties’ correlation for neutral homoleptic CPs, [M(SR)]n, M = Cu(I), Ag(I), Au(I). The compounds with thiophenolate derivatives studied in the work, illustrate how the use of some functionalized organic ligands leads to the formation of extended 2D networks or 1D columns by addition of some steric hindrance. The first comparative structural study of amorphous thiolated CPs was performed by PDF analysis. The photophysical studies showed the diversity of luminescent properties of the CPs based on thiolates of coinage metals. Double or multiple emission peaks, high quantum yield of orange-toinfrared emitters, significant lifetime variation with temperature… all of these intrinsic properties reveal the high potential of these compounds for diverse optical applications

In astronomy, interferometry by ground-based telescopes offers the greatest angular resolution. However, the Earth´s atmosphere distorts the incident wavefront from a celestial object, leading to blurring and signal loss. It also restricts the transmission of specific wavelengths within the electromagnetic spectrum. Space-based interferometers would mitigate atmospheric obstruction and potentially enable even higher angular resolutions. The main challenge of implementing space-based interferometry is the necessity of matching the light´s optical path differences at the telescopes within the coherence length of the light utilizing physical delay lines. This thesis explores the potential realization of digital delay lines via heterodyne interferometry. The technique generates a heterodyne beat note at the frequency difference between the incident stellar light and a reference laser in the radio regime, permitting digitization of the delay line while preserving the phase information for image reconstruction. The primary objective of the thesis is to advance the field of astronomy by constructing a testbed environment for investigating future space-based heterodyne interferometry in the NIR light range. It requires the achievement of two main tasks. Firstly, a laboratory starlight simulator is developed to simulate a distant star´s wavefront appearance as it reaches telescopes on or around Earth. The consequent starlight simulator contains an optical assembly that manifests a point source in NIR light, aligned with a mirror collimator’s focal point, transforming the wavefront from spherical to planar. Secondly, a fiber optical circuit with interference capability is constructed, consisting of a free-space optical delay line and a polarization-controlled custom-sized fiber. The delay line matches the optical paths within the light's coherence length, while the polarization controller optimizes interference visibility. The completion of the tasks establishes the foundation to investigate space-based heterodyne interferometry in the NIR light with the potential implementation of delay line digitization.

Die vorliegende Arbeit behandelt die elektrochemische Synthese (elektrochemische Polymerisation und Copolymerisation) und die Charakterisierung der Redox- und sensorischen Eigenschaften neuer funktionalisierter Polymere für die Ionensensorik. Die Funktionalisierung wird sowohl in der Polymer-Hauptkette (Polysalene) als auch in der Polymer-Seitenkette (ein Thiophen-Copolymer: 3-Methylthiophen/6-Hydroxy-2-(2-(3-thienyl)-ethoxy)-acetophenon) dargestellt. Die Redox-Prozesse der funktionalisierten Polymere wurden mit spektroelektrochemischen Methoden: ESR-, UV-Vis-NIR- und FTIR-Spektroelektrochemie charakterisiert. Durch diese Methoden konnten während der elektrochemischen Oxidation von funktionalisierten leitfähigen Polymeren verschiedene Polymer- bzw. Copolymer-Ladungsträger nachgewiesen werden: Polaronen, Bipolaronen beim Thiophen-Copolymer, zwei Polaronen auf einer Polymerkette im Singulettezustand beim Poly(3-methylthiophen) und eine diamagnetische Spin-Spin-Wechselwirkung zwischen ungepaarten Elektronen der Cu(II)-Ionen und der ungepaarten Elektronen von bisphenolischen Ligand-Kationradikalen beim Poly[Cu(II)-salen]. Sensorische Eigenschaften gegenüber Ni(II)-Ionen wurden durch Potentiometrie an einem Poly[Ni(II)-salen]-Derivat getestet. Es zeigt eine gute potentiometrische Ni(II)-Ionenselektivität (der Logarithmus des potentiometrischen Selektivitätskoeffizienten liegt im Bereich von -0.5 bis -1.5) in Anwesenheit von Cd(II), Mn(II), Zn(II) und Na(I).

Extensive deforestations, particularly in the (sub)tropics, have led to intense soil degradation and erosion with concomitant reduction in soil fertility. Reforestations or plantations on those degraded sites may provide effective measures to mitigate further soil degradation and erosion, and can lead to improved soil quality. However, a change in land use from, e.g., grassland to forest may have a crucial impact on water balance. This may affect water availability even under humid tropical climate conditions where water is normally not a limiting factor. In this context, it should also be considered that according to climate change projections rainfall may decrease in some of these regions. To mitigate climate change related problems (e.g. increases in erosion and drought), reforestations are often carried out. Unfortunately, those measures are seldom completely successful, because the environmental conditions and the plant specific requirements are not appropriately taken into account. This is often due to data-scarcity and limited financial resources in tropical regions. For this reason, innovative approaches are required that are able to measure environmental conditions quasi-continuously in a cost-effective manner. Simultaneously, reforestation measures should be accompanied by monitoring in order to evaluate reforestation success and to mitigate, or at least to reduce, potential problems associated with reforestation (e.g. water scarcity). To avoid reforestation failure and negative implications on ecosystem services, it is crucial to get insights into the water balance of the actual ecosystem, and potential changes resulting from reforestation. The identification and prediction of water balance changes as a result of reforestation under climate change requires the consideration of the complex feedback system of processes in the soil-vegetation-atmosphere continuum. Models that account for those feedback system are Soil-Vegetation-Atmosphere-Transfer (SVAT) models. For the before-mentioned reasons, this study targeted two main objectives: (i) to develop and test a method combination for site evaluation under data scarcity (i.e. study requirements) (Part I) and (ii) to investigate the consequences of prediction uncertainty of the SVAT model input parameters, which were derived using geophysical methods, on SVAT modeling (Part II). A water balance modeling approach was set at the center of the site evaluation approach. This study used the one-dimensional CoupModel, which is a SVAT model. CoupModel requires detailed spatial soil information for (i) model parameterization, (ii) upscaling of model results and accounting for local to regional-scale soil heterogeneity, and (iii) monitoring of changes in soil properties and plant characteristics over time. Since traditional approaches to soil and vegetation sampling and monitoring are time consuming and expensive (and therefore often limited to point information), geophysical methods were used to overcome this spatial limitation. For this reason, vis-NIR spectroscopy (visible to near-infrared wavelength range) was applied for the measurement of soil properties (physical and chemical), and remote sensing to derive vegetation characteristics (i.e. leaf area index (LAI)). Since the estimated soil properties (mainly texture) could be used to parameterize a SVAT model, this study investigated the whole processing chain and related prediction uncertainty of soil texture and LAI, and their impact on CoupModel water balance prediction uncertainty. A greenhouse experiment with bamboo plants was carried out to determine plant-physiological characteristics needed for CoupModel parameterization. Geoelectrics was used to investigate soil layering, with the intent of determining site-representative soil profiles for model parameterization. Soil structure was investigated using image analysis techniques that allow the quantitative assessment and comparability of structural features. In order to meet the requirements of the selected study approach, the developed methodology was applied and tested for a site in NE-Brazil (which has low data availability) with a bamboo plantation as the test site and a secondary forest as the reference (reference site). Nevertheless, the objective of the thesis was not the concrete modeling of the case study site, but rather the evaluation of the suitability of the selected methods to evaluate sites for reforestations and to monitor their influence on the water balance as well as soil properties. The results (Part III) highlight that one needs to be aware of the measurement uncertainty related to SVAT model input parameters, so for instance the uncertainty of model input parameters such as soil texture and leaf area index influences meaningfully the simulated model water balance output. Furthermore, this work indicates that vis-NIR spectroscopy is a fast and cost-efficient method for soil measurement, mapping, and monitoring of soil physical (texture) and chemical (N, TOC, TIC, TC) properties, where the quality of soil prediction depends on the instrument (e.g. sensor resolution), the sample properties (i.e. chemistry), and the site characteristics (i.e. climate). Additionally, also the sensitivity of the CoupModel with respect to texture prediction uncertainty with respect to surface runoff, transpiration, evaporation, evapotranspiration, and soil water content depends on site conditions (i.e. climate and soil type). For this reason, it is recommended that SVAT model sensitivity analysis be carried out prior to field spectroscopic measurements to account for site specific climate and soil conditions. Nevertheless, mapping of the soil properties estimated via spectroscopy using kriging resulted in poor interpolation (i.e. weak variograms) results as a consequence of a summation of uncertainty arising from the method of field measurement to mapping (i.e. spectroscopic soil prediction, kriging error) and site-specific ‘small-scale’ heterogeneity. The selected soil evaluation method (vis-NIR spectroscopy, structure comparison using image analysis, traditional laboratory analysis) showed that there are significant differences between the bamboo soil and the adjacent secondary forest soil established on the same soil type (Vertisol). Reflecting on the major study results, it can be stated that the selected method combination is a way forward to a more detailed and efficient way to evaluate the suitability of a specific site for reforestation. The results of this study provide insights into where and when during soil and vegetation measurements a high measurement accuracy is required to minimize uncertainties in SVAT modeling.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Umfangreiche Abholzungen, besonders in den (Sub-)Tropen, habe zu intensiver Bodendegradierung und Erosion mit einhergehendem Verlust der Bodenfruchtbarkeit geführt. Eine wirksame Maßnahme zur Vermeidung fortschreitender Bodendegradierung und Erosion sind Aufforstungen auf diesen Flächen, die bisweilen zu einer verbesserten Bodenqualität führen können. Eine Umwandlung von Grünland zu Wald kann jedoch einen entscheidenden Einfluss auf den Wasserhaushalt haben. Selbst unter humid-tropischen Klimabedingungen, wo Wasser in der Regel kein begrenzender Faktor ist, können sich Aufforstungen negativ auf die Wasserverfügbarkeit auswirken. In diesem Zusammenhang muss auch berücksichtigt werden, dass Klimamodelle eine Abnahme der Niederschläge in einigen dieser Regionen prognostizieren. Um die Probleme, die mit dem Klimawandel in Verbindung stehen zu mildern (z.B. Zunahme von Erosion und Dürreperioden), wurden und werden bereits umfangreiche Aufforstungsmaßnahmen durchgeführt. Viele dieser Maßnahmen waren nicht immer umfassend erfolgreich, weil die Umgebungsbedingungen sowie die pflanzenspezifischen Anforderungen nicht angemessen berücksichtigt wurden. Dies liegt häufig an der schlechten Datengrundlage sowie an den in vielen Entwicklungs- und Schwellenländern begrenzter verfügbarer finanzieller Mittel. Aus diesem Grund werden innovative Ansätze benötigt, die in der Lage sind quasi-kontinuierlich und kostengünstig die Standortbedingungen zu erfassen und zu bewerten. Gleichzeitig sollte eine Überwachung der Wiederaufforstungsmaßnahme erfolgen, um deren Erfolg zu bewerten und potentielle negative Effekte (z.B. Wasserknappheit) zu erkennen und diesen entgegenzuwirken bzw. reduzieren zu können. Um zu vermeiden, dass Wiederaufforstungen fehlschlagen oder negative Auswirkungen auf die Ökosystemdienstleistungen haben, ist es entscheidend, Kenntnisse vom tatsächlichen Wasserhaushalt des Ökosystems zu erhalten und Änderungen des Wasserhaushalts durch Wiederaufforstungen vorhersagen zu können. Die Ermittlung und Vorhersage von Wasserhaushaltsänderungen infolge einer Aufforstung unter Berücksichtigung des Klimawandels erfordert die Berücksichtigung komplex-verzahnter Rückkopplungsprozesse im Boden-Vegetations-Atmosphären Kontinuum. Hydrologische Modelle, die explizit den Einfluss der Vegetation auf den Wasserhaushalt untersuchen sind Soil-Vegetation-Atmosphere-Transfer (SVAT) Modelle. Die vorliegende Studie verfolgte zwei Hauptziele: (i) die Entwicklung und Erprobung einer Methodenkombination zur Standortbewertung unter Datenknappheit (d.h. Grundanforderung des Ansatzes) (Teil I) und (ii) die Untersuchung des Einflusses der mit geophysikalischen Methoden vorhergesagten SVAT-Modeleingangsparameter (d.h. Vorhersageunsicherheiten) auf die Modellierung (Teil II). Eine Wasserhaushaltsmodellierung wurde in den Mittelpunkt der Methodenkombination gesetzt. In dieser Studie wurde das 1D SVAT Model CoupModel verwendet. CoupModel benötigen detaillierte räumliche Bodeninformationen (i) zur Modellparametrisierung, (ii) zum Hochskalierung von Modellergebnissen unter Berücksichtigung lokaler und regionaler Bodenheterogenität, und (iii) zur Beobachtung (Monitoring) der zeitlichen Veränderungen des Bodens und der Vegetation. Traditionelle Ansätze zur Messung von Boden- und Vegetationseigenschaften und deren Monitoring sind jedoch zeitaufwendig, teuer und beschränken sich daher oft auf Punktinformationen. Ein vielversprechender Ansatz zur Überwindung der räumlichen Einschränkung sind die Nutzung geophysikalischer Methoden. Aus diesem Grund wurden vis-NIR Spektroskopie (sichtbarer bis nah-infraroter Wellenlängenbereich) zur quasi-kontinuierlichen Messung von physikalischer und chemischer Bodeneigenschaften und Satelliten-basierte Fernerkundung zur Ableitung von Vegetationscharakteristika (d.h. Blattflächenindex (BFI)) eingesetzt. Da die mit geophysikalisch hergeleiteten Bodenparameter (hier Bodenart) und Pflanzenparameter zur Parametrisierung eines SVAT Models verwendet werden können, wurde die gesamte Prozessierungskette und die damit verbundenen Unsicherheiten und deren potentiellen Auswirkungen auf die Wasserhaushaltsmodellierung mit CoupModel untersucht. Ein Gewächshausexperiment mit Bambuspflanzen wurde durchgeführt, um die zur CoupModel Parametrisierung notwendigen pflanzenphysio- logischen Parameter zu bestimmen. Geoelektrik wurde eingesetzt, um die Bodenschichtung der Untersuchungsfläche zu untersuchen und ein repräsentatives Bodenprofil zur Modellierung zu definieren. Die Bodenstruktur wurde unter Verwendung einer Bildanalysetechnik ausgewertet, die die qualitativen Bewertung und Vergleichbarkeit struktureller Merkmale ermöglicht. Um den Anforderungen des gewählten Standortbewertungsansatzes gerecht zu werden, wurde die Methodik auf einem Standort mit einer Bambusplantage und einem Sekundärregenwald (als Referenzfläche) in NO-Brasilien (d.h. geringe Datenverfügbarkeit) entwickelt und getestet. Das Ziel dieser Arbeit war jedoch nicht die Modellierung dieses konkreten Standortes, sondern die Bewertung der Eignung des gewählten Methodenansatzes zur Standortbewertung für Aufforstungen und deren zeitliche Beobachtung, als auch die Bewertung des Einfluss von Aufforstungen auf den Wasserhaushalt und die Bodenqualität. Die Ergebnisse (Teil III) verdeutlichen, dass es notwendig ist, sich den potentiellen Einfluss der Messunsicherheiten der SVAT Modelleingangsparameter auf die Modellierung bewusst zu sein. Beispielsweise zeigte sich, dass die Vorhersageunsicherheiten der Bodentextur und des BFI einen bedeutenden Einfluss auf die Wasserhaushaltsmodellierung mit CoupModel hatte. Die Arbeit zeigt weiterhin, dass vis-NIR Spektroskopie zur schnellen und kostengünstigen Messung, Kartierung und Überwachung boden-physikalischer (Bodenart) und -chemischer (N, TOC, TIC, TC) Eigenschaften geeignet ist. Die Qualität der Bodenvorhersage hängt vom Instrument (z.B. Sensorauflösung), den Probeneigenschaften (z.B. chemische Zusammensetzung) und den Standortmerkmalen (z.B. Klima) ab. Die Sensitivitätsanalyse mit CoupModel zeigte, dass der Einfluss der spektralen Bodenartvorhersageunsicherheiten auf den mit CoupModel simulierten Oberflächenabfluss, Evaporation, Transpiration und Evapotranspiration ebenfalls von den Standortbedingungen (z.B. Klima, Bodentyp) abhängt. Aus diesem Grund wird empfohlen eine SVAT Model Sensitivitätsanalyse vor der spektroskopischen Feldmessung von Bodenparametern durchzuführen, um die Standort-spezifischen Boden- und Klimabedingungen angemessen zu berücksichtigen. Die Anfertigung einer Bodenkarte unter Verwendung von Kriging führte zu schlechten Interpolationsergebnissen in Folge der Aufsummierung von Mess- und Schätzunsicherheiten (d.h. bei spektroskopischer Feldmessung, Kriging-Fehler) und der kleinskaligen Bodenheterogenität. Anhand des gewählten Bodenbewertungsansatzes (vis-NIR Spektroskopie, Strukturvergleich mit Bildanalysetechnik, traditionelle Laboranalysen) konnte gezeigt werden, dass es bei gleichem Bodentyp (Vertisol) signifikante Unterschiede zwischen den Böden unter Bambus und Sekundärwald gibt. Anhand der wichtigsten Ergebnisse kann festgehalten werden, dass die gewählte Methodenkombination zur detailreicheren und effizienteren Standortuntersuchung und -bewertung für Aufforstungen beitragen kann. Die Ergebnisse dieser Studie geben einen Einblick darauf, wo und wann bei Boden- und Vegetationsmessungen eine besonders hohe Messgenauigkeit erforderlich ist, um Unsicherheiten bei der SVAT Modellierung zu minimieren.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Extensos desmatamentos que estão sendo feitos especialmente nos trópicos e sub-trópicos resultam em uma intensa degradação do solo e num aumento da erosão gerando assim uma redução na sua fertilidade. Reflorestamentos ou plantações nestas áreas degradadas podem ser medidas eficazes para atenuar esses problemas e levar a uma melhoria da qualidade do mesmo. No entanto, uma mudança no uso da terra, por exemplo de pastagem para floresta pode ter um impacto crucial no balanço hídrico e isso pode afetar a disponibilidade de água, mesmo sob condições de clima tropical úmido, onde a água normalmente não é um fator limitante. Devemos levar também em consideração que de acordo com projeções de mudanças climáticas, as precipitações em algumas dessas regiões também diminuirão agravando assim, ainda mais o quadro apresentado. Para mitigar esses problemas relacionados com as alterações climáticas, reflorestamentos são frequentemente realizados mas raramente são bem-sucedidos, pois condições ambientais como os requisitos específicos de cada espécie de planta, não são devidamente levados em consideração. Isso é muitas vezes devido, não só pela falta de dados, como também por recursos financeiros limitados, que são problemas comuns em regiões tropicais. Por esses motivos, são necessárias abordagens inovadoras que devam ser capazes de medir as condições ambientais quase continuamente e de maneira rentável. Simultaneamente com o reflorestamento, deve ser feita uma monitoração a fim de avaliar o sucesso da atividade e para prevenir, ou pelo menos, reduzir os problemas potenciais associados com o mesmo (por exemplo, a escassez de água). Para se evitar falhas e reduzir implicações negativas sobre os ecossistemas, é crucial obter percepções sobre o real balanço hídrico e as mudanças que seriam geradas por esse reflorestamento. Por este motivo, esta tese teve como objetivo desenvolver e testar uma combinação de métodos para avaliação de áreas adequadas para reflorestamento. Com esse intuito, foi colocada no centro da abordagem de avaliação a modelagem do balanço hídrico local, que permite a identificação e estimação de possíveis alterações causadas pelo reflorestamento sob mudança climática considerando o sistema complexo de realimentação e a interação de processos do continuum solo-vegetação-atmosfera. Esses modelos hidrológicos que investigam explicitamente a influência da vegetação no equilíbrio da água são conhecidos como modelos Solo-Vegetação-Atmosfera (SVAT). Esta pesquisa focou em dois objetivos principais: (i) desenvolvimento e teste de uma combinação de métodos para avaliação de áreas que sofrem com a escassez de dados (pré-requisito do estudo) (Parte I), e (ii) a investigação das consequências da incerteza nos parâmetros de entrada do modelo SVAT, provenientes de dados geofísicos, para modelagem hídrica (Parte II). A fim de satisfazer esses objetivos, o estudo foi feito no nordeste brasileiro,por representar uma área de grande escassez de dados, utilizando como base uma plantação de bambu e uma área de floresta secundária. Uma modelagem do balanço hídrico foi disposta no centro da metodologia para a avaliação de áreas. Este estudo utilizou o CoupModel que é um modelo SVAT unidimensional e que requer informações espaciais detalhadas do solo para (i) a parametrização do modelo, (ii) aumento da escala dos resultados da modelagem, considerando a heterogeneidade do solo de escala local para regional e (iii) o monitoramento de mudanças nas propriedades do solo e características da vegetação ao longo do tempo. Entretanto, as abordagens tradicionais para amostragem de solo e de vegetação e o monitoramento são demorados e caros e portanto muitas vezes limitadas a informações pontuais. Por esta razão, métodos geofísicos como a espectroscopia visível e infravermelho próximo (vis-NIR) e sensoriamento remoto foram utilizados respectivamente para a medição de propriedades físicas e químicas do solo e para derivar as características da vegetação baseado no índice da área foliar (IAF). Como as propriedades estimadas de solo (principalmente a textura) poderiam ser usadas para parametrizar um modelo SVAT, este estudo investigou toda a cadeia de processamento e as incertezas de previsão relacionadas à textura de solo e ao IAF. Além disso explorou o impacto destas incertezas criadas sobre a previsão do balanço hídrico simulado por CoupModel. O método geoelétrico foi aplicado para investigar a estratificação do solo visando a determinação de um perfil representante. Já a sua estrutura foi explorada usando uma técnica de análise de imagens que permitiu a avaliação quantitativa e a comparabilidade dos aspectos estruturais. Um experimento realizado em uma estufa com plantas de bambu (Bambusa vulgaris) foi criado a fim de determinar as caraterísticas fisiológicas desta espécie que posteriormente seriam utilizadas como parâmetros para o CoupModel. Os resultados do estudo (Parte III) destacam que é preciso estar consciente das incertezas relacionadas à medição de parâmetros de entrada do modelo SVAT. A incerteza presente em alguns parâmetros de entrada como por exemplo, textura de solo e o IAF influencia significantemente a modelagem do balanço hídrico. Mesmo assim, esta pesquisa indica que vis-NIR espectroscopia é um método rápido e economicamente viável para medir, mapear e monitorar as propriedades físicas (textura) e químicas (N, TOC, TIC, TC) do solo. A precisão da previsão dessas propriedades depende do tipo de instrumento (por exemplo da resolução do sensor), da propriedade da amostra (a composição química por exemplo) e das características das condições climáticas da área. Os resultados apontam também que a sensitividade do CoupModel à incerteza da previsão da textura de solo em respeito ao escoamento superficial, transpiração, evaporação, evapotranspiração e ao conteúdo de água no solo depende das condições gerais da área (por exemplo condições climáticas e tipo de solo). Por isso, é recomendado realizar uma análise de sensitividade do modelo SVAT prior a medição espectral do solo no campo, para poder considerar adequadamente as condições especificas do área em relação ao clima e ao solo. Além disso, o mapeamento de propriedades de solo previstas pela espectroscopia usando o kriging, resultou em interpolações de baixa qualidade (variogramas fracos) como consequência da acumulação de incertezas surgidas desde a medição no campo até o seu mapeamento (ou seja, previsão do solo via espectroscopia, erro do kriging) e heterogeneidade especifica de uma pequena escala. Osmétodos selecionados para avaliação das áreas (vis-NIR espectroscopia, comparação da estrutura de solo por meio de análise de imagens, análise de laboratório tradicionais) revelou a existência de diferenças significativas entre o solo sob bambu e o sob floresta secundária, apesar de ambas terem sido estabelecidas no mesmo tipo de solo (vertissolo). Refletindo sobre os principais resultados do estudo, pode-se afirmar que a combinação dos métodos escolhidos e aplicados representam uma forma mais detalhada e eficaz de avaliar se uma determinada área é adequada para ser reflorestada. Os resultados apresentados fornecem percepções sobre onde e quando, durante a medição do solo e da vegetação, é necessário se ter uma precisão mais alta a fim de minimizar incertezas potenciais na modelagem com o modelo SVAT.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185

Die vorliegende Arbeit behandelt die elektrochemische Synthese (elektrochemische Polymerisation und Copolymerisation) und die Charakterisierung der Redox- und sensorischen Eigenschaften neuer funktionalisierter Polymere für die Ionensensorik. Die Funktionalisierung wird sowohl in der Polymer-Hauptkette (Polysalene) als auch in der Polymer-Seitenkette (ein Thiophen-Copolymer: 3-Methylthiophen/6-Hydroxy-2-(2-(3-thienyl)-ethoxy)-acetophenon) dargestellt. Die Redox-Prozesse der funktionalisierten Polymere wurden mit spektroelektrochemischen Methoden: ESR-, UV-Vis-NIR- und FTIR-Spektroelektrochemie charakterisiert. Durch diese Methoden konnten während der elektrochemischen Oxidation von funktionalisierten leitfähigen Polymeren verschiedene Polymer- bzw. Copolymer-Ladungsträger nachgewiesen werden: Polaronen, Bipolaronen beim Thiophen-Copolymer, zwei Polaronen auf einer Polymerkette im Singulettezustand beim Poly(3-methylthiophen) und eine diamagnetische Spin-Spin-Wechselwirkung zwischen ungepaarten Elektronen der Cu(II)-Ionen und der ungepaarten Elektronen von bisphenolischen Ligand-Kationradikalen beim Poly[Cu(II)-salen]. Sensorische Eigenschaften gegenüber Ni(II)-Ionen wurden durch Potentiometrie an einem Poly[Ni(II)-salen]-Derivat getestet. Es zeigt eine gute potentiometrische Ni(II)-Ionenselektivität (der Logarithmus des potentiometrischen Selektivitätskoeffizienten liegt im Bereich von -0.5 bis -1.5) in Anwesenheit von Cd(II), Mn(II), Zn(II) und Na(I).

Les entreprises pharmaceutiques ont progressivement adopté le concept de Process Analytical Technology (PAT) afin de contrôler et d'assurer en temps réel la qualité des produits pharmaceutiques au cours de leur production. Le PAT et un composant central du concept plus général de Quality-by-Design (QbD) promu par les agence régulatrices et visant à construire la qualité des produits via une approche scientifique et la gestion des risques.Une méthode basée sur la spectroscopie proche infrarouge (PIR) a été développée comme un outil du PAT pour contrôler en ligne la cristallisation d'un principe actif pharmaceutique. Au cours du procédé les teneurs en principe actif et en solvant résiduel doivent être déterminées avec précision afin d'atteindre un point d'ensemencement prédéfini. Une méthodologie basée sur les principes du QbD a guidé le développement et la validation de la méthode tout en assurant l'adéquation avec son utilisation prévue. Des modèles basés sur les moindres carrés partiels ont été construits à l'aide d'outils chimiométriques afin de quantifier les 2 analytes d'intérêt. La méthode a été totalement validée conformément aux requis officiels en utilisant les profils d'exactitude. Un suivi du procédé en temps réel a permis de prouver que la méthode correspond à son usage prévu.L'implémentation de cette méthode comme à l'échelle industrielle au lancement de ce nouveau procédé permettra le contrôle automatique de l'étape de cristallisation dans le but d'assurer un niveau de qualité prédéfini de l'API. D'autres avantages sont attendus incluant la réduction du temps du procédé, la suppression d'un échantillonnage difficile et d'analyses hors ligne fastidieuses. / Pharmaceutical companies are progressively adopting and introducing the Process Analytical Technology (PAT) concept to control and ensure in real-time product quality in development and manufacturing. PAT is a key component of the Quality-by-Design (QbD) framework promoted by the regulatory authorities, aiming the building of product quality based on both a strong scientific background and a quality risk management approach.An analytical method based on near infrared (NIR) spectroscopy was developed as a PAT tool to control on-line an API (active pharmaceutical ingredient) crystallization. During this process the API and residual solvent contents need to be precisely determined to reach a predefined seeding point. An original methodology based on the QbD principles was applied to conduct the development and validation of the NIR method and to ensure that it is fitted for its intended use. Partial least squares (PLS) models were developed and optimized through chemometrics tools in order to quantify the 2 analytes of interest. The method was fully validated according to the official requirements using the accuracy profile approach. Besides, a real-time process monitoring was added to the validation phase to prove and document that the method is fitted for purpose.Implementation of this method as an in-process control at industrial plant from the launch of this new pharmaceutical process will enable automatic control of the crystallization step in order to ensure a predefined quality level of the API. Other valuable benefits are expected such as reduction of the process time, and suppression of a difficult sampling and tedious off-line analyzes.

Les traitements antireflets (AR) sont très largement utilisés pour améliorer la transmission de systèmes optiques composés de hublots, lentilles, de lames séparatrices,… Dans cette thèse les gammes spectrales visées sont le visible étendu [400-1800nm] et le moyen infrarouge [3,7-4,8µm]. La méthode de nanostructuration par dépôts de films minces utilisant des techniques PVD en incidence oblique (Oblique Angle Deposition) a été choisie car elle permet d’envisager des AR hautes performances sur une large gamme de longueur d’onde, via un procédé industrialisable. L’introduction de porosité via le contrôle des angles de dépôt est utilisée pour nanostructurer l’architecture de chaque couche et de l’empilement ; méthode permettant de modifier et d’optimiser les propriétés optiques des couches constituantes en vue d’un design complet optimal. Une cartographie des indices effectifs accessibles par OAD a été dégagée concernant les trois matériaux déposés (TiO2, SiO2 et Ge). Mais les propriétés optiques de ces couches nanostructurées diffèrent largement de celles des couches denses du fait de la présence d’anisotropie, de gradient d’indice, de diffusion et d’absorption. A partir de caractérisations microstructurales, chimiques et optiques poussées (AFM, MEB, MET, tomographie FIB, tomographie MET, EDX, EELS, spectrophotométrie et ellipsométrie généralisée) un modèle optique analytique plus complexe et couplé à des analyses par éléments finis (FDTD) est présenté. L’ensemble du travail a permis d’élaborer par OAD de simples antireflet bicouches démontrant déjà de hauts niveaux de transmission, supérieurs aux traitements AR existants (interférentiel) ou en développement (Moth-eyes). / Anti-reflective (AR) coatings are widely used to improve the transmission of optical systems composed of window, lenses, separating filters,... In this thesis, the spectral ranges targeted are the extended visible [400-1800nm] and the mid infrared [3.7-4.8µm]. Thin film deposition nanostructuring method using oblique angle deposition (oblique angle deposition) PVD technique was chosen because it allows high performance AR to be considered over a wide wavelength range, by an industrial process. The introduction of porosity with the control of deposition angle is used to nanostructure the architecture of each layer and stack; a method for modifying and optimizing the optical properties of the constituent layers for optimal complete design. A mapping of the effective indices accessible by OAD has been identified for the three materials deposited (TiO2, SiO2 and Ge). However optical properties of these nanostructured layers differ greatly from those of dense layers due to the presence of anisotropy, index gradient, diffusion and absorption. Based on advanced microstructural, chemical and optical characterizations (AFM, SEM, TEM, FIB tomography, TEM tomography, EDX, EELS, spectrophotometry and generalized ellipsometry) a more complex analytical optical model coupled with finite element analyses (FDTD) is presented. All the work has enabled OAD to develop simple two-layer anti-reflective coatings that already demonstrate high levels of transmission, superior to existing (interferential) or work in progress (Moth-eyes) AR treatments.

O grande desafio da Implantodontia é alcançar resultados a longo prazo com maior preservação dos tecidos ósseos, resposta adequada dos tecidos conjuntivos e manutenção da estabilidade secundária adequada para a retenção de próteses. Estes fatores evitam a perda indesejável tanto do tratamento reabilitador quanto dos tecidos ósseos de suporte. A região extraoral apresenta menores espessuras ósseas em comparação às regiões de maxila e mandíbula, especialmente no osso temporal. Portanto, os seguintes aspectos foram investigados: (i) Análise pelo Método dos Elementos Finitos (FEA) de 16 modelos virtuais, com aplicação de forças estáticas de tensão e compressão nos pontos médios entre os implantes. Três implantes maxilofaciais extraorais foram posicionados no osso temporal, onde um deles foi posicionado sobre o processo mastóide. Este estudo foi totalmente realizado em ambiente computacional. Foram empregados dois tipos de conexões protéticas e intermediários retos de conicidades 20º e 45º, posicionados virtualmente no modelo de osso temporal, com paralelismo entre eles. Foi desenhada uma barra virtual para retenção protética do tipo barra-clipe da prótese auricular. Foram consideradas as melhores configurações aquelas apresentando distribuição uniforme e branda das tensões de von Mises sobre o tecido ósseo adjacente aos implantes. (ii) Confecção de corpos de prova com graus variáveis de pigmentação para (a) melhorar a reprodutibilidade da coloração (b) entender e reduzir o processo de descoloração para melhorar a longevidade das próteses. Um elastômero maxilofacial tipo A foi pigmentado intrinsecamente de acordo com os seis tons da Escala de Fitzpatrick. Uma técnica de pigmentação foi desenvolvida a partir dos resultados obtidos a partir de uma Escala de Fitzpatrick impressa em papel e dos pigmentos intrínsecos de cores primárias. Este dimetil-metilvinil-siloxano reforçado por platina A 223-30 (Factor II, Inc., Lakeside, AZ, USA) apresenta dureza \"Shore-A\" 30. As amostras foram expostas a fotoenvelhecimento acelerado, e sua degradação mapeada com Espectroscopia UV/Vis/NIR. Concluiu-se que: (i) Os implantes com conexão cone Morse plataforma 3.7 tem melhor comportamento biomecânico com intermediários de 45o e altura de 2mm. Sugere-se planejamento cirúrgico visando a instalação a 3 horas para o temporal esquerdo e 9 horas para o temporal direito, mais 6 horas (mastóide). Implantes com conexão cone Morse plataforma 4.0 apresentaram comportamento biomecânico desejável com intermediários de 20o e alturas de 2mm or 4mm. Para implantes com conexão cone Morse plataforma 4.0, sugere-se a instalação a 6-9-12 horas para o temporal direito, e 12-6-3 para o esquerdo, visando comportamento biomecânico adequado. Sugere-se que os implantes extraorais Brånemark hexágono externo com plataforma 4.1 regular ou expandida sejam preferencialmente empregados com intermediários de 20o. (ii) O SiO2 5% pode ser adicionado à preparação do silicone para retardar o desbotamento da prótese maxilofacial. O TiO2 traz mudanças visuais significativas e pode ser considerado como opacificador. / Major challenges in Implantology include: achieving longevity of implants, bone tissue preservation, connective tissue suitable answer and maintenance of secondary stability in order to retain prosthesis. These factors avoid the undesirable loss of both rehabilitation treatment and supporting bone tissues. The extraoral region has less bone depth compared to the maxilla and mandible regions, especially for the temporal bone. Therefore, the following aspects were investigated: (i) Finite Elements Analysis (FEA) of 16 virtual models, with static application of tension and compression forces in medium points between implants. Three extraoral maxillofacial implants were placed on the temporal bone, which one of them was placed on the mastoid process. This study was made entirely in a computational environment. Two prosthetic connection types and straight Intermediários were employed, with conicities of 20º and 45º, placed virtually on the temporal bone model, mutually parallel. A virtual bar for bar-clip prosthetic retention of auricular prosthesis was designed. The best configurations were considered to be the ones presenting uniform and mild von Mises tension distribution over the bone tissue around the implants. (ii) Generation of samples with varying degree of pigmentation to (a) improve the reproducibility of the colouration (b) understand and minimise the discolouration process to improve prosthetics longevity. A maxillofacial elastomer type A was intrinsically pigmented according to the six tones of the Fitzpatrick Scale. A pigmentation technique was developed from spectroscopy outcomes of paper printed Fitzpatrick Scale and the primary colors pigments. This platinum reinforced dimethyl methylvinyl siloxane A 223-30 (Factor II, Inc., Lakeside, AZ, USA) presents \"Shore-A\" hardness 30. The samples were exposed to accelerated photo aging, and their degradation mapped with UV/Vis/NIR Spectroscopy. The effects of exclusion of oxygen and inclusion of nanoparticle fillers during prosthetic formulation were investigated. It was concluded that: (i) Morse taper prosthetic connection 3.7 platform implants, should be employed with 45o abutments with 2mm height. The surgical planning shall be for placement 3 hours for the left temporal bone and 9 hours for the right, and in 6 hours (mastoid). Morse taper prosthetic connection 4.0 platform implants behaved biomechanically desirably with 20o abutments in 2mm or 4mm heights. For the Morse taper connection 4.0 platform implants, their placement shall be in 6-9-12 hours, for the right temporal, and 12-3-6, for the left temporal, in order to achieve proper biomechanical behaviour. The Brånemark extraoral external hexagon implant, with 4.1 platform (normal or expanded) should preferrably be used with 20o abutments. (ii) The SiO2 5% may be added to the silicone preparation in order to slow maxillofacial prosthesis colour fading. The TiO2 brings visually significant changes and may be considered as an opacifier.

Submitted by Hellen Luz (hellencrisluz@gmail.com) on 2017-08-02T18:05:21Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_DiscriminacaoEspeciesPeixes.pdf: 1850111 bytes, checksum: f9f85f1c66300e60fbd187c469db81b6 (MD5) / Approved for entry into archive by Irvana Coutinho (irvana@ufpa.br) on 2017-08-04T16:47:02Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_DiscriminacaoEspeciesPeixes.pdf: 1850111 bytes, checksum: f9f85f1c66300e60fbd187c469db81b6 (MD5) / Made available in DSpace on 2017-08-04T16:47:02Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_DiscriminacaoEspeciesPeixes.pdf: 1850111 bytes, checksum: f9f85f1c66300e60fbd187c469db81b6 (MD5) Previous issue date: 2014-03-28 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A espectroscopia vibracional no infravermelho próximo (NIR) tem muitas aplicações na indústria, particularmente no controle da qualidade, discriminação e determinação da composição química em diversos alimentos, devido à rapidez e facilidade de aplicações em análises de rotina em relação aos métodos físicos e químicos tradicionais. Este trabalho tem como objetivo utilizar esta técnica para discriminar espécies de peixes oriundos da costa amazônica (Genyatremus luteus, Lutjanus purpureus, Macrodon ancylodon, Cynoscion acoupa, Micropogonias furnieri) e predizer componentes majoritários (teor de água, lipídios e proteínas). Foi utilizado um espectrômetro portátil de baixa resolução (1600-2400 nm) para obtenção de medidas espectrais em amostras (filés) intactas, trituradas e liofilizadas. Os espectros foram pré-processados através de derivadas utilizando-se o algoritmo de Savitzky-Golay para suavização de ruídos espectrais, tanto para o emprego dos modelos de predição como para os de discriminação. Os modelos de PCA e SIMCA foram aplicados para a discriminação de espécies, enquanto que PCR e PLS foram utilizados na predição dos componentes. Utilizando PCA observou-se a formação de grupos referentes às espécies de G. luteus e L. purpureus em amostras intactas e liofilizadas, e quando se aplicou o SIMCA, observou-se a formação de grupos referente às cinco espécies, confirmados através da distancia entre os grupos na faixa de 4,16 a 13,31 e 2,90 a 57,05 para amostras intactas e liofilizadas, respectivamente. O modelo de regressão PLS apresentou uma pequena variação positiva para os valores r2 e R2, sendo que as amostras liofilizadas apresentaram melhores resultados para os três parâmetros estudados: umidade (r2: 0,58, RMSEPcv: 1,11 e RDP: 1,40), lipídios (r2: 0,96, RMSEPcv: 1,09 e RDP: 4,82), e proteína bruta (r2: 0,86, RMSEPcv: 1,96 e RDP: 2,60). Foi obtida uma boa correlação entre os dados obtidos por métodos AOAC e aqueles preditos pela regressão PLS. / Vibrational spectroscopy in the near infrared (NIR) has many applications in industry, particularly in quality control, discrimination and determination of the chemical composition of different foods because of the speed and ease of application in routine analysis when compared to traditional physical and chemical methods. This work aims to use this technique to discriminate five fish species from the Amazonian coast (Genyatremus luteus , Lutjanus purpureus , Macrodon ancylodon , Cynoscion acoupa, Micropogonias furnieri) and predict major components (water content, lipids and proteins). A portable low-resolution spectrometer (1600-2400 nm) was used for obtaining spectral measures of fish samples (intact, crushed and lyophilized muscle tissue). The spectra were preprocessed by means of derivatives using the Savitzky-Golay algorithm for smoothing of spectral noises, when constructing both prediction and discrimination models. The PCA and SIMCA models were applied for species discrimination, while PCR and PLS were used in the prediction of the major components. Using PCA, it was observed the formation of well-defined groups of the species G. luteus and L. purpureus in intact and lyophilized samples. With SIMCA, it was observed the formation of groups of the five species, which were confirmed by the distance between groups in the range from 4.16 to 13.31 and 2.90 to 57.05 for intact and lyophilized samples, respectively. The PLS regression model showed a small positive variation values for r2 and R2 , and the lyophilized samples showed better results for the three studied parameters: water content ( r2: 0.58 , RMSEPcv: 1.11 and RDP: 1.40 ), lipids (r2 : 0.96 , RMSEPcv : RDP 1.09 and 4.82 ), and crude protein (r2: 0.86 , RMSEPcv : RDP and 1.96 : 2.60). A good correlation between proximate composition data and those predicted by PLS regression was obtained.