Noble metal nanoparticles such as gold and silver are of interest because of the unique electro-optical properties (e.g., localized surface plasmon resonance [LSPR]) that originate from the collective behavior of their surface electrons. These nanoparticles are commonly developed and used for biomedical and industrial application. A recent report has predicted that the global market for gold nanoparticles will be over 12.7 tons by year 2020. However, these surface-functionalized nanoparticles can be potential environmental persistent contaminants post-use due to their high colloidal stability in the aquatic systems. Despite, the environmental risks associated with these nanoparticles, just a few studies have investigated the effect of nanofeature factors such as size and shape on the overall fate/transport and organismal uptake of these nanomaterials in the aquatic matrices. This study presents a comprehensive approach to evaluate the colloidal stability, fate/transport, and organismal uptake of these nanoparticles while factoring in the size and shape related properties. We demonstrate the importance and effect of anisotropicity of a gold nanoparticle on the colloidal behavior and interaction with ecologically susceptible aquatic biota. We also show how readily available characterization techniques can be utilized to monitor and assess the fate/transport of this class of nanoparticles. We further describe and investigate the relationship between the aspect ratio (AR) of these elongated gold nanoparticles with clearance mechanisms and rates from the aquatic suspension columns including aggregation, deposition, and biopurification. We illustrate how a fresh water filter-feeder bivalve, Corbicula fluminea, can be used as a model organism to study the size and shape-selective biofiltration and nanotoxicity of elongated gold nanoparticles. The results suggest that biofiltration by C. fluminea increases with an increase in the size and AR of gold nanoparticle. We develop a simple nanotoxicity assay to investigate the short-term exposure nanotoxicity of gold nanoparticles to C. fluminea. The toxicity results indicate that for the tested concentration and exposure period that gold nanoparticles were not acutely toxic (i.e., not lethal). However, gold nanoparticles significantly inhibited the activities of some antioxidant enzymes in gill and digestive gland tissues. These inhibitions could directly affect the resistance of these organisms to a secondary stressor (temperature, pathogens, hypoxia etc.) and threaten organismal health. / Ph. D. / Nanoparticles are fine particles that cannot be seen with naked eye and possess unique chemical and physical properties. Gold and silver nanoparticles are specifically of interest due to tunable optical properties and are commonly developed and used for biomedical and industrial applications. Unfortunately, these metallic nanoparticles can be potential environmental persistent contaminants post-use in the soil and aquatic systems. Despite, the environmental risks associated with these metallic nanoparticles, just a few studies have investigated the effect of size and shape of these nanoparticles on their interaction and transportation in the surrounding environment and with existing organisms. This study presents a comprehensive approach to evaluate the stability, transportation, and organismal uptake of these nanoparticles while factoring in the size and shape related properties. We also show how readily available detection techniques can be utilized to monitor and assess the presence and transport of this class of nanoparticles. We illustrate how a fresh water bivalve, Corbicula fluminea, can be used as a model organism to study the size and shape-selective uptake and toxicity of gold nanoparticles. The results suggest that nanoparticles uptake by C. fluminea increases with an increase in the size of gold nanoparticle. We develop a simple toxicity assay to investigate the short-term exposure toxicity of gold nanoparticles to C. fluminea. The toxicity results suggest that for the tested concentration and exposure period that gold nanoparticles were not acutely toxic (i.e., not lethal) but affect the resistance of these organisms to an environmental change (temperature, pathogens, hypoxia etc.) and threaten organismal health.

This project utilised a novel solvent-free continuous cocrystallisation (SFCC) method to manufacture pharmaceutical co-crystals. The objectives were to optimize the process towards achieving high co-crystal yields and to understand the behaviour of co-crystals under different conditions. Particular attention was paid to the development of near infrared (NIR) spectroscopy as a process analytical technology (PAT). Twin screw, hot melt extrusion was the base technique of the SFCC process. Changing parameters such as temperature, screw speed and screw geometry was important for improving the co-crystal yield. The level of mixing and shear was directly influenced by the screw geometry, whilst the screw speed was an important parameter for controlling the residence time of the material during hot melt extrusion. Ibuprofen – nicotinamide 1:1 cocrystals and carbamazepine – nicotinamide 1:1 co-crystals were successfully manufactured using the SFCC method. Characterisation techniques were important for this project, and NIR spectroscopy proved to be a convenient, accurate analytical technique for identifying the formation of co-crystals along the extruder barrel. Separate thermal and model shear deformation studies were also carried out to determine the effect of temperature and shear on co-crystal formation for several different pharmaceutical co-crystal pairs. Finally, NIR spectroscopy was used to create two partial least squares regression models, for predicting the 1:1 co-crystal yield of ibuprofen – nicotinamide and carbamazepine – nicotinamide, when in a powder mixture with the respective pure API. It is believed that the prediction models created in this project can be used to facilitate future in-line PAT studies of pharmaceutical co-crystals during different manufacturing processes. / Engineering and Physical Sciences Research Council (EPSRC)

Mikroplastik (1–1000 µm) kommt praktisch überall in der Umwelt vor, aber immer noch ist die Iden-tifizierung und Quantifizierung eine anspruchsvolle und zeitintensive Aufgabe. Erste analytisch Metho¬den beginnen sich zu etablieren, jedoch sind die benötigten Instrumente komplex und der Probendurchsatz für Routineuntersuchungen in den meisten Fällen noch zu gering. Diese Arbeit widmet sich zunächst dem Potenzial der Nahinfrarot (NIR)-Spektroskopie diese Lücke zu schließen. Exemplarisch wird ein günstiges Verfahren mit großem Probendurchsatz zur Bestimmung von Mikro¬plastik-Gesamtgehalten der verbreiteten Verpackungskunststoffe Polyethylen (PE), Polystyrol (PS) und Polypropylen (PP) in Böden und Kompost entwickelt. Neben der Untersuchung von Mikroplastik-Gesamtgehalten einer Probe ist auch die Charakterisierung individueller Partikel von großer Bedeutung. Die bildgebende Fourier-Transform-Infrarot (FTIR)-Mikrospektroskopie ist hierfür sehr gut geeignet. Allerdings ist es eine Herausforderung Mikroplastik in den aus mehreren Million Spektren bestehenden hyperspektralen Bildern zu identifizieren. Eine schnelle und zuverlässige Mikroplastikerkennung wird hier durch eine explorative Analyse und automatisierte Klassifizierung der Spektren erreicht. Zusammenfassend zeigt diese Arbeit, dass die optische Spektroskopie im mittleren und nahen Infrarot über ihre bisherige Anwendung hinaus ein großes Potenzial besitzen, die Mikroplastik-Analytik kostengünstiger, einfacher und schneller zu gestalten. / Microplastics (1-1000 µm) are ubiquitous in the environment, but their identification and quantification is still a challenging and time-consuming task. The first established methods require complex instruments and the sample throughput is still too low for routine analysis in most cases. This work first addresses the potential of near-infrared (NIR) spectroscopy to fill this gap. A low-cost method with large sample throughput is developed for the determination of total microplastic contents of the common packaging plastics polyethylene (PE), polystyrene (PS) and polypropylene (PP) in soils and compost. In addition to the investigation of total microplastic levels in a sample, the characterization of individual particles is also of great importance. Fourier transform infrared (FTIR) imaging microspectroscopy is well suited for this purpose. However, it is challenging to identify microplastics in hyperspectral images consisting of several million spectra. Fast and reliable microplastic detection is achieved by exploratory analysis and automated classification of the spectra. In summary, this work shows that mid- and near-infrared optical spectroscopy have great potential beyond their current application to make microplastics analysis cheaper, easier, and faster.

Postmortem interval (PMI) estimation is a necessary but often difficult task that must completed during a death investigation. The level of difficulty rises as time since death increases, especially with the case of skeletonized remains (long PMI). While challenging, a reliable PMI estimate may be of great importance for investigative direction and cost-savings (e.g. suspect identification, tailoring missing persons searches, non-forensic remains exclusion). Long PMI can be estimated by assessing changes in the organic content of bone (i.e. collagen), which degrades and is lost as the PMI lengthens. Visible-near infrared (VNIR) spectroscopy is one method that can be used for analyzing organic constituents, including proteins, in solid specimens. A 2013 preliminary investigation using a limited number of human cortical bone samples suggested that VNIR spectroscopy could provide a fast, reliable technique for assessing PMI in human skeletal remains. Clear separation was noted between "forensic" and "archaeological" specimen spectra within the near-infrared (NIR) bands. The goal of this research was to develop reliable multivariate classification models that could assign skeletal remains to appropriate PMI classes (e.g. "forensic" and "non-forensic"), based on NIR spectra collected from human cortical bone. Working with a large set of cortical samples (n=341), absorbance spectra were collected with an ASD/PANalytical LabSpec® 4 full range spectrometer. Sample spectra were then randomly assigned to training and test sets, where training set spectra were used to build internally cross-validated models in Camo Unscrambler® X 10.4; external validations of the models were then performed on test set spectra. Selected model algorithms included soft independent modeling of class analogy (SIMCA), linear discriminant analysis on principal components (LDA-PCA), and partial least squares discriminant analysis (PLSDA); an application of support vector machines on principal components (SVM-PCA) was attempted as well. Multivariate classification models were built using both raw and transformed spectra (standard normal variate, Savitzky-Golay) that were collected from the longitudinally cut cortical surfaces (Set A models) and the superficial cortical surface following light grinding (Set B models). SIMCA models were consistently the poorest performers, as were many of the SVM-PCA models; LDA-PCA models were generally the best performers for these data. Transformed-spectra model classification accuracies were generally the same or lower than corresponding raw spectral models. Set A models out-performed Set B counterparts in most cases; Set B models often yielded lower classification accuracy for older forensic and non-forensic spectra. A limited number of Set B transformed-spectra models out-performed the raw model counterparts, suggesting that these transformations may be removing scattering-related noise, leading to improvements in model accuracy. This study suggests that NIR spectroscopy may represent a reliable technique for assessing the PMI of unknown human skeletal remains. Future work will require identifying new sources of remains with established extended PMI values. Broadening the number of spectra collected from older forensic samples would allow for the determination of how many narrower potential PMI classes can be discriminated within the forensic time-frame.

Neste trabalho estudou-se o processo de polimerização de estireno em miniemulsão, através do monitoramento in-line da conversão do monômero e do tamanho das partículas geradas durante o processo de polimerização, através das técnicas espectroscópicas de Infravermelho Próximo (Near Infra Red - NIR) e Raman. As medições off-line de conversão foram feitas através de gravimetria e do tamanho das partículas através de Espalhamento Dinâmico de Luz (Dynamic Light Scattering - DLS). Também foi objeto deste estudo a modelagem matemática do process de polimerização em miniemulsão, assim como sua simulação utilizando o programa Matlab. A metodologia adotada para a obtenção dos resultados envolveu o trabalho experimental de monitoramento da síntese de poliestireno em miniemulsão utilizando iniciador hidrossolúvel (persulfato de potássio), tensoativo (lauril sulfato de sódio) e co-estabilizantes (hexadecano e poliestireno) e equipamento rotor-estator, Ultra Turrax T25, para obtenção da miniemulsão. O modelo matemático envolveu a determinação de equações fenomenológicas representativas do sistema em questão, prevendo as possíveis variações na cinética e fenômenos físico-químicos, decorrentes de variações na formulação prevendo inclusive os mecanismos de nucleação existentes em função da concentração de tensoativo utilizado. Como resultado, este trabalho validou as metodologias avaliadas para monitoramento da conversão e diâmetro das partículas poliméricas e também, ao comparar as metodologias avaliadas, identificou a espectroscopia NIR como metodologia preferencial por não exigir preparação da amostra, fornecer respostas em tempo real, sem defasagem de tempo e também por permitir coletar espectros com pequenos intervalos de tempo, garante melhor precisão e evita a perda de informações do processo. / In this work, is the development of a detailed study of the miniemulsion polymerization process monitoring monomer conversion and particle size along process. Near Infrared Spectroscopy (Near Infra Red - NIR) and Raman Spectroscopy were used to conversion and diameter determination. Gravimetric analyses were used to conversion determination. Dynamic Light Scattering (Dynamic Light Scattering - DLS) to particle size determination. It was also object of this study the Mathematical Modeling of Miniemulsion Polymerization Reaction Kinetic, as well as it simulation using Matlab software. The methodology used to obtain the results involved experimental work to synthesize and monitor miniemulsion polystyrene using water-soluble initiator (potassium persulfate), stabilizer (sodium lauryl sulfate) and co-stabilizers (hexadecane and polystyrene) and rotor-stator equipment Ultra Turrax T25 for miniemulsion obtaining. The mathematical model involved the determination of representative phenomenological equations this system, anticipating the possible variations in kinetics and physical-chemical phenomena, resulting from formulation variations mainly by verifying the surfactant concentration [S] to determine the existing nucleation mechanism when compared with the same surfactant critical micelle concentration [CMC]. The provided mechanisms are: micellar nucleation to [S] [CMC], droplets nucleation and homogeneous nucleation to [S] < [CMC]. As a result, this study validated the proposed methods for monitoring conversion and polymer particles diameter and also compare the evaluated methodologies, identifying NIR spectroscopy as a differential method among others for not to require sample preparation, supply answers in real time, no time delay and also to perform in shorter intervals, preventing the loss of process information.

Neste trabalho estudou-se o processo de polimerização de estireno em miniemulsão, através do monitoramento in-line da conversão do monômero e do tamanho das partículas geradas durante o processo de polimerização, através das técnicas espectroscópicas de Infravermelho Próximo (Near Infra Red - NIR) e Raman. As medições off-line de conversão foram feitas através de gravimetria e do tamanho das partículas através de Espalhamento Dinâmico de Luz (Dynamic Light Scattering - DLS). Também foi objeto deste estudo a modelagem matemática do process de polimerização em miniemulsão, assim como sua simulação utilizando o programa Matlab. A metodologia adotada para a obtenção dos resultados envolveu o trabalho experimental de monitoramento da síntese de poliestireno em miniemulsão utilizando iniciador hidrossolúvel (persulfato de potássio), tensoativo (lauril sulfato de sódio) e co-estabilizantes (hexadecano e poliestireno) e equipamento rotor-estator, Ultra Turrax T25, para obtenção da miniemulsão. O modelo matemático envolveu a determinação de equações fenomenológicas representativas do sistema em questão, prevendo as possíveis variações na cinética e fenômenos físico-químicos, decorrentes de variações na formulação prevendo inclusive os mecanismos de nucleação existentes em função da concentração de tensoativo utilizado. Como resultado, este trabalho validou as metodologias avaliadas para monitoramento da conversão e diâmetro das partículas poliméricas e também, ao comparar as metodologias avaliadas, identificou a espectroscopia NIR como metodologia preferencial por não exigir preparação da amostra, fornecer respostas em tempo real, sem defasagem de tempo e também por permitir coletar espectros com pequenos intervalos de tempo, garante melhor precisão e evita a perda de informações do processo. / In this work, is the development of a detailed study of the miniemulsion polymerization process monitoring monomer conversion and particle size along process. Near Infrared Spectroscopy (Near Infra Red - NIR) and Raman Spectroscopy were used to conversion and diameter determination. Gravimetric analyses were used to conversion determination. Dynamic Light Scattering (Dynamic Light Scattering - DLS) to particle size determination. It was also object of this study the Mathematical Modeling of Miniemulsion Polymerization Reaction Kinetic, as well as it simulation using Matlab software. The methodology used to obtain the results involved experimental work to synthesize and monitor miniemulsion polystyrene using water-soluble initiator (potassium persulfate), stabilizer (sodium lauryl sulfate) and co-stabilizers (hexadecane and polystyrene) and rotor-stator equipment Ultra Turrax T25 for miniemulsion obtaining. The mathematical model involved the determination of representative phenomenological equations this system, anticipating the possible variations in kinetics and physical-chemical phenomena, resulting from formulation variations mainly by verifying the surfactant concentration [S] to determine the existing nucleation mechanism when compared with the same surfactant critical micelle concentration [CMC]. The provided mechanisms are: micellar nucleation to [S] [CMC], droplets nucleation and homogeneous nucleation to [S] < [CMC]. As a result, this study validated the proposed methods for monitoring conversion and polymer particles diameter and also compare the evaluated methodologies, identifying NIR spectroscopy as a differential method among others for not to require sample preparation, supply answers in real time, no time delay and also to perform in shorter intervals, preventing the loss of process information.

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.

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. / 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. / 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.

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<p>To apply Industry 4.0 technologies and accelerate the modernization of continuous pharmaceutical manufacturing, digital twin (DT) and advanced process control (APC) strategies are indispensable. The DT serves as a virtual representation that mirrors the behavior of the physical process system, enabling real-time monitoring and predictive capabilities. Consequently, this facilitates the feasibility of real-time release testing (RTRT) and enhances drug product development and manufacturing efficiency by reducing the need for extensive sampling and testing. Moreover, APC strategies are required to address variations in raw material properties and process uncertainties while ensuring that desired critical quality attributes (CQAs) of in-process materials and final products are maintained. When deviations from quality targets are detected, APC must provide optimal real-time corrective actions, offering better control performance than the traditional open loop-control method. The progress in DT and APC is beneficial in shifting from the paradigm of Quality-by-Test (QbT) to that of Quality-by-Design (QbD) and Quality-by-Control (QbC), which emphasize the importance of process knowledge and real-time information to ensure product quality.</p> <p><br></p> <p>This study focuses on four key elements and their applications in a continuous dry granulation tableting process, including feeding, blending, roll compaction, ribbon milling and tableting unit operations. Firstly, the necessity of a digital infrastructure for data collection and integration is emphasized. An ISA-95-based hierarchical automation framework is implemented for continuous pharmaceutical manufacturing, with each level serving specific purposes related to production, sensing, process control, manufacturing operations, and business planning. Secondly, investigation of process analytical technology (PAT) tools for real-time measurements is highlighted as a prerequisite for effective real-time process management. For instance, the measurement of mass flow rate, a critical process parameter (CPP) in continuous manufacturing, was previously limited to loss-in-weight (LIW) feeders. To overcome this limitation, a novel capacitance-based mass flow sensor, the ECVT sensor, has been integrated into the continuous direct compaction process to capture real-time powder flow rates downstream of the LIW feeders. Additionally, the use of near-infrared (NIR)-based sensor for real-time measurement of ribbon solid fraction in dry granulation processes is explored. Proper spectra selection and pre-processing techniques are employed to transform the spectra into useful real-time information. Thirdly, the development of quantitative models that establish a link between CPPs and CQAs is addressed, enabling effective product design and process control. Mechanistic models and hybrid models are employed to describe the continuous direct compaction (DC) and dry granulation (DG) processes. Finally, applying APC strategies becomes feasible with the aid of real-time measurements and model predictions. Real-time optimization techniques are used to combine measurements and model predictions to infer unmeasured states or mitigate the impact of measurement noise. In this work, the moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework is utilized. It leverages the capabilities of MHE for parameter updates and state estimation to enable adaptive models using data from the past time window. Simultaneously, NMPC ensures satisfactory setpoint tracking and disturbance rejection by minimizing the error between the model predictions and setpoint in the future time window. The MHE-NMPC framework has been implemented in the tableting process and demonstrated satisfactory control performance even when plant model mismatch exists. In addition, the application of MHE enables the sensor fusion framework, where at-line measurements and online measurements can be integrated if the past time window length is sufficient. The sensor fusion framework proves to be beneficial in extending the at-line measurement application from just validation to real-time decision-making.</p>