Micro-Raman spectroscopy and SEM/EDX applied to improve the zircon fission track method used for dating geological formations

The zircon mineral is widely studied in geochronology. In the case of the fission track method (FTM), the age is determined by the density of fission tracks at the zircon surface, which can be observed with an optical microscope after an appropriate chemical treatment (etching). The etching must be isotropic at the zircon grain surface to be used in the FTM, which leads those zircon grains whose etching is anisotropic to be discarded. The only reason for this discarding is the nonuniform morphology of the surface grain seen by optical microscopy, that is, no further physicochemical analysis is performed. In this work, combining micro-Raman and scanning electron microscopy (SEM) to study the etching anisotropy, it was shown that zircon grains that present at least one area at the surface where the density of fission track is uniform can be used in the FTM. The micro-Raman showed characteristic spectra of the standard zircon sample either from the areas where there are tracks or from where there are not. The only difference found was in the Raman bandwidths, which were broader for the areas with higher density of fission tracks. This suggests simply a decrease in the relative percentage of the crystalline/amorphous phases at these areas. The SEM/energy dispersive spectrometry (EDX) showed that there were no significant differences in the principal chemical composition at the areas with and without fission tracks. Copyright (c) 2008 John Wiley & Sons, Ltd.

Analysis of depth of the microporosity in a nickel-chromium system alloy: effects of electrolytic, chemical and sandblasting etching

The present study was designed to analyse the average depth of the microporosity of a nickel-chromium (Ni-Cr) system alloy (Verabond II). The metal surface was subject to one of the following surface treatment: (i) Electrolytic etching in nitric acid 0.5 N at a current density of 250 mA cm(-2) ; (ii) chemical etching with CG-Etch etchant; and (iii) Sandblasting with alumina particles 50 mum. Half of the samples were polished before the surface treatments. The depth of porosity was measured through photomicrographs (500x) with a profilometer, and the data were statistically analysed using an analysis of variance (anova) followed by Tukey's test. The conclusions were (i) Differents surface treatment of the Ni-Cr system alloy lead to different depths of microporosity; (ii) the greatest depth of porosity was observed in non-polished alloy; (iii) the greatest and identical depth of microporosity was observed following electrolytic etching and chemical etching; (iv) the least and identical depth of microporosity was observed with chemical etching and sandblasting with alumina particles 50 mum, and (v) Chemical etching showed an intermediary depth.

Profile simulations of gas chopping etching processes : model development and comparison with experiments

The progress in microsystem technology or nano technology places extended requirements to the fabrication processes. The trend is moving towards structuring within the nanometer scale on the one hand, and towards fabrication of structures with high aspect ratio (ratio of vertical vs. lateral dimensions) and large depths in the 100 µm scale on the other hand. Current procedures for the microstructuring of silicon are wet chemical etching and dry or plasma etching. A modern plasma etching technique for the structuring of silicon is the so-called "gas chopping" etching technique (also called "time-multiplexed etching"). In this etching technique, passivation cycles, which prevent lateral underetching of sidewalls, and etching cycles, which etch preferably in the vertical direction because of the sidewall passivation, are constantly alternated during the complete etching process. To do this, a CHF3/CH4 plasma, which generates CF monomeres is employed during the passivation cycle, and a SF6/Ar, which generates fluorine radicals and ions plasma is employed during the etching cycle. Depending on the requirements on the etched profile, the durations of the individual passivation and etching cycles are in the range of a few seconds up to several minutes. The profiles achieved with this etching process crucially depend on the flow of reactants, i.e. CF monomeres during the passivation cycle, and ions and fluorine radicals during the etching cycle, to the bottom of the profile, especially for profiles with high aspect ratio. With regard to the predictability of the etching processes, knowledge of the fundamental effects taking place during a gas chopping etching process, and their impact onto the resulting profile is required. For this purpose in the context of this work, a model for the description of the profile evolution of such etching processes is proposed, which considers the reactions (etching or deposition) at the sample surface on a phenomenological basis. Furthermore, the reactant transport inside the etching trench is modelled, based on angular distribution functions and on absorption probabilities at the sidewalls and bottom of the trench. A comparison of the simulated profiles with corresponding experimental profiles reveals that the proposed model reproduces the experimental profiles, if the angular distribution functions and absorption probabilities employed in the model is in agreement with data found in the literature. Therefor the model developed in the context of this work is an adequate description of the effects taking place during a gas chopping plasma etching process.

Control of retention and fatigue-free characteristics in CaBi4Ti4O15 thin films prepared by chemical method

Ferroelectric CaBi4Ti4O15 (CBTi144) thin films were deposited on Pt/Ti/SiO2/Si substrates by the polymeric precursor method. The films present a single phase of layered-structured perovskite with polar axis orientation after annealing at 700 degrees C for 2 h in static air and oxygen atmosphere. The a/b-axis orientation of the ferroelectric film is considered to be associated with the preferred orientation of the Pt bottom electrode. It is noted that the films annealed in static air showed good polarization fatigue characteristics at least up to 10(10) bipolar pulse cycles and excellent retention properties up to 10(4) s. on the other hand, oxygen atmosphere seems to be crucial in the decrease of both, fatigue and retention characteristics of the capacitors. Independently of the applied electric field, the retained switchable polarization approached a nearly steady-state value after a retention time of 10 s. (C) 2006 Elsevier B.V. All rights reserved.

Raman Spectroscopy and Scanning Electron Microscopy Characterizations of Fission Track Method Datable Zircon Grains

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Time-lapse and probabilistic inversion strategies for plane-wave electromagnetic data.

L'utilisation efficace des systèmes géothermaux, la séquestration du CO2 pour limiter le changement climatique et la prévention de l'intrusion d'eau salée dans les aquifères costaux ne sont que quelques exemples qui démontrent notre besoin en technologies nouvelles pour suivre l'évolution des processus souterrains à partir de la surface. Un défi majeur est d'assurer la caractérisation et l'optimisation des performances de ces technologies à différentes échelles spatiales et temporelles. Les méthodes électromagnétiques (EM) d'ondes planes sont sensibles à la conductivité électrique du sous-sol et, par conséquent, à la conductivité électrique des fluides saturant la roche, à la présence de fractures connectées, à la température et aux matériaux géologiques. Ces méthodes sont régies par des équations valides sur de larges gammes de fréquences, permettant détudier de manières analogues des processus allant de quelques mètres sous la surface jusqu'à plusieurs kilomètres de profondeur. Néanmoins, ces méthodes sont soumises à une perte de résolution avec la profondeur à cause des propriétés diffusives du champ électromagnétique. Pour cette raison, l'estimation des modèles du sous-sol par ces méthodes doit prendre en compte des informations a priori afin de contraindre les modèles autant que possible et de permettre la quantification des incertitudes de ces modèles de façon appropriée. Dans la présente thèse, je développe des approches permettant la caractérisation statique et dynamique du sous-sol à l'aide d'ondes EM planes. Dans une première partie, je présente une approche déterministe permettant de réaliser des inversions répétées dans le temps (time-lapse) de données d'ondes EM planes en deux dimensions. Cette stratégie est basée sur l'incorporation dans l'algorithme d'informations a priori en fonction des changements du modèle de conductivité électrique attendus. Ceci est réalisé en intégrant une régularisation stochastique et des contraintes flexibles par rapport à la gamme des changements attendus en utilisant les multiplicateurs de Lagrange. J'utilise des normes différentes de la norme l2 pour contraindre la structure du modèle et obtenir des transitions abruptes entre les régions du model qui subissent des changements dans le temps et celles qui n'en subissent pas. Aussi, j'incorpore une stratégie afin d'éliminer les erreurs systématiques de données time-lapse. Ce travail a mis en évidence l'amélioration de la caractérisation des changements temporels par rapport aux approches classiques qui réalisent des inversions indépendantes à chaque pas de temps et comparent les modèles. Dans la seconde partie de cette thèse, j'adopte un formalisme bayésien et je teste la possibilité de quantifier les incertitudes sur les paramètres du modèle dans l'inversion d'ondes EM planes. Pour ce faire, je présente une stratégie d'inversion probabiliste basée sur des pixels à deux dimensions pour des inversions de données d'ondes EM planes et de tomographies de résistivité électrique (ERT) séparées et jointes. Je compare les incertitudes des paramètres du modèle en considérant différents types d'information a priori sur la structure du modèle et différentes fonctions de vraisemblance pour décrire les erreurs sur les données. Les résultats indiquent que la régularisation du modèle est nécessaire lorsqu'on a à faire à un large nombre de paramètres car cela permet d'accélérer la convergence des chaînes et d'obtenir des modèles plus réalistes. Cependent, ces contraintes mènent à des incertitudes d'estimations plus faibles, ce qui implique des distributions a posteriori qui ne contiennent pas le vrai modèledans les régions ou` la méthode présente une sensibilité limitée. Cette situation peut être améliorée en combinant des méthodes d'ondes EM planes avec d'autres méthodes complémentaires telles que l'ERT. De plus, je montre que le poids de régularisation des paramètres et l'écart-type des erreurs sur les données peuvent être retrouvés par une inversion probabiliste. Finalement, j'évalue la possibilité de caractériser une distribution tridimensionnelle d'un panache de traceur salin injecté dans le sous-sol en réalisant une inversion probabiliste time-lapse tridimensionnelle d'ondes EM planes. Etant donné que les inversions probabilistes sont très coûteuses en temps de calcul lorsque l'espace des paramètres présente une grande dimension, je propose une stratégie de réduction du modèle ou` les coefficients de décomposition des moments de Legendre du panache de traceur injecté ainsi que sa position sont estimés. Pour ce faire, un modèle de résistivité de base est nécessaire. Il peut être obtenu avant l'expérience time-lapse. Un test synthétique montre que la méthodologie marche bien quand le modèle de résistivité de base est caractérisé correctement. Cette méthodologie est aussi appliquée à un test de trac¸age par injection d'une solution saline et d'acides réalisé dans un système géothermal en Australie, puis comparée à une inversion time-lapse tridimensionnelle réalisée selon une approche déterministe. L'inversion probabiliste permet de mieux contraindre le panache du traceur salin gr^ace à la grande quantité d'informations a priori incluse dans l'algorithme. Néanmoins, les changements de conductivités nécessaires pour expliquer les changements observés dans les données sont plus grands que ce qu'expliquent notre connaissance actuelle des phénomenès physiques. Ce problème peut être lié à la qualité limitée du modèle de résistivité de base utilisé, indiquant ainsi que des efforts plus grands devront être fournis dans le futur pour obtenir des modèles de base de bonne qualité avant de réaliser des expériences dynamiques. Les études décrites dans cette thèse montrent que les méthodes d'ondes EM planes sont très utiles pour caractériser et suivre les variations temporelles du sous-sol sur de larges échelles. Les présentes approches améliorent l'évaluation des modèles obtenus, autant en termes d'incorporation d'informations a priori, qu'en termes de quantification d'incertitudes a posteriori. De plus, les stratégies développées peuvent être appliquées à d'autres méthodes géophysiques, et offrent une grande flexibilité pour l'incorporation d'informations additionnelles lorsqu'elles sont disponibles. -- The efficient use of geothermal systems, the sequestration of CO2 to mitigate climate change, and the prevention of seawater intrusion in coastal aquifers are only some examples that demonstrate the need for novel technologies to monitor subsurface processes from the surface. A main challenge is to assure optimal performance of such technologies at different temporal and spatial scales. Plane-wave electromagnetic (EM) methods are sensitive to subsurface electrical conductivity and consequently to fluid conductivity, fracture connectivity, temperature, and rock mineralogy. These methods have governing equations that are the same over a large range of frequencies, thus allowing to study in an analogous manner processes on scales ranging from few meters close to the surface down to several hundreds of kilometers depth. Unfortunately, they suffer from a significant resolution loss with depth due to the diffusive nature of the electromagnetic fields. Therefore, estimations of subsurface models that use these methods should incorporate a priori information to better constrain the models, and provide appropriate measures of model uncertainty. During my thesis, I have developed approaches to improve the static and dynamic characterization of the subsurface with plane-wave EM methods. In the first part of this thesis, I present a two-dimensional deterministic approach to perform time-lapse inversion of plane-wave EM data. The strategy is based on the incorporation of prior information into the inversion algorithm regarding the expected temporal changes in electrical conductivity. This is done by incorporating a flexible stochastic regularization and constraints regarding the expected ranges of the changes by using Lagrange multipliers. I use non-l2 norms to penalize the model update in order to obtain sharp transitions between regions that experience temporal changes and regions that do not. I also incorporate a time-lapse differencing strategy to remove systematic errors in the time-lapse inversion. This work presents improvements in the characterization of temporal changes with respect to the classical approach of performing separate inversions and computing differences between the models. In the second part of this thesis, I adopt a Bayesian framework and use Markov chain Monte Carlo (MCMC) simulations to quantify model parameter uncertainty in plane-wave EM inversion. For this purpose, I present a two-dimensional pixel-based probabilistic inversion strategy for separate and joint inversions of plane-wave EM and electrical resistivity tomography (ERT) data. I compare the uncertainties of the model parameters when considering different types of prior information on the model structure and different likelihood functions to describe the data errors. The results indicate that model regularization is necessary when dealing with a large number of model parameters because it helps to accelerate the convergence of the chains and leads to more realistic models. These constraints also lead to smaller uncertainty estimates, which imply posterior distributions that do not include the true underlying model in regions where the method has limited sensitivity. This situation can be improved by combining planewave EM methods with complimentary geophysical methods such as ERT. In addition, I show that an appropriate regularization weight and the standard deviation of the data errors can be retrieved by the MCMC inversion. Finally, I evaluate the possibility of characterizing the three-dimensional distribution of an injected water plume by performing three-dimensional time-lapse MCMC inversion of planewave EM data. Since MCMC inversion involves a significant computational burden in high parameter dimensions, I propose a model reduction strategy where the coefficients of a Legendre moment decomposition of the injected water plume and its location are estimated. For this purpose, a base resistivity model is needed which is obtained prior to the time-lapse experiment. A synthetic test shows that the methodology works well when the base resistivity model is correctly characterized. The methodology is also applied to an injection experiment performed in a geothermal system in Australia, and compared to a three-dimensional time-lapse inversion performed within a deterministic framework. The MCMC inversion better constrains the water plumes due to the larger amount of prior information that is included in the algorithm. The conductivity changes needed to explain the time-lapse data are much larger than what is physically possible based on present day understandings. This issue may be related to the base resistivity model used, therefore indicating that more efforts should be given to obtain high-quality base models prior to dynamic experiments. The studies described herein give clear evidence that plane-wave EM methods are useful to characterize and monitor the subsurface at a wide range of scales. The presented approaches contribute to an improved appraisal of the obtained models, both in terms of the incorporation of prior information in the algorithms and the posterior uncertainty quantification. In addition, the developed strategies can be applied to other geophysical methods, and offer great flexibility to incorporate additional information when available.

Chemical stability of enalapril maleate drug substance and tablets by a stability-indicating liquid chromatographic method

The chemical stability of enalapril drug substance and tablets was studied by a stability-indicating liquid chromatographic method. Stress testing was performed on drug substance under various conditions. Accelerated stability testing was carried out for different formulations of enalapril tablets. Chromatographic separation was achieved on a RP-18 column, using a mobile phase of methanol phosphate buffer at 1.0 mL min"1 and UV detection. Degradation of the drug substance was greater under hydrolytic conditions. After 180 days of accelerated stability testing most enalapril tablets showed more than 10% of degradation. Enalapril drug substance and tablets showed instability under stress and accelerated testing respectively, with possible implications on the therapeutic activity.

Understanding the in vitro dissolution rate of glasses with respect to future clinical applications

Glass is a unique material with a long history. Several glass products are used daily in our everyday life, often unnoticed. Glass can be found not only in obvious applications such as tableware, windows, and light bulbs, but also in tennis rackets, windmill turbine blades, optical devices, and medical implants. The glasses used at present as implants are inorganic silica-based melt-derived compositions mainly for hard-tissue repair as bone graft substitute in dentistry and orthopedics. The degree of glass reactivity desired varies according to implantation situation and it is vital that the ion release from any glasses used in medical applications is controlled. Understanding the in vitro dissolution rate of glasses provides a first approximation of their behavior in vivo. Specific studies concerning dissolution properties of bioactive glasses have been relatively scarce and mostly concentrated to static condition studies. The motivation behind this work was to develop a simple and accurate method for quantifying the in vitro dissolution rate of highly different types of glass compositions with interest for future clinical applications. By combining information from various experimental conditions, a better knowledge of glass dissolution and the suitability of different glasses for different medical applications can be obtained. Thus, two traditional and one novel approach were utilized in this thesis to study glass dissolution. The chemical durability of silicate glasses was tested in water and TRIS-buffered solution at static and dynamic conditions. The traditional in vitro testing with a TRISbuffered solution under static conditions works well with bioactive or with readily dissolving glasses, and it is easy to follow the ion dissolution reactions. However, in the buffered solution no marked differences between the more durable glasses were observed. The hydrolytic resistance of the glasses was studied using the standard procedure ISO 719. The relative scale given by the standard failed to provide any relevant information when bioactive glasses were studied. However, the clear differences in the hydrolytic resistance values imply that the method could be used as a rapid test to get an overall idea of the biodegradability of glasses. The standard method combined with the ion concentration and pH measurements gives a better estimate of the hydrolytic resistance because of the high silicon amount released from a glass. A sensitive on-line analysis method utilizing inductively coupled plasma optical emission spectrometer and a flow-through micro-volume pH electrode was developed to study the initial dissolution of biocompatible glasses. This approach was found suitable for compositions within a large range of chemical durability. With this approach, the initial dissolution of all ions could be measured simultaneously and quantitatively, which gave a good overall idea of the initial dissolution rates for the individual ions and the dissolution mechanism. These types of results with glass dissolution were presented for the first time during the course of writing this thesis. Based on the initial dissolution patterns obtained with the novel approach using TRIS, the experimental glasses could be divided into four distinct categories. The initial dissolution patterns of glasses correlated well with the anticipated bioactivity. Moreover, the normalized surface-specific mass loss rates and the different in vivo models and the actual in vivo data correlated well. The results suggest that this type of approach can be used for prescreening the suitability of novel glass compositions for future clinical applications. Furthermore, the results shed light on the possible bioactivity of glasses. An additional goal in this thesis was to gain insight into the phase changes occurring during various heat treatments of glasses with three selected compositions. Engineering-type T-T-T curves for glasses 1-98 and 13-93 were stablished. The information gained is essential in manufacturing amorphous porous implants or for drawing of continuous fibers of the glasses. Although both glasses can be hot worked to amorphous products at carefully controlled conditions, 1-98 showed one magnitude greater nucleation and crystal growth rate than 13-93. Thus, 13-93 is better suited than 1-98 for working processes which require long residence times at high temperatures. It was also shown that amorphous and partially crystalline porous implants can be sintered from bioactive glass S53P4. Surface crystallization of S53P4, forming Na2O∙CaO∙2SiO2, was observed to start at 650°C. The secondary crystals of Na2Ca4(PO4)2SiO4, reported for the first time in this thesis, were detected at higher temperatures, from 850°C to 1000°C. The crystal phases formed affected the dissolution behavior of the implants in simulated body fluid. This study opens up new possibilities for using S53P4 to manufacture various structures, while tailoring their bioactivity by controlling the proportions of the different phases. The results obtained in this thesis give valuable additional information and tools to the state of the art for designing glasses with respect to future clinical applications. With the knowledge gained we can identify different dissolution patters and use this information to improve the tuning of glass compositions. In addition, the novel online analysis approach provides an excellent opportunity to further enhance our knowledge of glass behavior in simulated body conditions.

An in vitro analysis of the total phenolic content, antioxidant power, physical, physicochemical, and chemical composition of Terminalia Catappa Linn fruits

This study assessed the antioxidant, total phenolic, and physicochemical properties of in vitro Terminalia Catappa Linn (locally called castanhola) using the DPPH assay. The castanhola fruits had an average weight of 19.60 ± 0.00 g, combining shell, pulp, and seed weight, and a soluble solids content of 8 °Brix. The chemical composition was determined with predominance of carbohydrates (76,88 ± 0,58%).The titration method was used to determine Vitamin C content using 2,6-dichlorophenolindophenol (DCFI), known as reactive Tillmans resulting in no significant levels. Aqueous extracts of castanhola pulp showed a higher concentration of phenolics, 244.33 ± 18.86 GAE.g-1 of fruit, and alcoholic extracts, 142.84 ± 2.09 GAE.g-1 of fruit. EC50 values of the aqueous extract showed a greater ability to scavenge free radicals than the alcoholic extracts. The fruit had a significant content of phenolic compounds and high antioxidant capacity.

The Kineticist's Workbench: Combining Symbolic and Numerical Methods in the Simulation of Chemical Reaction Mechanisms

The Kineticist's Workbench is a program that simulates chemical reaction mechanisms by predicting, generating, and interpreting numerical data. Prior to simulation, it analyzes a given mechanism to predict that mechanism's behavior; it then simulates the mechanism numerically; and afterward, it interprets and summarizes the data it has generated. In performing these tasks, the Workbench uses a variety of techniques: graph- theoretic algorithms (for analyzing mechanisms), traditional numerical simulation methods, and algorithms that examine simulation results and reinterpret them in qualitative terms. The Workbench thus serves as a prototype for a new class of scientific computational tools---tools that provide symbiotic collaborations between qualitative and quantitative methods.

A holistic method to assess building energy efficiency combining D-S theory and the evidential reasoning approach

The assessment of building energy efficiency is one of the most effective measures for reducing building energy consumption. This paper proposes a holistic method (HMEEB) for assessing and certifying building energy efficiency based on the D-S (Dempster-Shafer) theory of evidence and the Evidential Reasoning (ER) approach. HMEEB has three main features: (i) it provides both a method to assess and certify building energy efficiency, and exists as an analytical tool to identify improvement opportunities; (ii) it combines a wealth of information on building energy efficiency assessment, including identification of indicators and a weighting mechanism; and (iii) it provides a method to identify and deal with inherent uncertainties within the assessment procedure. This paper demonstrates the robustness, flexibility and effectiveness of the proposed method, using two examples to assess the energy efficiency of two residential buildings, both located in the ‘Hot Summer and Cold Winter’ zone in China. The proposed certification method provides detailed recommendations for policymakers in the context of carbon emission reduction targets and promoting energy efficiency in the built environment. The method is transferable to other countries and regions, using an indicator weighting system to modify local climatic, economic and social factors.

A rapid and reliable bonding process for microchip electrophoresis fabricated in glass substrates

In this report, we describe a rapid and reliable process to bond channels fabricated in glass substrates. Glass channels were fabricated by photolithography and wet chemical etching. The resulting channels were bonded against another glass plate containing a 50-mu m thick PDMS layer. This same PDMS layer was also used to provide the electrical insulation of planar electrodes to carry out capacitively coupled contactless conductivity detection. The analytical performance of the proposed device was shown by using both LIF and capacitively coupled contactless conductivity detection systems. Efficiency around 47 000 plates/m was achieved with good chip-to-chip repeatability and satisfactory long-term stability of EOF. The RSD for the EOF measured in three different devices was ca. 7%. For a chip-to-chip comparison, the RSD values for migration time, electrophoretic current and peak area were below 10%. With the proposed approach, a single chip can be fabricated in less than 30 min including patterning, etching and sealing steps. This fabrication process is faster and easier than the thermal bonding process. Besides, the proposed method does not require high temperatures and provides excellent day-to-day and device-to-device repeatability.

Uranium-lead dating method at the Pará-Iso isotope geology laboratory, UFPA, Belém – Brazil

ABSTRACT: Analytical procedures for U-Pb isotope dilution analyses at the Pará-Iso isotope geology laboratory of the Federal University of Pará (UFPA) are described in detail. The procedures are applied to zircon, titanite, rutile, apatite, columbite-tantalite and whole rock. Reagent preparation and chemical processing are done in clean-room conditions. Samples are dissolved using Teflon™ microcapsules in steel jacket Teflon™ Parr Instrument™ bomb or Teflon™ screw cap containers. U and Pb are separated using anion exchange AG 1x8 resin columns. Typical blanks for mineral sample amounts of 0.01 to 1.0 mg are less than 1 pg U and 20-30 pg Pb. Isotope analysis of the U and Pb from the same filament are carried out using a Finnigan MAT 262 mass-spectrometer in static and dynamic modes. The current analytical level is demonstrated on analyses of international standard zircon 91500 with three different ²³⁵U-²⁰⁵Pb and ²³⁵U-²⁰⁸Pb isotope tracers and whole rock standards. Results of analyses of two zircon samples are also presented.