Electrochemistry of carbon based engineered structures

The main aims of my PhD research work have been the investigation of the redox, photophysical and electronic properties of carbon nanotubes (CNT) and their possible uses as functional substrates for the (electro)catalytic production of oxygen and as molecular connectors for Quantum-dot Molecular Automata. While for CNT many and diverse applications in electronics, in sensors and biosensors field, as a structural reinforcing in composite materials have long been proposed, the study of their properties as individual species has been for long a challenging task. CNT are in fact virtually insoluble in any solvent and, for years, most of the studies has been carried out on bulk samples (bundles). In Chapter 2 an appropriate description of carbon nanotubes is reported, about their production methods and the functionalization strategies for their solubilization. In Chapter 3 an extensive voltammetric and vis-NIR spectroelectrochemical investigation of true solutions of unfunctionalized individual single wall CNT (SWNT) is reported that permitted to determine for the first time the standard electrochemical potentials of reduction and oxidation as a function of the tube diameter of a large number of semiconducting SWNTs. We also established the Fermi energy and the exciton binding energy for individual tubes in solution and, from the linear correlation found between the potentials and the optical transition energies, one to calculate the redox potentials of SWNTs that are insufficiently abundant or absent in the samples. In Chapter 4 we report on very efficient and stable nano-structured, oxygen-evolving anodes (OEA) that were obtained by the assembly of an oxygen evolving polyoxometalate cluster, (a totally inorganic ruthenium catalyst) with a conducting bed of multiwalled carbon nanotubes (MWCNT). Here, MWCNT were effectively used as carrier of the polyoxometallate for the electrocatalytic production of oxygen and turned out to greatly increase both the efficiency and stability of the device avoiding the release of the catalysts. Our bioinspired electrode addresses the major challenge of artificial photosynthesis, i.e. efficient water oxidation, taking us closer to when we might power the planet with carbon-free fuels. In Chapter 5 a study on surface-active chiral bis-ferrocenes conveniently designed in order to act as prototypical units for molecular computing devices is reported. Preliminary electrochemical studies in liquid environment demonstrated the capability of such molecules to enter three indistinguishable oxidation states. Side chains introduction allowed to organize them in the form of self-assembled monolayers (SAM) onto a surface and to study the molecular and redox properties on solid substrates. Electrochemical studies on SAMs of these molecules confirmed their attitude to undergo fast (Nernstian) electron transfer processes generating, in the positive potential region, either the full oxidized Fc+-Fc+ or the partly oxidized Fc+-Fc species. Finally, in Chapter 6 we report on a preliminary electrochemical study of graphene solutions prepared according to an original procedure recently described in the literature. Graphene is the newly-born of carbon nanomaterials and is certainly bound to be among the most promising materials for the next nanoelectronic generation.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Shaking table tests of structures with hysteretic dampers: experimental results vs. prediction using non-linear static methods

The use of seismic hysteretic dampers for passive control is increasing exponentially in recent years for both new and existing buildings. In order to utilize hysteretic dampers within a structural system, it is of paramount importance to have simplified design procedures based upon knowledge gained from theoretical studies and validated with experimental results. Non-linear Static Procedures (NSPs) are presented as an alternative to the force-based methods more common nowadays. The application of NSPs to conventional structures has been well established; yet there is a lack of experimental information on how NSPs apply to systems with hysteretic dampers. In this research, several shaking table tests were conducted on two single bay and single story 1:2 scale structures with and without hysteretic dampers. The maximum response of the structure with dampers in terms of lateral displacement and base shear obtained from the tests was compared with the prediction provided by three well-known NSPs: (1) the improved version of the Capacity Spectrum Method (CSM) from FEMA 440; (2) the improved version of the Displacement Coefficient Method (DCM) from FEMA 440; and (3) the N2 Method implemented in Eurocode 8. In general, the improved version of the DCM and N2 methods are found to provide acceptable accuracy in prediction, but the CSM tends to underestimate the response.

Some relations between visual perception and non-linear photonic structures

The main objective of this work is to present a way to emulate some functions of the mammalian visual system and a model to analyze subjective sensations and visual illusions

CRASH: a computer simulator of non-linear transient response of structures, basis for car crash simulation. Research report.

National Highway Traffic Safety Administration, Washington, D.C.

Design of artificial myocardial microtissues

Cultivation technologies promoting organization of mammalian cells in three dimensions are essential for gene-function analyses as well as drug testing and represent the first step toward the design of tissue replacements and bioartificial organs. Embedded in a three-dimensional environment, cells are expected to develop tissue-like higher order intercellular structures (cell-cell contacts, extracellular matrix) that orchestrate cellular functions including proliferation, differentiation, apoptosis, and angiogenesis with unmatched quality. We have refined the hanging drop cultivation technology to pioneer beating heart microtissues derived from pure primary rat and mouse cardiomyocyte cultures as well as mixed populations reflecting the cell type composition of rodent hearts. Phenotypic characterization combined with detailed analysis of muscle-specific cell traits, extracellular matrix components, as well as endogenous vascular endothelial growth factor (VEGF) expression profiles of heart microtissues revealed (1) a linear cell number-microtissue size correlation, (2) intermicrotissue superstructures, (3) retention of key cardiomyocyte-specific cell qualities, (4) a sophisticated extracellular matrix, and (5) a high degree of self-organization exemplified by the tendency of muscle structures to assemble at the periphery of these myocardial spheroids. Furthermore (6), myocardial spheroids support endogenous VEGF expression in a size-dependent manner that will likely promote vascularization of heart microtissues produced from defined cell mixtures as well as support connection to the host vascular system after implantation. As cardiomyocytes are known to be refractory to current transfection technologies we have designed lentivirus-based transduction strategies to lead the way for genetic engineering of myocardial microtissues in a clinical setting.

System identification of linear vibrating structures

Methods of dynamic modelling and analysis of structures, for example the finite element method, are well developed. However, it is generally agreed that accurate modelling of complex structures is difficult and for critical applications it is necessary to validate or update the theoretical models using data measured from actual structures. The techniques of identifying the parameters of linear dynamic models using Vibration test data have attracted considerable interest recently. However, no method has received a general acceptance due to a number of difficulties. These difficulties are mainly due to (i) Incomplete number of Vibration modes that can be excited and measured, (ii) Incomplete number of coordinates that can be measured, (iii) Inaccuracy in the experimental data (iv) Inaccuracy in the model structure. This thesis reports on a new approach to update the parameters of a finite element model as well as a lumped parameter model with a diagonal mass matrix. The structure and its theoretical model are equally perturbed by adding mass or stiffness and the incomplete number of eigen-data is measured. The parameters are then identified by an iterative updating of the initial estimates, by sensitivity analysis, using eigenvalues or both eigenvalues and eigenvectors of the structure before and after perturbation. It is shown that with a suitable choice of the perturbing coordinates exact parameters can be identified if the data and the model structure are exact. The theoretical basis of the technique is presented. To cope with measurement errors and possible inaccuracies in the model structure, a well known Bayesian approach is used to minimize the least squares difference between the updated and the initial parameters. The eigen-data of the structure with added mass or stiffness is also determined using the frequency response data of the unmodified structure by a structural modification technique. Thus, mass or stiffness do not have to be added physically. The mass-stiffness addition technique is demonstrated by simulation examples and Laboratory experiments on beams and an H-frame.

Synthesis and characterization of some Mn(II) and Mn(III) complexes of N,N′-o-phenylenebis(salicylideneimine)(LH2) and N,N′-o-phenylenebis(5-bromosalicylideneimine)(L′H2). Crystal structures of [Mn(L)(H2O)(ClO4)], [Mn(L)(NCS)] and an infinite linear chain of [Mn(L)(OAc)]

A series of manganese(II) [Mn(L)] and manganese(III) [Mn(L)(X)] (X = ClO4, OAc, NCS, N3, Cl, Br and I) complexes have been synthesized from Schiff base ligands N,N′-o- phenylenebis(salicylideneimine)(LH2) and N,N′-o-phenylenebis(5- bromosalicylideneimine)(L′H2) obtained by condensation of salicylaldehyde or 5-Br salicylaldehyde with o-phenylene-diamine. The complexes have been characterized by the combination of IR, UV-Vis spectroscopy, magnetic measurements and electrochemical studies. Three manganese(III) complexes 3 [Mn(L)(ClO4)(H2O)], 5 [Mn(L)(OAc)] and 13 [Mn(L)(NCS)] have been characterized by X-ray crystallography. The X-ray structures show that the manganese(III) is hexa-coordinated in 3, it is penta-coordinated in 13, while in 5 there is an infinite chain where the MnL moieties are connected by acetate ions acting as bridging bidentate ligand. The cyclic voltammograms of all the manganese(III) complexes exhibit two reversible/quasi-reversible/ irreversible responses assignable to Mn(III)/Mn(II) and Mn(IV)/Mn(III) couples. It was observed that the ligand L′H2 containing the 5-bromosal moiety always stabilizes the lower oxidation states compared to the corresponding unsubstituted LH2. Cyclic voltammograms of the manganese(II) complexes (1 and 2) exhibit a quasi-reversible Mn(III)/Mn(II) couple at E1/2 -0.08 V for 1 and 0.054 V for 2. © 2005 Elsevier B.V. All rights reserved.

RELIBILITY ANALYSIS OF SIMPLE SLOPES AND SOIL-STRUCTURES WITH LINEAR LIMIT STATES

The first objective of this research was to develop closed-form and numerical probabilistic methods of analysis that can be applied to otherwise conventional methods of unreinforced and geosynthetic reinforced slopes and walls. These probabilistic methods explicitly include random variability of soil and reinforcement, spatial variability of the soil, and cross-correlation between soil input parameters on probability of failure. The quantitative impact of simultaneously considering the influence of random and/or spatial variability in soil properties in combination with cross-correlation in soil properties is investigated for the first time in the research literature. Depending on the magnitude of these statistical descriptors, margins of safety based on conventional notions of safety may be very different from margins of safety expressed in terms of probability of failure (or reliability index). The thesis work also shows that intuitive notions of margin of safety using conventional factor of safety and probability of failure can be brought into alignment when cross-correlation between soil properties is considered in a rigorous manner. The second objective of this thesis work was to develop a general closed-form solution to compute the true probability of failure (or reliability index) of a simple linear limit state function with one load term and one resistance term expressed first in general probabilistic terms and then migrated to a LRFD format for the purpose of LRFD calibration. The formulation considers contributions to probability of failure due to model type, uncertainty in bias values, bias dependencies, uncertainty in estimates of nominal values for correlated and uncorrelated load and resistance terms, and average margin of safety expressed as the operational factor of safety (OFS). Bias is defined as the ratio of measured to predicted value. Parametric analyses were carried out to show that ignoring possible correlations between random variables can lead to conservative (safe) values of resistance factor in some cases and in other cases to non-conservative (unsafe) values. Example LRFD calibrations were carried out using different load and resistance models for the pullout internal stability limit state of steel strip and geosynthetic reinforced soil walls together with matching bias data reported in the literature.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.

Biomimetic surfaces for greener coastal infrastructures: an analysis of factors that contribute to make artificial structures more “natural”

The growing ecological awareness of Ocean Sprawl impacts is promoting the adoption of eco-engineering strategies to enhance the ecological performance of coastal infrastructures. Biomimicry, as an eco-engineering tool, aims to design infrastructure more suitable for wildlife by manipulating structural factors to mimic natural habitats. However, little is known about the extent to which natural and artificial substrates differ in their structure and to what extent such differences affect the biota. To fill these knowledge gaps and consequently design biomimetic surfaces, I initially explored how much physical structure diverges between various types of natural and artificial substrates and tested to what extent differences in physical structure and material composition affect the epibenthic communities. By mean of an in-field mensurative experiment and a systematic review coupled with a meta-analysis, I found that, although communities tended to differ between natural and artificial coastal habitats, both physical structure and material composition reported an overall mild effect on epibenthic communities. However, an informed choice of building material and an appropriate combination of multiple structural manipulations can promote ecological benefits at multiple levels, from increasing the ecological performance in situ to reducing the impacts during the production process. Thus, I combined my findings in a final experiment, still in progress, where I am testing the combined role of shape, brightness and inclination of biomimetic surfaces I have designed in producing benefits at multiple levels. Overall, I suggest that biomimicry has the potential to increase the ecological value of artificial habitats especially when a wide range of aspects is simultaneously considered. Indeed, none of the structural factors, individually, can fully mimic the “natural conditions” to effectively improve the ecological performance of the artificial substrates. This emphasizes the need to include in future works a multi-level perspective to fully achieve the great potential of biomimicry.

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Linear dimensional changes in plaster die models using different elastomeric materials

Dental impression is an important step in the preparation of prostheses since it provides the reproduction of anatomic and surface details of teeth and adjacent structures. The objective of this study was to evaluate the linear dimensional alterations in gypsum dies obtained with different elastomeric materials, using a resin coping impression technique with individual shells. A master cast made of stainless steel with fixed prosthesis characteristics with two prepared abutment teeth was used to obtain the impressions. References points (A, B, C, D, E and F) were recorded on the occlusal and buccal surfaces of abutments to register the distances. The impressions were obtained using the following materials: polyether, mercaptan-polysulfide, addition silicone, and condensation silicone. The transfer impressions were made with custom trays and an irreversible hydrocolloid material and were poured with type IV gypsum. The distances between identified points in gypsum dies were measured using an optical microscope and the results were statistically analyzed by ANOVA (p < 0.05) and Tukey's test. The mean of the distances were registered as follows: addition silicone (AB = 13.6 µm, CD=15.0 µm, EF = 14.6 µm, GH=15.2 µm), mercaptan-polysulfide (AB = 36.0 µm, CD = 36.0 µm, EF = 39.6 µm, GH = 40.6 µm), polyether (AB = 35.2 µm, CD = 35.6 µm, EF = 39.4 µm, GH = 41.4 µm) and condensation silicone (AB = 69.2 µm, CD = 71.0 µm, EF = 80.6 µm, GH = 81.2 µm). All of the measurements found in gypsum dies were compared to those of a master cast. The results demonstrated that the addition silicone provides the best stability of the compounds tested, followed by polyether, polysulfide and condensation silicone. No statistical differences were obtained between polyether and mercaptan-polysulfide materials.

Abrasion wear resistance of different artificial teeth opposed to metal and composite antagonists

One of the most important properties of artificial teeth is the abrasion wear resistance, which is determinant in the maintenance of the rehabilitation's occlusal pattern. OBJECTIVES: This in vitro study aims to evaluate the abrasion wear resistance of 7 brands of artificial teeth opposed to two types of antagonists. MATERIAL AND METHODS: Seven groups were prepared with 12 specimens each (BIOLUX & BL, TRILUX & TR, BLUE DENT & BD, BIOCLER & BC, POSTARIS & PO, ORTHOSIT & OR, GNATHOSTAR & GN), opposed to metallic (M & nickel-chromium alloy), and to composite antagonists (C & Solidex indirect composite). A mechanical loading device was used (240 cycles/min, 4 Hz speed, 10 mm antagonist course). Initial and final contours of each specimen were registered with aid of a profile projector (20x magnification). The linear difference between the two profiles was measured and the registered values were subjected to ANOVA and Tukey's test. RESULTS: Regarding the antagonists, only OR (M = 10.45 ± 1.42 µm and C = 2.77 ± 0.69 µm) and BC (M = 6.70 ± 1.37 µm and C = 4.48 ± 0.80 µm) presented statistically significant differences (p < 0.05). Best results were obtained with PO (C = 2.33 ± 0.91 µm and M = 1.78 ± 0.42 µm), followed by BL (C = 3.70 ± 1.32 µm and M = 3.70 ± 0.61 µm), statistically similar for both antagonists (p>0.05). Greater result variance was obtained with OR, which presented the worse results opposed to Ni-Cr (10.45 ± 1.42 µm), and results similar to the best ones against composite (2.77 ± 0.69 µm). CONCLUSIONS: Within the limitations of this study, it may be concluded that the antagonist material is a factor of major importance to be considered in the choice of the artificial teeth to be used in the prosthesis.