Phthalocyanines : interaction with carbon structures and as PDT agents

This dissertation describes the synthesis and characterization of different phthalocyanine (Pc) derivatives, as well as some porphyrins (Pors), for supramolecular interaction with different carbon nanostructures, to evaluate their potential application in electronic nanodevices. Likewise, it is also reported the preparation and biological evaluation of interesting phthalocyanine conjugates for cancer photodynamic therapy (PDT) and microorganisms photodynamic inactivation (PDI). The phthalonitrile precursors were prepared from commercial phthalonitriles by nucleophilic substitution of -NO2, -Cl, or -F groups, present in the phthalonitrile core, by thiol or pyridyl units. After the synthesis of these phthalonitriles, the corresponding Pcs were prepared by ciclotetramerization using a metallic salt as template at high temperatures. A second strategy involved the postfunctionalization of hexadecafluorophthalocyaninato zinc(II) through the adequate substituents of mercaptopyridine or cyclodextrin units on the macrocycle periphery. The different compounds were structurally characterized by diverse spectroscopic techniques, namely 1H, 13C and 19F nuclear magnetic resonance spectroscopies (attending the elemental composition of each structure); absorption and emission spectroscopy, and mass spectrometry. For the specific photophysical studies were also used electrochemical characterization, femtosecond and raman spectroscopy, transmission electron and atomic force microscopy. It was highlighted the noncovalent derivatisation of carbon nanostructures, mainly single wall carbon nanotubes (SWNT) and graphene nanosheets with the prepared Pc conjugates to study the photophysical properties of these supramolecular nanoassemblies. Also, from pyridyl-Pors and ruthenium phthalocyanines (RuPcs) were performed Por-RuPcs arrays via coordination chemistry. The results obtained of the novel supramolecular assemblies showed interesting electron donor-acceptor interactions and might be considered attractive candidates for nanotechnological devices. On the other hand, the amphiphilic phthalocyanine-cyclodextrin (Pc-CD) conjugates were tested in biological trials to assess their ability to inhibit UMUC- 3 human bladder cancer cells. The results obtained demonstrated that these photoactive conjugates are highly phototoxic against human bladder cancer cells and could be applied as promising PDT drugs.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.