Synthesis and Characterisation of Functional Magnetic and Plasmonic Nanomaterials

The main aim of this thesis is the controlled and reproducible synthesis of functional materials at the nanoscale. In the first chapter, a tuning of morphology and magnetic properties of magnetite nanoparticles is presented. It was achieved by an innovative approach, which involves the use of an organic macrocycle (calixarene) to induce the oriented aggregation of NPs during the synthesis. This method is potentially applicable to the preparation of other metal oxide NPs by thermal decomposition of the respective precursors. Products obtained, in particular the multi-core nanoparticles, show remarkable magnetic and colloidal properties, making them very interesting for biomedical applications. The synthesis and functionalisation of plasmonic Au and Ag nanoparticles is presented in the second chapter. Here, a supramolecular approach was exploited to achieve a controlled and potentially reversible aggregation between Au and Ag NPs. This aggregation phenomena was followed by UV - visible spectroscopy and dynamic light scattering. In the final chapters, the conjugation of plasmonic and magnetic functionalities was tackled through the preparation of dimeric nanostructures. Au - Fe oxide heterodimeric nanoparticles were prepared and their magnetic properties thoroughly characterised. The results demonstrate the formation of FeO (wustite), together with magnetite, during the thermal decomposition of the iron precursor. By an oxidation process that preserves Au in the dimeric structures, wustite completely disappeared, with the formation of either magnetite and / or maghemite, much better from the magnetic point of view. The plasmon resonance of Au results damped by the presence of the iron oxide, a material with high refractive index, but it is still present if the Au domain of the nanoparticles is exposed towards the bulk. Finally, remarkable hyperthermia, also in vitro, was found for these structures.

Advanced receivers for next generation wireless communication systems

Despite extensive progress on the theoretical aspects of spectral efficient communication systems, hardware impairments, such as phase noise, are the key bottlenecks in next generation wireless communication systems. The presence of non-ideal oscillators at the transceiver introduces time varying phase noise and degrades the performance of the communication system. Significant research literature focuses on joint synchronization and decoding based on joint posterior distribution, which incorporate both the channel and code graph. These joint synchronization and decoding approaches operate on well designed sum-product algorithms, which involves calculating probabilistic messages iteratively passed between the channel statistical information and decoding information. Channel statistical information, generally entails a high computational complexity because its probabilistic model may involve continuous random variables. The detailed knowledge about the channel statistics for these algorithms make them an inadequate choice for real world applications due to power and computational limitations. In this thesis, novel phase estimation strategies are proposed, in which soft decision-directed iterative receivers for a separate A Posteriori Probability (APP)-based synchronization and decoding are proposed. These algorithms do not require any a priori statistical characterization of the phase noise process. The proposed approach relies on a Maximum A Posteriori (MAP)-based algorithm to perform phase noise estimation and does not depend on the considered modulation/coding scheme as it only exploits the APPs of the transmitted symbols. Different variants of APP-based phase estimation are considered. The proposed algorithm has significantly lower computational complexity with respect to joint synchronization/decoding approaches at the cost of slight performance degradation. With the aim to improve the robustness of the iterative receiver, we derive a new system model for an oversampled (more than one sample per symbol interval) phase noise channel. We extend the separate APP-based synchronization and decoding algorithm to a multi-sample receiver, which exploits the received information from the channel by exchanging the information in an iterative fashion to achieve robust convergence. Two algorithms based on sliding block-wise processing with soft ISI cancellation and detection are proposed, based on the use of reliable information from the channel decoder. Dually polarized systems provide a cost-and spatial-effective solution to increase spectral efficiency and are competitive candidates for next generation wireless communication systems. A novel soft decision-directed iterative receiver, for separate APP-based synchronization and decoding, is proposed. This algorithm relies on an Minimum Mean Square Error (MMSE)-based cancellation of the cross polarization interference (XPI) followed by phase estimation on the polarization of interest. This iterative receiver structure is motivated from Master/Slave Phase Estimation (M/S-PE), where M-PE corresponds to the polarization of interest. The operational principle of a M/S-PE block is to improve the phase tracking performance of both polarization branches: more precisely, the M-PE block tracks the co-polar phase and the S-PE block reduces the residual phase error on the cross-polar branch. Two variants of MMSE-based phase estimation are considered; BW and PLP.