Size-Dependent and Step-Modulated Supramolecular Electrochemical Properties of Catechol-Derived Adlayers at Pt(hkl) Surfaces

The electrochemical reactivity of catechol-derived adlayers is reported at platinum (Pt) single-crystal electrodes. Pt(111) and stepped vicinal surfaces are used as model surfaces possessing well-ordered nanometer-sized Pt(111) terraces ranging from 0.4 to 12 nm. The electrochemical experiments were designed to probe how the control of monatomic step-density and of atomic-level step structure can be used to modulate molecule–molecule interactions during self-assembly of aromatic-derived organic monolayers at metallic single-crystal electrode surfaces. A hard sphere model of surfaces and a simplified band formation model are used as a theoretical framework for interpretation of experimental results. The experimental results reveal (i) that supramolecular electrochemical effects may be confined, propagated, or modulated by the choice of atomic level crystallographic features (i.e.monatomic steps), deliberately introduced at metallic substrate surfaces, suggesting (ii) that substrate-defect engineering may be used to tune the macroscopic electronic properties of aromatic molecular adlayers and of smaller molecular aggregates.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.