Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Thin film & Vortex. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Mechanically Durable Superhydrophobic Surfaces

Abstract: Development of durable non-wetting surfaces is hindered by the fragility of the microscopic roughness features that are necessary for superhydrophobicity. Mechanical wear on superhydrophobic surfaces usually shows as increased sticking of water, leading to loss of non-wettability. Increased wear resistance has been demonstrated by exploiting hierarchical roughness where nanoscale roughness is protected to some degree by large scale features, and avoiding the use of hydrophilic bulk materials is shown to help prevent the formation of hydrophilic defects as a result of wear. Additionally, self-healing hydrophobic layers and roughness patterns have been suggested and demonstrated. Nevertheless, mechanical contact not only causes damage to roughness patterns but also surface contamination, which shortens the lifetime of superhydrophobic surfaces in spite of the self-cleaning effect. The use of photocatalytic effect and reduced electric resistance have been suggested to prevent the accumulation of surface contaminants. Resistance to organic contaminants is more challenging, however, oleophobic surface patterns which are non-wetting to organic liquids have been demonstrated. While the fragility of superhydrophobic surfaces currently limits their applicability, development of mechanically durable surfaces will enable a wide range of new applications in the future.

Mercer kernel-based clustering in feature space

Abstract: The article presents a method for both the unsupervised partitioning of a sample of data and the estimation of the possible number of inherent clusters which generate the data. This work exploits the notion that performing a nonlinear data transformation into some high dimensional feature space increases the probability of the linear separability of the patterns within the transformed space and therefore simplifies the associated data structure. It is shown that the eigenvectors of a kernel matrix which defines the implicit mapping provides a means to estimate the number of clusters inherent within the data and a computationally simple iterative procedure is presented for the subsequent feature space partitioning of the data.

Engineering of nanostructured carbon materials with electron or ion beams

Abstract: Irradiating solids with energetic particles is usually thought to introduce disorder, normally an undesirable phenomenon. But recent experiments on electron or ion irradiation of various nanostructures demonstrate that it can have beneficial effects and that electron or ion beams may be used to tailor the structure and properties of nanosystems with high precision. Moreover, in many cases irradiation can lead to self-organization or self-assembly in nanostructures. In this review we survey recent advances in the rapidly evolving area of irradiation effects in nanostructured materials, with particular emphasis on carbon systems because of their technological importance and the unique ability of graphitic networks to reconstruct under irradiation. We dwell not only on the physics behind irradiation of nanostructures but also on the technical applicability of irradiation for nanoengineering of carbon and other systems.

Non-negative sparse coding

Abstract: Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. We briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization. We then give a simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components. In addition, we show how the basis vectors can be learned from the observed data. Simulations demonstrate the effectiveness of the proposed method.

Severe Plastic Deformation (SPD) Processes for Metals

Abstract: Processes of severe plastic deformation (SPD) are defined as metal forming processes in which a very large plastic strain is imposed on a bulk process in order to make an ultra-fine grained metal The objective of the SPD processes for creating ultra-fine grained metal is to produce lightweight parts by using high strength metal for the safety and reliability of micro-parts and for environmental harmony In this keynote paper, the fabrication process of equal channel angular pressing (ECAP), accumulative roll-bonding (ARB), high pressure torsion (HPT), and others are introduced, and the properties of metals processed by the SPD processes are shown Moreover, the combined processes developed recently are also explained Finally, the applications of the ultra-fine grained (UFG) metals are discussed