Tailoring the Ti surface via electropolishing nanopatterning as a route to obtain highly ordered TiO2 nanotubes

Highly ordered TiO2 nanotubes (NTs) were synthesized by the electrochemical anodization of Tifoils subjected to electropolishing (EP) pre-treatment. We found that the Ti surface roughnessplays an important role in the onset of pore nucleation in enhancing the local focusing effect ofthe electrical field. Additionally, EP induces the formation of dimple structures on the metalsurface, which can work as a pre-pattern prior to anodization. These shallow ripples lead to apreferentially ordered pore nucleation, offering an organizational improvement of the anodicoxide NTs. We found that, depending on the EP applied potential, the roughness and the spatialperiod of the ripple-like structures varies from 82 nm and from 12230 nm, respectively. Suchtuning allowed us to focus on the influence of the initial Ti pre-surface topography features onthe NTs length, organization, and hexagonal arrangement quality, as well as diameter anddensity. Our results show that an EP under 10 V is the most suitable to obtain a small Ti surfaceroughness, the largest NT length (40% enhancement), and the effective improvement of theordered hexagonal NTs arrays over larger areas. Furthermore, the NTs dimensions (porediameters and density) were also found to depend on the initial Ti surface topography. The use ofoptimized EP allows us to obtain highly hexagonal self-ordered samples at a reduced timeand cost

Deep learning for the classification of quenched jets

An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying deep learning techniques for this purpose. Samples of $Z+$jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.Fundação para a Ciência e a Tecnologiainfo:eu-repo/semantics/publishedVersio

Contribution from Tree Legumes to Mixed Grass-Legume Pastures

Legumes and associated microorganisms may fix N from atmosphere and benefit grass on mixed grass-legume pastures. Nitrogen may be transferred by different mechanisms, including direct transfer of N compounds by roots, decomposition of nodules, roots, litter from legume (Nair 1993), and through animal excreta after legume intake by cattle. Silvopastoral systems including tree legumes may become a viable option in tropical regions, considering the increasing prices of N fertilizers compared to farm products such as beef and milk. This experiment evaluated legume contribution on mixed grass-legume pastures in the coastal region of Pernambuco State, Brazil