HPLC-EICD: An Useful Tool for the Pursuit of Novel Analytical Strategies for the Detection of Antioxidant Secondary Metabolites

Living cells are continuously exposed to a variety of challenges that exert oxidative stress and are directly related with senescence and the onset of various pathological conditions such as coronary heart disease, rheumatoid arthritis and cancer. Nevertheless, living organisms have developed a complex antioxidant network to counteract reactive species that are detrimental to life. With the aim of bio-prospecting plant species from the Brazilian Cerrado and Atlantic Forest, we have established a methodology to detect secondary antioxidant metabolites in crude extracts and fractions obtained from plant species. Combining HPLC with an electrochemical detector allowed us to detect micromolecules that showed antioxidant activities in Chimarrhis turbinata (DC) leaf extracts. Comparison with purified flavonoid standards led us to identify the compounds in their natural matrices giving valuable information on their antioxidant capacity.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)