Controlled route to the fabrication of carbon and boron nitride nanoscrolls: A molecular dynamics investigation

Carbon nanoscrolls (graphene layers rolled up into papyrus-like tubular structures) are nanostructures with unique and interesting characteristics that could be exploited to build several new nanodevices. However, an efficient and controlled synthesis of these structures was not achieved yet, making its large scale production a challenge to materials scientists. Also, the formation process and detailed mechanisms that occur during its synthesis are not completely known. In this work, using fully atomistic molecular dynamics simulations, we discuss a possible route to nanoscrolls made from graphene layers deposited over silicon oxide substrates containing chambers/pits. The scrolling mechanism is triggered by carbon nanotubes deposited on the layers. The process is completely general and can be used to produce scrolls from other lamellar materials, like boron nitride, for instance. © 2013 American Institute of Physics.

Shvo's catalyst in chemoenzymatic dynamic kinetic resolution of amines-inner or outer sphere mechanism?

Evidence is provided for the inner-sphere mechanism with actual metal coordination of the racemic amine in the crucial hydrogen transfer step promoted by Shvo's catalyst of the chemoenzymatic dynamic kinetic resolution (DKR) of amines. Key intermediates involved in this H-transfer step were intercepted and continuously monitored by electrospray ionization mass spectrometry (ESI-MS) and characterized by their dissociation chemistries via ESI-MS/MS. © 2013 The Royal Society of Chemistry.

Osteoblast Adhesion Dynamics: A Possible Role for ROS and LMW-PTP

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Combining Experimental Evidence and Molecular Dynamic Simulations To Understand the Mechanism of Action of the Antimicrobial Octapeptide Jelleine-I

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

On nonlinear dynamics and control of a robotic arm with chaos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)