Publications

by Keyword: Dispersion


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Ramos, E., Pardo, W. A., Mir, M., Samitier, J., (2017). Dependence of carbon nanotubes dispersion kinetics on surfactants Nanotechnology 28, (13), 135702

Carbon nanotubes (CNTs) have been the subject of many studies due to their unique structure and desirable properties. However, the ability to solubilize and separate single CNTs from the bundles they form is still a challenge that needs to be overcome in order to extend their applications in the field of Nanotechnology. Covalent interactions are designed to modify CNTs surface and so prevent agglomeration. Though, this method alters the structures and intrinsic properties of CNTs. In the present work, noncovalent approaches to functionalize and solubilize CNTs are studied in detail. A dispersion kinetic study was performed to characterize the ability of different type of surfactants (non-ionic, anionic, cationic and biopolymer) to unzip CNT bundles. The dispersion kinetic study performed depicts the distinct CNTs bundles unzipping behavior of the different type of surfactants and the results elucidate specific wavelengths in relation with the degree of CNT clustering, which provides new tools for a deeper understanding and characterization of CNTs. Small angle x-ray scattering and transmission electron microscopy results are in agreement with UV-vis-NIR observations, revealing perfectly monodispersed CNTs for the biopolymer and cationic surfactant.

Keywords: Dispersion, DNA, Single-walled carbon nanotubes (SWCNTs), Small angle x-ray scattering (SAXS), Sodium dodecyl sulfate (SDS), Surfactant, Triton X-100


Lozano, M., Fiz, J. A., Jané, R., (2016). Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency IEEE Journal of Biomedical and Health Informatics 20, (2), 486-497

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

Keywords: Diseases, Dispersion, Empirical mode decomposition, Feature extraction, Informatics, Support vector machines


Olmedo, Ivonne, Araya, Eyleen, Sanz, Fausto, Medina, Elias, Arbiol, Jordi, Toledo, Pedro, Àlvarez-Lueje, Alejandro, Giralt, Ernest, Kogan, Marcelo J., (2008). How changes in the sequence of the peptide CLPFFD-NH2 can modify the conjugation and stability of gold nanoparticles and their affinity for beta-amyloid fibrils Bioconjugate Chemistry 19, (6), 1154-1163

In a previous work, we studied the interaction of

Keywords: Self-assembled monolayers, Aggregation, Dispersions, Adsorption, Particles, Design, Size


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