Radio frequency plasma assisted surface modification of Fe3O4 nanoparticles using polyaniline/polypyrrole for bioimaging and magnetic hyperthermia applications.

Surface modification of superparamagnetic Fe3O4 nanoparticles using polymers (polyaniline/polypyrrole) was done by radio frequency (r.f.) plasma polymerization technique and characterized by XRD, TEM, TG/DTA and VSM. Surface-passivated Fe3O4 nanoparticles with polymers were having spherical/rod-shaped structures with superparamagnetic properties. Broad visible photoluminescence emission bands were observed at 445 and 580 nm for polyaniline-coated Fe3O4 and at 488 nm for polypyrrole-coated Fe3O4. These samples exhibit good fluorescence emissions with L929 cellular assay and were non-toxic. Magnetic hyperthermia response of Fe3O4 and polymer (polyaniline/polypyrrole)-coated Fe3O4 was evaluated and all the samples exhibit hyperthermia activity in the range of 42-45 °C. Specific loss power (SLP) values of polyaniline and polypyrrole-coated Fe3O4 nanoparticles (5 and 10 mg/ml) exhibit a controlled heat generation with an increase in the magnetic field.

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis.

Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer's detection. However, it requires a defined basis function considered a significant problem. In this work, the concept of empirical mode decomposition is introduced as an alternative to process Alzheimer signals. The performance of empirical mode decomposition heavily relies on a parameter called threshold. In our previous works, we found that the existing thresholding techniques were not able to highlight relevant information. The use of Tsallis entropy as a thresholder is evaluated through the combination of empirical mode decomposition and neural networks. Thanks to the extraction of better features that boost the classification accuracy, the proposed approach outperforms the state-of-the-art in terms of peak signal to noise ratio and root mean square error. Hence, our methodology is more likely to succeed than methods based on other landmarks such as Bayes, Normal and Visu shrink. We finally report an accuracy rate of 80%, while the aforementioned techniques only yield performances of 65%, 60% and 40%, respectively.

Evidence for enhanced optical properties through plasmon resonance energy transfer in silver silica nanocomposites.

Silver nanoparticles were dispersed in the pores of monolithic mesoporous silica prepared by a modified sol-gel method. Structural and microstructural analyses were carried out by Fourier transform infrared spectroscopy and transmission electron microscopy. X-ray photoelectron spectroscopy was employed to determine the chemical states of silver in the silica matrix. Optical absorption studies show the evolution absorption band around 300 nm for silver (Ag) in a silica matrix and it was found to be redshifted to 422 nm on annealing. Photoluminescence studies indicate the presence of various luminescent emitting centers corresponding to silver ions and silver dimers in the SiO2 matrix. The enhancement of absorption and photoluminescence properties is attributed to plasmon resonance energy transfer from Ag nanoparticles to luminescent species in the matrix.

Fuzzy-entropy threshold based on a complex wavelet denoising technique to diagnose Alzheimer disease.

The presence of irregularities in electroencephalographic (EEG) signals entails complexities during the Alzheimer's disease (AD) diagnosis. In addition, the uncertainty presented on EEG raises major issues in the improvement of the classification rate. The multi-resolution analysis through an optimum threshold will likely achieve better results in distinguishing AD and normal EEG signals. Hence, a fuzzy-entropy concept defined in a complex multi-resolution wavelet has been proposed to obtain the most appropriate threshold. First, the complex coefficients are fuzzified using a Gaussian membership function. Afterwards, the ability of the proposed fuzzy-entropy threshold has been compared with traditional thresholds in complex wavelet domain. Experimental results show that the authors' methodology produces a higher signal-to-noise ratio and a lower root-mean-square error than traditional approaches. Moreover, a neural network scheme is performed along several features to classify AD from normal EEG signals obtaining a specificity of 87.5%.