Synthesis and characterization of nanoapatites organofunctionalized with aminotriphosphonate agents

Organofunctionalized apatite nanoparticles were prepared using a one step process involving dissolution/precipitation of natural phosphate rock and covalent grafting of nitrilotris(methylene)triphosphonate (NTP). The synthesized materials were characterized by Brunauer–Emmett–Teller (BET) surface measurement, thermogravimetry, inductively coupled plasma emission spectroscopy (ICP–ES), elemental analysis, multinuclear solid state cross-polarization/magic angle spinning (CP/MAS) and single-pulse NMR spectroscopy, transmission electron microscopy (TEM) and energy dispersive X-ray analysis (EDXA). After grafting BET measurements yielded particle specific surface areas ranging from 88 to 193 m² g^?1 depending on the grafted phosphonate. The results show that the surfaces of the nanoapatite particles can be covered with functional groups bound through a variable number of R–P–O–Ca bonds to render them organoapatites.     

Modeling semiconductor devices by using Neuro Space Mapping

In this paper a new method for modeling semiconductor devices by use of the drift-diffusion (DD) model and neural networks is presented. Unlike the hydrodynamic (HD) model which is complicated, time consuming with high processing cost, the proposed method has lower complexity and lower simulation time. In this method the RBF neural network has been used for correcting parameters in the drift-diffusion model. Therefore solving approximate model (DD) causes to obtain accurate response. The proposed method is first applied to a silicon n-i-n diode in one dimension, and then to a silicon thin-film MOSFET in two-dimensions, both for interpolation and extrapolation. The obtained results for basic variables, i.e., electron and potential distribution for different voltages, confirm the high efficiency of the proposed method.     

Dielectrophoresis microbial characterization and isolation of Staphylococcus aureus based on optimum crossover frequency

Characterization of antibiotic-resistant bacteria is a significant concern that persists for the rapid classification and analysis of the bacteria. A technology that utilizes the manipulation of antibiotic-resistant bacteria is key to solving the significant threat of these pathogenic bacteria by rapid characterization profile. Dielectrophoresis (DEP) can differentiate between antibiotic-resistant and susceptible bacteria based on their physical structure and polarization properties. In this work, the DEP response of two Gram-positive bacteria, namely, Methicillin-resistant Staphylococcus aureus (MRSA) and Methicillin-susceptible S. aureus (MSSA), was investigated and simulated. The DEP characterization was experimentally observed on the bacteria influenced by oxacillin and vancomycin antibiotics. MSSA control without antibiotics has crossover frequencies ($f_{x0}$) from 6 to 8 MHz, whereas MRSA control is from 2 to 3 MHz. The $f_{x0}$ changed when bacteria were exposed to the antibiotic. As for MSSA, the $f_{x0}$ decreased to 3.35 MHz compared to $f_{x0}$ MSSA control without antibiotics, MRSA, $f_{x0}$ increased to 7 MHz when compared to MRSA control. The changes in the DEP response of MSSA and MRSA with and without antibiotics were theoretically proven using MyDEP and COMSOL simulation and experimentally based on the modification to the bacteria cell walls. Thus, the DEP response can be employed as a label-free detectable method to sense and differentiate between resistant and susceptible strains with different antibiotic profiles. The developed method can be implemented on a single platform to analyze and identify bacteria for rapid, scalable, and accurate characterization.