Secondary Dopants of Electrically Conducting Polyanilines.

Secondary dopants and the doping methods were identified for increasing the electrical conductivity of a highly processable and a primarily doped polyaniline dinonylnaphthalene sulfonic acid (PANI-DNNSA). The secondary doping was carried out using film, solution, and vapor doping methods. The doping methods and functional groups of secondary dopants were observed to play a critical role for inducing electrical characteristics of polyaniline. When secondary film doping method and p-toluenesulfonic acid were used, the electrical conductivity of the secondary doped polyaniline was measured to be increased from 0.16 to 334 S/cm. A novel vapor annealing doping method was developed to incorporate secondary dopants into solution cast polyaniline films.

Adsorptive Removal of Antibiotic Ciprofloxacin from Aqueous Solution Using Protein-Modified Nanosilica.

The present study aims to investigate adsorptive removal of molecular ciprofloxacin using protein-modified nanosilica (ProMNS). Protein was successfully extracted from Moringa seeds while nanosilica was synthesized from rice husk. Fourier-transform infrared (FTIR), ultraviolet visible (UV-Vis) and high-performance liquid chromatography (HPLC) were used to evaluate the characterization of protein. Adsorption of protein onto nanosilica at different pH and ionic strength was thoroughly studied to modify nanosilica surface. The removal efficiency of antibiotic ciprofloxacin (CFX) increased from 56.84% to 89.86% after surface modification with protein. Effective conditions for CFX removal using ProMNS were systematically optimized and found to be pH 7.0, adsorption time 90 min, adsorbent dosage 10 mg/mL, and ionic strength 1 mM KCl. A two-step model was successfully used to fit the adsorption isotherms of CFX onto ProMNS at different ionic strength while a pseudo-second-order model could fit adsorption kinetic of CFX onto ProMNS very well. Maximum adsorption capacity was very high that reached to 85 mg/g. Adsorption of CFX onto ProMNS decreased with increasing KCl concentration, suggesting that adsorption of CFX onto ProMNS is mainly controlled by electrostatic attraction between positively charged ProMNS surface and anionic species of CFX. Adsorption mechanisms of CFX onto ProMNS were discussed in detail based on adsorption isotherms, the change in surface charge by zeta potentail and the change in functional groups by FT-IR. The removal of CFX after three regenerations was greater than 73% while CFX removal from an actual hospital wastewater using ProMNS reached to 70%. Our results suggest that ProMNS is a new and eco-friendly adsorbent to remove antibiotics from aqueous solutions.

Synthesis, Characterization, and Modification of Alumina Nanoparticles for Cationic Dye Removal.

In the present study, alumina nanoparticles (nano-alumina) which were successfully fabricated by solvothermal method, were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Transmission Electron Microscopy (TEM), and Brunauer⁻Emmett⁻Teller (BET) methods. The removal of cationic dye, Rhodamine B (RhB), through adsorption method using synthesized nano-alumina with surface modification by anionic surfactant was also investigated. An anionic surfactant, sodium dodecyl sulfate (SDS) was used to modify nano-alumina surface at low pH and high ionic strength increased the removal efficiency of RhB significantly. The optimum adsorption conditions of contact time, pH, and adsorbent dosage for RhB removal using SDS modified nano-alumina (SMNA) were found to be 120 min, pH 4, and 5 mg/mL respectively. The RhB removal using SMNA reached a very high removal efficiency of 100%. After four times regeneration of adsorbent, the removal efficiency of RhB using SMNA was still higher than 86%. Adsorption isotherms of RhB onto SMNA at different salt concentrations were fitted well by a two-step model. A very high adsorption capacity of RhB onto SMNA of 165 mg/g was achieved. Adsorption mechanisms of RhB onto SMNA were discussed on the basis of the changes in surface modifications, the change in surface charges and adsorption isotherms.

Modeling perceived stress via HRV and accelerometer sensor streams.

Discovering and modeling of stress patterns of human beings is a key step towards achieving automatic stress monitoring, stress management and healthy lifestyle. As various wearable sensors become popular, it becomes possible for individuals to acquire their own relevant sensory data and to automatically assess their stress level on the go. Previous studies for stress analysis were conducted in the controlled laboratory and clinic settings. These studies are not suitable for stress monitoring in one's daily life as various physical activities may affect the physiological signals. In this paper, we address such issue by integrating two modalities of sensors, i.e., HRV sensors and accelerometers, to monitor the perceived stress levels in daily life. We gathered both the heart and the motion data from 8 participants continuously for about 2 weeks. We then extracted features from both sensory data and compared the existing machine learning methods for learning personalized models to interpret the perceived stress levels. Experimental results showed that Bagging classifier with feature selection is able to achieve a prediction accuracy 85.7%, indicating our stress monitoring on daily basis is fairly practical.