Proton-Functionalized Graphitic Carbon Nitride for Efficient Metal-Free Destruction of Escherichia coli under Low-Power Light Irradiation.

Universal access to clean water has been a global ambition over the years. Photocatalytic water disinfection through advanced oxidation processes has been regarded as one of the promising methods for breaking down microbials. The forefront of this research focuses on the application of metal-free photocatalysts for disinfection to prevent secondary pollution. Graphitic carbon nitride (g-C3 N4 ) has achieved instant attention as a metal-free and visible-light-responsive photocatalyst for various energy and environmental applications. However, the photocatalytic efficiency of g-C3 N4 is still affected by its rapid charge recombination and sluggish electron-transfer kinetics. In this contribution, two-dimensionally protonated g-C3 N4 was employed as metal-free photocatalyst for water treatment and demonstrated 100 % of Escherichia coli within 4 h under irradiation with a 23 W light bulb. The introduction of protonation can modulate the surface charge of g-C3 N4 ; this enhances its conductivity and provides a "highway" for the delocalization of electrons. This work highlights the potential of conjugated polymers in antibacterial application.

THE EFFECTIVENESS OF HYPNOTHERAPY IN THE TREATMENT OF CHINESE PSYCHIATRIC PATIENTS.

This is a prospective randomized, controlled study of patients suffering from anxiety, depression, or mixed anxiety-depressive disorder attending a hypnotherapy clinic for 8 weeks. Participants were assessed with various clinical scales and randomly assigned to either the study or control group. The study group received 5 to 7 sessions of hypnotherapy through the 8-week period, whereas the control group received conventional psychiatric treatment. All patients' clinical conditions were reassessed at the end of the 8th week. Comparing study and control groups at the end of the 8th week, there was improvement in the study group in the scores of an array of clinical scales. Such satisfactory results illustrate the effectiveness of hypnotherapy in relieving anxiety and depressive symptoms.

Heteroatom Nitrogen- and Boron-Doping as a Facile Strategy to Improve Photocatalytic Activity of Standalone Reduced Graphene Oxide in Hydrogen Evolution.

Owing to its superior properties and versatility, graphene has been proliferating the energy research scene in the past decade. In this contribution, nitrogen (N-) and boron (B-) doped reduced graphene oxide (rGO) variants were investigated as a sole photocatalyst for the green production of H2 and their properties with respect to photocatalysis were elucidated for the first time. N- and B-rGOs were facilely prepared via the pyrolysis of graphene oxide with urea and boron anhydride as their respective dopant source. The pyrolysis temperature was varied (600-800 °C for N-rGO and 800-1000 °C for B-rGO) in order to modify dopant loading percentage (%) which was found to be influential to photocatalytic activity. N-rGO600 (8.26 N at%) and B-rGO1000 (3.59 B at%), which holds the highest at% from each of their party, exhibited the highest H2 activity. Additionally, the effects of the nature of N and B bonding configuration in H2 photoactivity were also examined. This study demonstrates the importance of dopant atoms in graphene, rendering doping as an effective strategy to bolster photocatalytic activity for standalone graphene derivative photocatalysts.

Ternary nanohybrid of reduced graphene oxide-nafion@silver nanoparticles for boosting the sensor performance in non-enzymatic amperometric detection of hydrogen peroxide.

A sensitive and novel electrochemical sensor was developed for the detection of hydrogen peroxide (H2O2) using a reduced graphene oxide-nafion@silver6 (rGO-Nf@Ag6) nanohybrid modified glassy carbon electrode (GC/rGO-Nf@Ag6). The GC/rGO-Nf@Ag6 electrode exhibited an excellent electrochemical sensing ability for determining H2O2 with high sensitivity and selectivity. The detection limit of the electrochemical sensor using the GC/rGO-Nf@Ag6 electrode for H2O2 determination was calculated to be 5.35×10-7M with sensitivity of 0.4508µAµM-1. The coupling between rGO-Nf with silver nanoparticles (AgNPs) significantly boosted the electroanalytical performance by providing more active area for analyte interaction, thereby allowing more rapid interfacial electron transfer process. The interfering effect on the current response of H2O2 was studied and the results revealed that the sensor electrode exhibited an excellent immunity from most common interferents. The proposed non-enzymatic electrochemical sensor was used for determining H2O2 in apple juice, and the sensor electrode provided satisfactory results with reliable recovery values. These studies revealed that the novel GC/rGO-Nf@Ag6 sensor electrode could be a potential candidate for the detection of H2O2.

Highly sensitive integrated pressure sensor with horizontally oriented carbon nanotube network.

This paper presents a functionalized, horizontally oriented carbon nanotube network as a sensing element to enhance the sensitivity of a pressure sensor. The synthesis of horizontally oriented nanotubes from the AuFe catalyst and their deposition onto a mechanically flexible substrate via transfer printing are studied. Nanotube formation on thermally oxidized Si (100) substrates via plasma-enhanced chemical vapor deposition controls the nanotube coverage and orientation on the flexible substrate. These nanotubes can be simply transferred to the flexible substrate without changing their physical structure. When tested under a pressure range of 0 to 50 kPa, the performance of the fabricated pressure sensor reaches as high as approximately 1.68%/kPa, which indicates high sensitivity to a small change of pressure. Such sensitivity may be induced by the slight contact in isolated nanotubes. This nanotube formation, in turn, enhances the modification of the contact and tunneling distance of the nanotubes upon the deformation of the network. Therefore, the horizontally oriented carbon nanotube network has great potential as a sensing element for future transparent sensors.

Formation of silicon nanostructures with a combination of spacer technology and deep reactive ion etching.

A new method of fabricating high aspect ratio nanostructures in silicon without the use of sub-micron lithographic technique is reported. The proposed method comprises two important steps including the use of CMOS spacer technique to form silicon nitride nanostructure masking followed by deep reactive ion etching (DRIE) of the silicon substrate to form the final silicon nanostructures. Silicon dioxide is used as the sacrificial layer to form the silicon nitride nanostructures. With DRIE a high etch selectivity of 50:1 between silicon and silicon nitride was achieved. The use of the spacer technique is particularly advantageous where self-aligned nanostructures with potentially unlimited lengths are formed without the need of submicron lithographic tools and resist materials. With this method, uniform arrays of 100 nm silicon nanostructures which are at least 4 µm tall with aspect ratio higher than 40 were successfully fabricated.

Selective formation of tungsten nanowires.

We report on a process for fabricating self-aligned tungsten (W) nanowires with polycrystalline silicon core. Tungsten nanowires as thin as 10 nm were formed by utilizing polysilicon sidewall transfer technology followed by selective deposition of tungsten by chemical vapor deposition (CVD) using WF6 as the precursor. With selective CVD, the process is self-limiting whereby the tungsten formation is confined to the polysilicon regions; hence, the nanowires are formed without the need for lithography or for additional processing. The fabricated tungsten nanowires were observed to be perfectly aligned, showing 100% selectivity to polysilicon and can be made to be electrically isolated from one another. The electrical conductivity of the nanowires was characterized to determine the effect of its physical dimensions. The conductivity for the tungsten nanowires were found to be 40% higher when compared to doped polysilicon nanowires of similar dimensions.

Neuro-genetic optimization of the diffuser elements for applications in a valveless diaphragm micropumps system.

In this study, a hybridized neuro-genetic optimization methodology realized by embedding numerical simulations trained artificial neural networks (ANN) into a genetic algorithm (GA) is used to optimize the flow rectification efficiency of the diffuser element for a valveless diaphragm micropump application. A higher efficiency ratio of the diffuser element consequently yields a higher flow rate for the micropump. For that purpose, optimization of the diffuser element is essential to determine the maximum pumping rate that the micropump is able to generate. Numerical simulations are initially carried out using CoventorWare® to analyze the effects of varying parameters such as diffuser angle, Reynolds number and aspect ratio on the volumetric flow rate of the micropump. A limited range of simulation results will then be used to train the neural network via back-propagation algorithm and optimization process commence subsequently by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to maximize the efficiency ratio of the diffuser element for the range of parameters investigated. The optimized efficiency ratio obtained from the neuro-genetic optimization is 1.38, which is higher than any of the maximum efficiency ratio attained from the overall parametric studies, establishing the superiority of the optimization method.

Neuro-genetic optimization of temperature control for a continuous flow polymerase chain reaction microdevice.

In this study, a hybridized neuro-genetic optimization methodology realized by embedding finite element analysis (FEA) trained artificial neural networks (ANN) into genetic algorithms (GA), is used to optimize temperature control in a ceramic based continuous flow polymerase chain reaction (CPCR) device. The CPCR device requires three thermally isolated reaction zones of 94 degrees C, 65 degrees C, and 72 degrees C for the denaturing, annealing, and extension processes, respectively, to complete a cycle of polymerase chain reaction. The most important aspect of temperature control in the CPCR is to maintain temperature distribution at each reaction zone with a precision of +/-1 degree C or better, irrespective of changing ambient conditions. Results obtained from the FEA simulation shows good comparison with published experimental work for the temperature control in each reaction zone of the microfluidic channels. The simulation data are then used to train the ANN to predict the temperature distribution of the microfluidic channel for various heater input power and fluid flow rate. Once trained, the ANN analysis is able to predict the temperature distribution in the microchannel in less than 20 min, whereas the FEA simulation takes approximately 7 h to do so. The final optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. Finally, the GA optimized results are used to build a new FEA model for numerical simulation analysis. The simulation results for the neuro-genetic optimized CPCR model and the initial CPCR model are then compared. The neuro-genetic optimized model shows a significant improvement from the initial model, establishing the optimization method's superiority.