RESUMO
This study reports on a facile and economical method for the scalable synthesis of few-layered graphene sheets by the microwave-assisted functionalization. Herein, single-layered and few-layered graphene sheets were produced by dispersion and exfoliation of functionalized graphite in ethylene glycol. Thermal treatment was used to prepare pure graphene without functional groups, and the pure graphene was labeled as thermally-treated graphene (T-GR). The morphological and statistical studies about the distribution of the number of layers showed that more than 90% of the flakes of T-GR had less than two layers and about 84% of T-GR were single-layered. The microwave-assisted exfoliation approach presents us with a possibility for a mass production of graphene at low cost and great potentials in energy storage applications of graphene-based materials. Owing to unique surface chemistry, the T-GR demonstrates an excellent energy storage performance, and the electrochemical capacitance is much higher than that of the other carbon-based nanostructures. The nanoscopic porous morphology of the T-GR-based electrodes made a significant contribution in increasing the BET surface as well as the specific capacitance of graphene. T-GR, with a capacitance of 354.1 Fg(-1) at 5 mVs(-1) and 264 Fg(-1) at 100 mVs(-1), exhibits excellent performance as a supercapacitor.
RESUMO
Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.
RESUMO
Capacitive deionization (CDI) is a promising procedure for removing various charged ionic species from brackish water. The performance of graphene-based material in capacitive deionization is lower than the expectation of the industry, so highly-crumpled, few-layered graphene (HCG) and highly-crumpled nitrogen-doped graphene (HCNDG) with high surface area have been introduced as promising candidates for CDI electrodes. Thus, HCG and HCNDG were prepared by exfoliation of graphite in the presence of liquid-phase, microwave-assisted methods. An industrially-scalable, cost-effective, and simple approach was employed to synthesize HCG and HCNDG, resulting in few-layered graphene and nitrogen-doped graphene with large specific surface area. Then, HCG and HCNDG were utilized for manufacturing a new class of carbon nanostructure-based electrodes for use in large-scale CDI equipment. The electrosorption results indicated that both the HCG and HCNDG have fairly large specific surface areas, indicating their huge potential for capacitive deionization applications.
