Kilometer-scale continuous graphene fibers (GFs) with outstanding mechanical properties and excellent electrical conductivity are produced by high-throughput wet-spinning of graphene oxide liquid crystals followed by graphitization through a full-scale synergetic defect-engineering strategy. GFs with superior performances promise wide applications in functional textiles, lightweight motors, microelectronic devices, and so on.

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A new method using nonthermal plasma to add Cl active sites on activated carbon was proposed to improve the efficiency of activated carbon (AC) for removal of mercury from flue gas. The experiments were conducted via a lab-scale dielectric barrier discharge nonthermal plasma system and a vertical adsorption reactor. The results showed that the nonthermal plasma treatment with a small amount of Cl successfully added Cl active sites on AC and greatly increased the mercury removal efficiency of AC by chemisorption in a very short treatment time. The increase in Cl concentration for AC treatment promoted the efficiency of AC. The capacity of mercury adsorption positively correlated with the content of Cl for AC treatment, which depends on the number of Cl active sites on activated carbon. The treated AC maintained a high mercury removal efficiency within a temperature range of 30-210 °C. SO and HO in flue gas inhibited the removal of mercury by AC, while HCl had a promotional effect. Scanning electron microscopy and X-ray photoelectron spectroscopy analysis indicated the chemisorption of mercury was attributed to the C-Cl groups generated on AC surfaces during Cl nonthermal plasma treatment. The C-Cl groups as active sites had strong adsorption energy for mercury, which converted elemental mercury to HgCl.

In this paper we report a new model for predicting the thermal conductivity of nanofluids by taking into account the fractal distribution of nanoparticle sizes and heat convection between nanoparticles and liquids due to the Brownian motion of nanoparticles in fluids. The proposed model is expressed as a function of the average size of nanoparticles, fractal dimension, concentration of nanoparticles, temperature and properties of fluids. The model shows the reasonable dependences of the thermal conductivity on the temperature of nanofluids, nanoparticle size and concentration. The parameter c introduced in thermal boundary layer depends on fluids, but is independent of nanoparticles added in the fluids. The model predictions are in good agreement with the available experimental data. The model also reveals that there is a critical concentration of 12.6% of nanoparticles at which the contribution from heat convection due to the Brownian movement of nanoparticles reaches the maximum value, below which the contribution from heat convection decreases with the decrease in concentration and above which the contribution from heat convection decreases with the increase in concentration.

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