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  1. Cher Pin, S., Rashmi, W., Khalid, M., Chong, C.H., Woo, M.W., Tee, L.H.
    MyJurnal
    The drying of Piper betle Linn (betel) leaf extract using a lab scale spray dryer was simulated using Computational Fluid Dynamics (CFD). Three different turbulent models (standard k-ε, RNG k-ε and realizable k-ε) were used in the present study to determine the most suitable model for predicting the flow profile. Parametric studies were also conducted to evaluate the effect of process variables on the final moisture content. Four different initial droplet sizes (36, 79, 123 and 166 μm) were tested with four sets of combination of hot air temperature (140 and 160°C) and feed rate (4, 9.5 and 15 ml/min). It was found that standard k-ε is the most suitable turbulent model to predict the flow behaviour Moreover, the lowest final moisture content present in samples was obtained at 140°C and a feed rate of 15.0 ml/min.
  2. Fang YK, Osama M, Rashmi W, Shahbaz K, Khalid M, Mjalli FS, et al.
    Nanotechnology, 2016 Feb 19;27(7):075702.
    PMID: 26766874 DOI: 10.1088/0957-4484/27/7/075702
    This study introduces a new class of heat transfer fluids by dispersing functionalised graphene oxide nanoparticles (GNPs) in ammonium and phosphonium-based deep eutectic solvents (DESs) without the aid of a surfactant. Different molar ratios of salts and hydrogen bond donors (HBD) were used to synthesise DESs for the preparation of different concentrations of graphene nanofluids (GNFs). The concentrations of GNPs were 0.01 wt%, 0.02 wt% and 0.05 wt %. Homogeneous and stable suspensions of nanofluids were obtained by high speed homogenisation and an ultrasonication process. The stability of the GNFs was determined through visual observation for 4 weeks followed by a centrifugal process (5000-20,000 rpm) for 30 min in addition to zeta potential studies. Dispersion of the GNPs in DES was observed using an optical microscope. The synthesised DES-based GNFs showed no particle agglomeration and formation of sediments in the nanofluids. Thermo-physical properties such as thermal conductivity and specific heat of the nanofluids were also investigated in this research. The highest thermal conductivity enhancement of 177% was observed. The findings of this research provide a new class of engineered fluid for heat transfer applications as a function of temperature, type and composition DESs as well as the GNPs concentration.
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