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Abstract Two types of highly stable 0.1% graphene oxide-based aqueous nanofluids were synthesised and investigated. The first nanofluid (GO) was prepared under the influence of ultrasonic irradiation without surfactant. The second nanofluid was treated with tetra ethyl ammonium hydroxide to reduce the graphene oxide to form reduced graphene oxide (RGO) during ultrasonic irradiation. The GO and RGO powders were characterised by various techniques such as field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction and Raman. Also UV–visible absorption spectroscopy was carried out and band gap energies were determined. Optical band gap energies for indirect transitions ranged from 3.4 to 4.4 eV and for direct transitions they ranged between 2.2 and 3.7 eV. Thermal conductivity measurements of the GO-based aqueous nanofluid revealed an enhancement of 9.5% at 40 °C compared to pure water, while the RGO-based aqueous nanofluid at 40 °C had a value 9.23% lower than pure water. Furthermore, the photothermal response of the RGO-based aqueous nanofluid had a temperature increase of 13.5 °C, (enhancement of 60.2%) compared to pure water, the GO-based aqueous nanofluid only displayed a temperature rise of 10.9 °C, (enhancement of 46.6%) after 20 min exposure to a solar irradiance of 1000 W m−2. Both nanofluid types displayed good long-term stability, with the GO-based aqueous nanofluid having a zeta potential of 30.3 mV and the RGO-based aqueous nanofluid having a value of 47.6 mV after 6 months. The good dispersion stability and photothermal performance makes both nanofluid types very promising working fluids for low-temperature direct absorption solar collectors.

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

The global demand for energy is increasing and the detrimental consequences of rising greenhouse gas emissions, global warming and environmental degradation present major challenges. Solar energy offers a clean and viable renewable energy source with the potential to alleviate the detrimental consequences normally associated with fossil fuel-based energy generation. However, there are two inherent problems associated with conventional solar thermal energy conversion systems. The first involves low thermal conductivity values of heat transfer fluids, and the second involves the poor optical properties of many absorbers and their coating. Hence, there is an imperative need to improve both thermal and optical properties of current solar conversion systems. Direct solar thermal absorption collectors incorporating a nanofluid offers the opportunity to achieve significant improvements in both optical and thermal performance. Since nanofluids offer much greater heat absorbing and heat transfer properties compared to traditional working fluids. The review summarizes current research in this innovative field. It discusses direct solar absorber collectors and methods for improving their performance. This is followed by a discussion of the various types of nanofluids available and the synthesis techniques used to manufacture them. In closing, a brief discussion of nanofluid property modelling is also presented.