• Energy Research

Abstract Covalent Functionalized-Multi wall carbon nanotubes (CF-MWCNTs) and Covalent Functionalized-graphene nanoplatelets (CF-GNPs) with hexagonal boron nitride (h-BN) were suspended in distilled water to prepare the hybrid nanofluids as working fluids inside the Flat Plate Solar Collector (FPSC). Different concentrations of the hybrid nanoparticles were considered and Tween-80 (Tw-80) was used as a surfactant. The stability and thermophysical properties were tested using different measurement tools. The structural and morphological properties were examined using FTIR, XRD, UV–vis spectrometry, HRTEM, FESEM, and EDX. The thermal efficiency of FPSC were tested under different volumetric flow rates (2 L/min, 3 L/min, and 4 L/min), whereas the efficiency of the collector was determined based on ASHRAE standard 93-2010. As a result, the most thermal-efficient solar collector improved up to 85% with hybrid nanofluid as the absorption medium at 4 L/min flow rate. Increment in nanoparticles’ concentrations enhanced thermal energy gain and resulted in higher fluid outlet temperature.

The bubble point pressure (P b) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model's reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.

Abstract In the attempt of preparing multi-walled carbon nanotube (MWCNTs), covalent functionalisation (CF-MWCNTs) were applied. The stable thermal conductivity was measured as a function of temperature. A number of techniques, such as FTIR, FESEM and UV-vis spectrophotometer were employed to characterise both dispersion stability and morphology of functionalised materials. By using ultrasonic test time, the highest stability of nanofluids was achieved at 60 minutes. As a result, the thermal conductivity displayed by CF-MWCNTs was higher than distilled water. In conclusion, improvement in thermal conductivity and stability displayed by CF-MWCNTs was higher, while the best thermal conductivity improvement was recorded at 31%.

Abstract Dye-Sensitized Solar Cells have emerged as solar technology for practical application as building-integrated photovoltaic (BIPV) due to low-cost processing, working ability in the ambient environment, and colour transparency. The colour transparency is inherited from the nanostructured mesoporous TiO2 and colourful dye. However, mesoporous TiO2 possesses poor electron transport properties due to multiple grain boundaries, resulting in undesirable recombination reactions that lead to the loss of photoconversion efficiency (PCE). Reduced graphene oxide (rGO) is incorporated in the TiO2 matrix to improve its electron transport properties. Nevertheless, rGO cannot give full benefit of graphene due to its defective structure, which promotes recombination. Therefore, here a solvent exfoliated high-quality graphene in mesoporous TiO2 photoelectrode having mixed phase composition anatase, rutile, and brookite phase is introduced. Graphene/TiO2 hybrid structures are produced with different graphene loading, and their structural, optical, and electron transport properties are thoroughly investigated. Electrochemical impedance spectroscopy (EIS) indicates that electron lifetime increases by 159%, electron diffusion length increases by 25%, while recombination rate reduces by 62.77% than the pure TiO2 at optimum graphene loading (0.0025 wt%). Consequently, an increase of 18.45% and 30.89% PCE is obtained relative to pure TiO2 and commercial paste, respectively.

Covalent functionalization (CF-GNPs) and non-covalent functionalization (NCF-GNPs) approaches were applied to prepare graphene nanoplatelets (GNPs).