The paper is related to the use of magnetic nanofluids (ferrofluids) in a direct absorption solar parabolic trough collector, which enhances thermal efficiency compared to conventional solar collectors. By applying the right magnetic intensity and magnetic field direction, the thermal conductivity of the fluid increased higher than typical nanofluids. Moreover, the ferrofluids exhibit excellent optical properties. The external magnetic source is installed to alter the thermo-physical properties of the fluid, and the absorber tube does not have selective surface allowing ferrofluids to absorb the incoming solar irradiance directly. In this paper, an experimental investigation of the performance of small scale direct absorption solar collector using ferrofluids as an absorber was conducted. Nanoparticle concentrations of 0.05 vol% at the operational temperatures between 19°C and 40°C were used in the current study. The results show that using ferrofluids as a heat transfer fluid increases the efficiency of solar collectors. In the presence of the external magnetic field, the solar collector efficiency increases to the maximum, 25% higher than the conventional parabolic trough. At higher temperatures, the ferrofluids show much better efficiency than conventional heat transfer fluid. The study indicated that nanofluids, even of low-content, have good absorption of solar radiation, and can improve the outlet temperatures and system efficiencies. The study shows the potential of using ferrofluids in the direct absorption solar collector.

The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s⁻¹). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R² values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids.

This research focuses on developing and optimizing mixed matrix membranes (MMMs) by incorporating graphene oxide (GO) into a polysulfone (PSF) matrix to enhance the separation performance of CO₂ and CH₄. The morphology and gas separation performance of the MMMs were systematically characterized. The incorporation of GO enhanced gas permeation and CO₂/CH₄ selectivity, as evaluated using a gas permeation setup. Notably, the PSF/GO-0.3 wt.% membrane exhibited superior performance, achieving a CO₂ permeability of 21.63 Barrer, among the highest reported for PSF-based MMMs. Additionally, the membrane demonstrated a CO₂/CH₄ selectivity of 14.32, highlighting its effectiveness in distinguishing between the two gases, which is essential for carbon capture and natural gas decontamination applications. The uniform distribution of GO within the polymer matrix contributed to the membrane’s enhanced performance. Furthermore, the MMMs exhibited outstanding resistance to CO₂ plasticization, with the PSF/GO-0.3 wt.% membrane maintaining its performance at pressures up to 10 bar, a significant improvement over the pristine PSF membrane, which failed at 4 bar. The improved plasticization resistance is ascribed to the reinforcing effect of GO, which stabilizes the polymer matrix, minimizing CO₂-induced swelling. The PSF/GO-0.3 wt.% membrane exhibited exceptional CO₂ permeability, selectivity, and plasticization resistance, making it a viable alternative for industrial gas separation applications and outperforming previously reported PSF-based MMMs.

The present study comprises the modeling and optimization of the thermal and viscous properties of nanodiamond–nickel (Ni) particle-based nanofluid in ethylene glycol. The temperature and nanoparticle weight percentage are selected as the process variables, which are considered crucial for the operational condition of the application and the economic factor. The impact of these process variables was investigated on thermal conductivity and viscosity simultaneously using response surface methodology (RSM). The models for thermal conductivity and viscosity were developed and validated using experimentally measured property data. The validated model was further used for the prediction. A detailed multi-objective optimization study was conducted to maximize thermal conductivity and minimize viscosity. The optimum results suggested that the maximum values for thermal conductivity and viscosity of nanofluids were estimated to be 0.282 Wm/°C and 5.867 mPa·s, respectively. The optimum values for the input parameters such as temperature and nanodiamond–Ni concentration were calculated to be 60°C and 2.998 wt.%, respectively. The coefficient of determination R² for the developed model showed 0.9971 and 0.9975 for thermal conductivity and viscosity, respectively.