A combination of thermoplastics and natural fiber reinforcements is considered an ideal choice to mitigate environmental impacts and enhance recyclability or reusability. Chemical treatments are often employed to enhance the thermomechanical properties of natural fiber-reinforced plastics. Nevertheless, it is of paramount importance to assess the techno-economic impact of such chemical treatments and environmentally friendly materials for their implementation in mass productions on an industrial scale. In this work, high-density polyethylene is reinforced with sodium hydroxide (NaOH)-treated and untreated flax fibers to study its impact on mechanical and environmental properties. The composites treated with NaOH exhibited a 37% increase in tensile strength. However, life cycle assessment performed on the NaOH-treated samples showed that they had a global warming potential of 5.8 kg of CO₂, a terrestrial acidification potential of 0.0269 kg of SO₂, and a human carcinogenic toxicity of 0.031 kg of 1,4-DCB compared to the untreated samples. In summary, the techno-environmental analysis reveals a novel approach to identifying chemical treatments based on their technical and environmental effects.

As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Random Forest Regressor, Linear Regression Model, Artificial Neural Network, k-Nearest Neighbors, Support Vector Regression, Gradient Boosting Regressor, Gaussian Process Regression, and Deep Learning, as to their use in forecasting direct solar radiation across six climatically diverse regions in the Kingdom of Saudi Arabia. The models were evaluated using eight statistical metrics along with time-series and absolute error analyses. A key contribution of this work is the introduction of Trigonometric Cyclical Encoding, which has significantly improved temporal representation learning. Comparative SHAP-based feature-importance analysis revealed that Trigonometric Cyclical Encoding enhanced the explanatory power of temporal features by 49.26% for monthly cycles and 53.30% for daily cycles. The findings show that Deep Learning achieved the lowest root mean square error, as well as the highest coefficient of determination, while Artificial Neural Network demonstrated consistently high accuracy across the sites. Support Vector Regression performed optimally but was less reliable in some regions. Error and time-series analyses reveal that Artificial Neural Network and Deep Learning maintained stable prediction accuracy throughout high solar radiation seasons, whereas Linear Regression, Random Forest Regressor, and k-Nearest Neighbors showed greater fluctuations. The proposed Trigonometric Cyclical Encoding technique further enhanced model performance by maintaining the overall fitness of the models, which ranged between 81.79% and 94.36% in all scenarios. This paper supports the effective planning of solar energy and integration in challenging climatic conditions.

Polycarbonate sheets are optically transparent and have the potential to be used as one of the cover materials for PV applications. Solution treatment of polycarbonate surfaces enables to create surface texture topology giving rise to a hydrophobic state, which is favorable for self-cleaning applications. In the present study, hydrophobization of polycarbonate surface is investigated via crystallization of surface by a one-step process. The influence of texture topology, which is created via crystallization, on water droplet mobility and optical transmittance is examined. Findings revealed that solution treatment, using acetone, results in crystallized polycarbonate surfaces with a hydrophobic state. Depending on the treatment duration, the texture characteristics of crystallized surface change while influencing the water contact angle hysteresis. This in turn affects the droplet mobility over the inclined crystallized surface and alters the UV visible transmittance. Moreover, the droplet mobility improves and dust mitigation rates from the treated surface increase as the solution treatment duration are reduced to 2 min. Oil impregnated samples result in improved UV visible transmittance; however, droplet motion changes from rolling to sliding over the surface. A sliding water droplet enables the removal of the dust particles from the oil-impregnated sample surface.