Bio-inspired superhydrophobic coatings have garnered significant attention in recent years due to their potential in creating self-cleaning and anti-icing surfaces. Drawing inspiration from natural systems such as lotus leaves and insect wings, these coatings leverage hierarchical nanostructures to achieve extreme water repellency and low surface adhesion. This review explores recent advances in the design, fabrication, and functional performance of bio-inspired superhydrophobic materials, with a focus on hierarchical micro/nanostructured surfaces. We discuss the underlying mechanisms of wettability, the role of surface chemistry, and the integration of durable nanostructures for enhanced durability. Additionally, the paper discusses the latest progress in scalable manufacturing techniques, environmental adaptability, and multifunctional performance, particularly in self-cleaning and anti-icing applications. Emerging trends, such as stimuli-responsive surfaces and smart coatings, are also examined to provide a comprehensive overview of the field. This review discusses the challenges and future directions for translating laboratory-scale innovations into real-world applications, particularly in aerospace, automotive, energy, and infrastructure sectors.

Sectoral exergy studies of transport usually end with a single projected path and no stated uncertainty. In the current study, we develop a calibrated framework to address this and apply it to the Nigerian transport sector. First, we show that exergy-based indicators reduce to one parameter, the subsector exergy efficiency, so that they agree by construction. Next, we forecast fuel demand with an equal weight combination of a Gaussian process trend model and a drift model. Rolling origin backtests show that drift remains the best point forecaster, while the combination gives the best interval score. One scale factor calibrates the 90% intervals to 90.3% coverage, 88.3% on held-out origins, with no widening needed at long horizons. Independent national statistics for 2020 to 2023, never used in fitting, fall inside the 90% intervals in all four years. After updating with these data, Monte Carlo propagation gives 81 Mt of CO₂ in 2040 (90% interval 34 to 123 Mt) and an improvement potential of 695 PJ (294 to 1028 PJ). This potential is a thermodynamic upper bound, not an achievable saving. Demand drives almost all CO₂ uncertainty. Correlated demand, interactions and changing efficiencies alter them only modestly.