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Abstract Seventy percent of global electricity is generated by steam-cycle power plants. A hydrophobic condenser surface within these plants could boost overall cycle efficiency by 2%. In 2022, this enhancement equates to an additional electrical power generation of 1000 TWh annually, or 83% of the global solar electricity production. Furthermore, this efficiency increase reduces CO₂emissions by 460 million tons /year with a decreased use of 2 trillion gallons of cooling water per year. However, the main challenge with hydrophobic surfaces is their poor durability. Here, we show that solid microscale-thick fluorinated diamond-like carbon (F-DLC) possesses mechanical and thermal properties that ensure durability in moist, abrasive, and thermally harsh conditions. The F-DLC coating achieves this without relying on atmospheric interactions, infused lubricants, self-healing strategies, or sacrificial surface designs. Through tailored substrate adhesion and multilayer deposition, we develop a pinhole-free F-DLC coating with low surface energy and comparable Young’s modulus to metals. In a three-year steam condensation experiment, the F-DLC coating maintains hydrophobicity, resulting in sustained and improved dropwise condensation on multiple metallic substrates. Our findings provide a promising solution to hydrophobic material fragility and can enhance the sustainability of renewable and non-renewable energy sources. 

Abstract Condensation is ubiquitous in nature and industry. Heterogeneous condensation on surfaces is typified by the continuous cycle of droplet nucleation, growth, and departure. Central to the mechanistic understanding of the thermofluidic processes governing condensation is the rapid and high‐fidelity extraction of interpretable physical descriptors from the highly transient droplet population. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. Here, an intelligent vision‐based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio‐temporal resolutions of 300 nm and 200 ms, respectively. The data‐centric analysis conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key tradeoff between heat transfer rate per individual droplet and droplet population density. The vision‐based approach presents a powerful tool for the study of not only phase‐change processes but also any nucleation‐based process within and beyond the thermal science community through the harnessing of big data.