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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 Durable hydrophobic materials have attracted considerable interest in the last century. Currently, the most popular strategy to achieve hydrophobic coating durability is through the combination of a perfluoro-compound with a mechanically robust matrix to form a composite for coating protection. The matrix structure is typically large (thicker than 10 μm), difficult to scale to arbitrary materials, and incompatible with applications requiring nanoscale thickness such as heat transfer, water harvesting, and desalination. Here, we demonstrate durable hydrophobicity and superhydrophobicity with nanoscale-thick, perfluorinated compound-free polydimethylsiloxane vitrimers that are self-healing due to the exchange of network strands. The polydimethylsiloxane vitrimer thin film maintains excellent hydrophobicity and optical transparency after scratching, cutting, and indenting. We show that the polydimethylsiloxane vitrimer thin film can be deposited through scalable dip-coating on a variety of substrates. In contrast to previous work achieving thick durable hydrophobic coatings by passively stacking protective structures, this work presents a pathway to achieving ultra-thin (thinner than 100 nm) durable hydrophobic films. 

Droplet nucleation and condensation are ubiquitous phenomena in nature and industry. Over the past century, research has shown dropwise condensation heat transfer on nonwetting surfaces to be an order of magnitude higher than filmwise condensation heat transfer on wetting substrates. However, the necessity for nonwetting to achieve dropwise condensation is unclear. This article reports stable dropwise condensation on a smooth, solid, hydrophilic surface (θ a = 38°) having low contact angle hysteresis (<3°). We show that the distribution of nano- to micro- to macroscale droplet sizes (about 100 nm to 1 mm) for coalescing droplets agrees well with the classical distribution on hydrophobic surfaces and elucidate that the wettability-governed dropwise-to-filmwise transition is mediated by the departing droplet Bond number. Our findings demonstrate that achieving stable dropwise condensation is not governed by surface intrinsic wettability, as assumed for the past eight decades, but rather, it is dictated by contact angle hysteresis. 

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.