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Coalescence‐induced droplet jumping phenomena on superhydrophobic surfaces can significantly enhance their heat transfer performances by effectively removing droplets from the surfaces. However, understanding the ideal design for condensing surfaces is still challenging due to the complex nature of droplet dynamics associated with their nucleation, coalescing, and jumping mechanisms. The intrinsic dynamic nature of droplet behaviors suggests the use of hierarchical concave morphology to account for the different length scales associated with each transport phenomenon. The hierarchical morphology thereby enables heterogeneous wetting characteristics by realizing both microscale droplets on superhydrophobic surfaces and nanoscale pinning regions beneath the droplets by arresting liquid residues after droplet jumping. Heat transfer performances are further examined by extracting physically meaningful descriptors, such as nucleation sites, droplet growth rates, and droplet jumping frequency, showing 44% enhancements when droplet nucleation sites are designed in selective locations.

 

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.

 

Understanding phase transition between the liquid and gaseous states has gained significant interest, and has been ubiquitously observed in many places ranging from natural systems to water–energy nexus and thermal management applications. Phase transition phenomena at liquid–vapor interfaces are greatly governed by intermolecular‐level kinetics, which requires the use of empirical parameters in continuum‐level relations to explain the discrete nature of molecular particles. Despite its significance, it has been a great challenge to find detailed expressions of empirical parameters such as accommodation coefficients, which represent the probabilities for phase transition of liquid or vapor molecules at the interface. Here, direct statistical measurements of accommodation coefficients are reported by tracking the trajectories of liquid and vapor molecules in molecular simulations. The measurements reveal that evaporation and condensation coefficients are different by ≈50%, whereas they have been assumed to be equal in most previous studies. Then, the indirect measurement method is studied from a perspective of theoretical genetics based on the diffusion approximation. A good agreement between two approaches suggests that diffusion approximation can contribute to provide empirical parameters with a cost‐effective method.

 

As modern electronics continuously exceed their performance limits, there is an urgent need to develop new cooling devices that balance the increasing power demands. To meet this need, cutting-edge cooling devices often utilize microscale structures that facilitate two-phase heat transfer. However, it has been difficult to understand how microstructures trigger enhanced evaporation performances through traditional experimental methods due to low spatial resolution. The previous methods can only provide coarse interpretations on how physical properties such as permeability, thermal conduction, and effective surface areas interact at the microscale to effectively dissipate heat. This motivates researchers to develop new methods to observe and analyze local evaporation phenomena at the microscale.

Herein, we present techniques to characterize submicron to macroscale evaporative phenomena of microscale structures using micro laser induced fluorescence (μLIF). We corroborate the use of unsealed temperature-sensitive dyes by systematically investigating their effects on temperature, concentration, and liquid thickness on the fluorescence intensity. Considering these factors, we analyze the evaporative performances of microstructures using two approaches. The first approach characterizes local or overall evaporation rates by measuring the solution drying time. The second method employs an intensity-to-temperature calibration curve to convert temperature-sensitive fluorescence signals to surface temperatures. Then, submicron-level evaporation rates are calculated by employing a species transport equation for vapor at the liquid-vapor interface. Using these methods, we reveal that capillary-assisted liquid feeding dominates evaporation phenomena on microstructured surfaces. This study will enable engineers to decompose the key thermofluidic parameters contributing to the evaporative performance of microscale structures.

 

Abstract Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology. 

Abstract As the heat generation at device footprint continuously increases in modern high-tech electronics, there is an urgent need to develop new cooling devices that balance the increasing power demands. To meet this need, cutting-edge cooling devices often utilize microscale structures that facilitate two-phase heat transfer. However, it has been difficult to understand how microstructures enhance evaporation performances through traditional experimental methods due to low spatial resolution. The previous methods can only provide coarse interpretations on how physical properties such as permeability, thermal conduction, and effective surface areas interact at the microscale to effectively dissipate heat. This motivates researchers to develop new methods to observe and analyze local evaporation phenomena at the microscale. Herein, we present techniques to characterize submicron to macroscale evaporative phenomena of microscale structures by using microlaser-induced fluorescence (μLIF). We corroborate the use of unsealed temperature-sensitive dyes by systematically investigating the effects of temperature, concentration, and liquid thickness on the fluorescence intensity. Considering these factors, we analyze the evaporative performances of microstructures using two approaches. The first approach characterizes the overall and local evaporation rates by measuring the solution drying time. The second approach employs an intensity-to-temperature calibration curve to convert temperature-sensitive fluorescence signals to surface temperatures, which calculates the submicron-level evaporation rates. Using these methods, we reveal that the local evaporation rate between microstructures is high but is balanced with a large capillary-feeding. This study will enable engineers to decompose the key thermofluidic parameters contributing to the evaporative performance of microscale structures.