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1. MACHINE LEARNING COMBINED IMAGE AND SENSOR DATA ANALYSIS OF TWO-PHASE MORPHOLOGY AND HEAT TRANSFER DATA FOR DROPLET VAPORIZATION ON A SURFACE AT NEAR CRITICAL HEAT FLUX CONDITIONS

   Tchouteng-Njike, U; Silva, Anisa; Carey, VP (July 2025, ASME)

   ABSTRACT For droplet vaporization on a superheated hydrophilic surface, earlier studies have demonstrated that use of machine learning tools to analyze both image information from high-speed video and digital data from sensors can be an effective path to understanding the physics and developing a useful model to predict performance when the surface superheat is at low to moderate levels. For such conditions, the two-phase morphology of the system is usually well-behaved, exhibiting conduction-dominated film evaporation of the spread droplet, or nucleate boiling at active nucleation sites in the liquid film of the spread droplet. At higher surface superheat levels, experiments have shown that the droplet vaporization process becomes chaotic, with the process alternating between rapid vaporization of liquid in contact with the surface and ejection of liquid off the surface by strong vapor recoil forces. For our experiments with water droplets at atmospheric pressure, this regime corresponds to superheat levels ranging from about 35 to 55 deg. C. At the low superheat end of this regime, extremely high mean heat flux levels are achieved, but as superheat further increases, less of the surface stays wetted due to the increasing vapor recoil forces, and heat flux begins to decrease as the boiling process becomes like transition pool boiling with progressively less of the surface in contact with liquid. This exploration of the use of a specialized convolution neural network (CNN) to simultaneously analyze high speed video images and digital data for this high-superheat, near-critical-heat-flux regime of droplet vaporization is of special interest for two reasons. First, this vaporization regime results in high heat flux levels that make it attractive for high heat flux cooling for high-powered electronics. Use of machine learning tools to learn more about the mechanisms of this vaporization regime may open the door to new high flux thermal management technologies. In addition, because of its complexity, the two-phase morphology of the vaporization process in this regime is expected to be a very challenging task for CNN machine learning tools. In this study we conducted deposited water droplet spreading and vaporization experiments that captured digital data input (measured surface superheat, mean heat flux during the vaporization process, wetting contact angle, droplet size, etc.) and images of the droplet vaporization two-phase morphology from high-speed video during each experiment. This paper summarizes our successful development of a specialized hybrid CNN design that is trained using the combination of digital measurements and images obtained in our experiments. This CNN design provides deep insight into correlation between the two-phase morphology and heat transfer performance for this near critical heat flux vaporization regime. It also provides a pathway to a heat transfer performance model that fits the performance data to a high level of agreement. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat flux with a root mean square percent error of only about 2.0% and 8.0% on a training and testing dataset respectively. The hybrid network developed in this research appears to be a promising strategy for analyzing experimental data for physical systems with complex morphology that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation. 

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2. Perfluorocarbon nanodroplet size, acoustic vaporization, and inertial cavitation affected by lipid shell composition in vitro

   https://doi.org/10.1121/10.0014934  

   Welch, Phoebe J.; Li, David S.; Forest, Craig R.; Pozzo, Lilo D.; Shi, Chengzhi (October 2022, The Journal of the Acoustical Society of America)

   Perfluorocarbon nanodroplets (PFCnDs) are ultrasound contrast agents that phase-transition from liquid nanodroplets to gas microbubbles when activated by laser irradiation or insonated with an ultrasound pulse. The dynamics of PFCnDs can vary drastically depending on the nanodroplet composition, including the lipid shell properties. In this paper, we investigate the effect of varying the ratio of PEGylated to non-PEGylated phospholipids in the outer shell of PFCnDs on the acoustic nanodroplet vaporization (liquid to gas phase transition) and inertial cavitation (rapid collapse of the vaporized nanodroplets) dynamics in vitro when insonated with focused ultrasound. Nanodroplets with a high concentration of PEGylated lipids had larger diameters and exhibited greater variance in size distribution compared to nanodroplets with lower proportions of PEGylated lipids in the lipid shell. PFCnDs with a lipid shell composed of 50:50 PEGylated to non-PEGylated lipids yielded the highest B-mode image intensity and duration, as well as the greatest pressure difference between acoustic droplet vaporization onset and inertial cavitation onset. We demonstrate that slight changes in lipid shell composition of PFCnDs can significantly impact droplet phase transitioning and inertial cavitation dynamics. These findings can help guide researchers to fabricate PFCnDs with optimized compositions for their specific applications. 

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3. Surface tension and evaporation behavior of liquid fuel droplets at transcritical conditions: Towards bridging the gap between molecular dynamics and continuum simulations

   https://doi.org/10.1016/j.fuel.2023.130187  

   Jangale, Prajesh; Hosseini, Ehsan; Zakertabrizi, Mohammad; Jarrahbashi, Dorrin (November 2023, Fuel)

   The phase transition from subcritical to supercritical conditions, referred to as transcritical behavior, significantly impacts the evaporation and fuel–air mixing in high-pressure liquid-fuel propulsion systems. Transcritical behavior is characterized as a transition from classical two-phase evaporation to a single-phase gas-like diffusion regime as surface tension and latent heat of vaporization reduce. However, the interfacial behavior represented by the surface tension coefficient and evaporation rate during this transition which are crucial inputs for Computational Fluid Dynamics (CFD) simulations of practical transcritical fuel spray is still missing. This study aims at developing new evaporation rate and surface tension models for transcritical n-dodecane droplets using molecular dynamics (MD) simulations irrespective of the droplet size. As MD simulations are primarily limited to the nanoscale, the new models can bridge the gap between MD and continuum simulations and enable the direct application of these findings to microscopic droplets. A new characteristic timescale, i.e., “undroplet time,” is defined which marks the transition from classical two-phase evaporation to single-phase gas-like diffusion behavior. The undroplet time indicates the onset of droplet core disintegration and penetration of nitrogen molecules into the droplet, which occurs after the vanishment of the surface tension. By normalizing the time with respect to the undroplet time, the rate of surface tension decay, evaporation rate, and the rate of droplet mass depletion become independent of the droplet size. Calculation of pairwise correlation coefficients for the entire MD results shows that both surface tension coefficient and evaporation rate are strongly correlated with the background temperature, while pressure and droplet size play a less significant role past the critical point. Therefore, new models for surface tension coefficient and evaporation rate spanning from sub- to supercritical conditions are developed as a function of background pressure and temperature, which can be used in continuum simulations. The identified phase change behavior based on the undroplet time shows a good agreement with the phase change regime maps obtained using microscale experiments and nanoscale MD predictions. 

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4. Emulsion imaging of a DNA nanostar condensate phase diagram reveals valence and electrostatic effects

   https://doi.org/10.1063/5.0130808  

   Conrad, Nathaniel; Chang, Grace; Fygenson, Deborah K.; Saleh, Omar A. (December 2022, The Journal of Chemical Physics)

   Liquid–liquid phase separation (LLPS) in macromolecular solutions (e.g., coacervation) is relevant both to technology and to the process of mesoscale structure formation in cells. The LLPS process is characterized by a phase diagram, i.e., binodal lines in the temperature/concentration plane, which must be quantified to predict the system’s behavior. Experimentally, this can be difficult due to complications in handling the dense macromolecular phase. Here, we develop a method for accurately quantifying the phase diagram without direct handling: We confine the sample within micron-scale, water-in-oil emulsion droplets and then use precision fluorescent imaging to measure the volume fraction of the condensate within the droplet. We find that this volume fraction grows linearly with macromolecule concentration; thus, by applying the lever rule, we can directly extract the dense and dilute binodal concentrations. We use this approach to study a model LLPS system of self-assembled, fixed-valence DNA particles termed nanostars (NSs). We find that temperature/concentration phase diagrams of NSs display, with certain exceptions, a larger co-existence regime upon increasing salt or valence, in line with expectations. Aspects of the measured phase behavior validate recent predictions that account for the role of valence in modulating the connectivity of the condensed phase. Generally, our results on NS phase diagrams give fundamental insight into limited-valence phase separation, while the method we have developed will likely be useful in the study of other LLPS systems. 

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5. Phase Separation and Protein Partitioning in Compartmentalized Cell-Free Expression Reactions

   https://doi.org/10.1021/acs.biomac.1c00546  

   Kato, Shuzo; Garenne, David; Noireaux, Vincent; Maeda, Yusuke T. (July 2021, Biomacromolecules)

   null (Ed.)

   Liquid-liquid phase separation (LLPS) is important to control a wide range of reactions from gene expression to protein degradation in a cell-sized space. To bring a better understanding of the compatibility of such phase-separated structures with protein synthesis, we study emergent LLPS in a cell-free transcription-translation (TXTL) reaction. When the TXTL reaction composed of many proteins is concentrated, the uniformly mixed state becomes unstable, and membrane-less phases form spontaneously. This LLPS droplet formation is induced when the TXTL reaction is enclosed in water-in-oil emulsion droplets, in which water evaporates from the surface. As the emulsion droplets shrink, smaller LLPS droplets appear inside the emulsion droplets and coalesce into large phase-separated domains that partition the localization of synthesized reporter proteins. The presence of PEG in the TXTL reaction is important not only for versatile cell-free protein synthesis but also for the formation of two large domains capable of protein partitioning. Our results may shed light on the dynamic interplay of LLPS formation and cell-free protein synthesis toward the construction of synthetic organelles. 

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