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Abstract Combining high-speed video cameras and optical measurement techniques with digital sensors controlled by a data acquisition system can provide an effective means of exploring boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and other sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network (CNN) to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Two specialized CNN models were developed in this study that can simultaneously analyze both image and digital data. One of our CNN model designs (case B) was trained to take an image of the vaporization process and nonthermal digital data as input and predict thermal heat transfer performance. This model predicts performance remarkably well given its nonthermal inputs, matching independent heat flux test data to a root-mean-square percent error (RMSPE) of 10.3%. This model appears to learn how the variations of nucleate boiling, vapor recoil activity, and local dryout over the surface vary with surface temperature and/or heat flux from changes in boiling system images. We also describe a CNN model (case C) that takes digital nonthermal data, digital thermal data, and image information and provides a high-fidelity prediction of vaporization heat transfer performance. This model predicted performance very well—better than our conventional fit to data (case A) and on par with best fits to quality nucleate boiling heat transfer data in the literature. This type of trained model fit independent heat flux test data to an RMSPE of 5.8%. Our results indicate that training this type of model which predicts performance from input image information and digital operating condition thermal data makes the resulting predictive model more accurate and robust. The successful use of the hybrid CNN models described here suggests that there is a strong correlation between two-phase morphology variations and changes in heat transfer performance. The hybrid CNN modeling approach developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with combined use of imaging and digital sensor instrumentation. Possible use of this type of modeling in other systems is also discussed. 

Pseudoernestia (Melastomataceae: Marcetieae) is a genus comprising two species, P. cordifolia and P. glandulosa, with disjunct distributions along the Amazon Rainforest. The genus is characterized by narrow sepals, petals bearing an apical glandular trichome, antesepalous stamens with aristate ventral appendages and calcarate dorsal appendages positioned opposite the ventral ones, a glabrous ovary with three locules, rarely two, and seeds lacking dorsolateral projections. We present a taxonomic review of Pseudoernestia, including a lectotypification for P. cordifolia, the type species of the genus. Based on herbarium data, we confirmed that P. cordifolia occurs in Venezuela and Brazil, typically on rocky outcrops, whereas P. glandulosa is found in French Guiana and Guyana, primarily on lateritic soils. Pseudoernestia cordifolia is categorized as Endangered, while P. glandulosa is assessed as Least Concern. We provide detailed descriptions, photographs of living specimens, scanning electron microscopy images of the seeds, geographic distribution maps, and notes on conservation status, habitat preferences, and phenology. 

ABSTRACT Bubble nucleation associated with nucleate boiling at superheated surfaces has typically focused on surfaces with features on the order of microns and bubble embryos with comparable interface radii of curvature. For such surfaces the vapor embryo growth or collapse behavior is consistent with surface tension and wetting forces being confined to the contact line region at the surface. In a pure fluid saturating a superheated nanopororus layer, random density fluctuations can lead to the formation of nanoscale bubble embryos. Said fluctuations increase as the liquid is superheated and can lead to macroscopic nucleation. Entrapment of gas when nanostructured surfaces are flooded with liquid can also result in nanoscale bubble embryos in or near the porous layer. For highly wetted nanostructured surfaces, the fluid-to-surface attractive forces are strong over much of a nanobubble embryo, and the critical bubble size that results in spontaneous bubble growth is affected more strongly by surface forces. A Lattice Boltzmann model (LBM) is used to simulate the time evolution behavior of bubble embryos, with radii ranging from 5 to 15 nanometers, close to or within nanoscale interstitial spaces of a nanostructured surface. Single vapor nanobubbles are seeded in surrounding fluid with varying degrees of contact with solid surfaces to simulate smooth or nanostructured surfaces. The effects of varying adsorption coefficient (which dictates contact angle), varying bubble surface radius of curvature, mean distance of wall nanostructures from the embryo, and varying degrees of enclosure of the embryo by surrounding wall structures are explored. The simulation results indicate that the critical radius is largely impacted by the proximity of nanostructures, demonstrating how the fluid-surface forces affect the stability of a vapor embryo. The results suggest that the hydrophilic nature of the surfaces contributes to the suppression in the onset of nucleate boiling which is often seen in hydrophilic nanoporous layers. The implications of these results on convective and nucleate boiling at and within nanostructured surfaces are also discussed. 

Abstract Nanostructured hydrophilic surfaces can enhance boiling processes due to the liquid wicking effect of the small surface structures. However, consistently uniform nanoscale interstitial spaces would require high superheat to initiate heterogeneous nucleation in the available small cavity spaces. Experimental studies indicate that surfaces of this type initiate onset of nucleate boiling at relatively low superheat levels, implying that significantly larger interstitial spaces exist, apparently as a consequence of the fabrication process. To explore the correlation between nanostructured surface morphology variations and variation of nucleation behavior with superheat, in this study, a zinc oxide nanostructured coating was fabricated on various copper substrates for wetting and droplet vaporization heat transfer experiments and morphology analysis. Our experiments determined the variation of mean droplet heat flux with superheat, and high-speed videos documented how nucleation features varied with superheat. Image analysis of the electron microscopy images was used to assess the variability of pore size and surface complexity (entropy) over the surface. Our data demonstrates the correlation between surface morphology feature distributions and the variation of nucleate boiling active site density with superheat. Specifically, our results indicate that increased availability of larger-scale surface irregularities with low surface entropy corresponds to enhanced probability of nucleation onset and an increase in active nucleation site density as superheat increases. This information can help guide development of enhanced boiling surfaces by providing insight into the nanosurface feature density distributions that enhance nucleation onset while also providing enhanced wicking and low contact angle over most of the surface. 

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. 

We present a high-speed underwater optical backscatter communication technique based on acousto-optic light steering. Our approach enables underwater assets to transmit data at rates potentially reaching hundreds of Mbps, vastly outperforming current state-of-the-art optical and underwater backscatter systems, which typically operate at only a few kbps. In our system, a base station illuminates the backscatter device with a pulsed laser and captures the retroreflected signal using an ultrafast photodetector. The backscatter device comprises a retroreflector and a 2 MHz ultrasound transducer. The transducer generates pressure waves that dynamically modulate the refractive index of the surrounding medium, steering the light either toward the photodetector (encodingbit1) or away from it (encodingbit0). Using a 3-bit redundancy scheme, our prototype achieves a communication rate of approximately 0.66 Mbps with an energy consumption of ≤ 1 μJ/bit, representing a 60× improvement over prior techniques. We validate its performance through extensive laboratory experiments in which remote underwater assets wirelessly transmit multimedia data to the base station under various environmental conditions. 

Abstract DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, have advanced our understanding but often struggle to capture these rare conformational states due to high computational demands. Here, we introduce a novel AI framework based on dynamical graphical models (DGMs), a generative machine learning approach trained on equilibrium MD data, to predict DNA conformational transitions that are never seen in the MD ensembles. By leveraging local DNA interactions, DGMs generate a comprehensive transition matrix that captures both thermodynamic and kinetic properties of unsampled states, enabling accurate predictions of rare global conformations without the need for extensive sampling. Applying this model to the B→A transition, we demonstrate that DGMs can efficiently predict sequence-dependent A-DNA preferences, achieving results that align closely with replica exchange umbrella sampling simulations. DGMs provide new insights into DNA sequence–structure relationships, paving the way for applications in DNA sequence design and optimization.