Flow Condensation Heat Transfer Characteristics of Nanochannels with Nanopillars: A Molecular Dynamics Study

Flow condensation in nanochannels is a high-efficiency method to deal with increasingly higher heat flux from micro/nanoelectronic devices. Here, we study the flow condensation heat transfer characteristics of nanochannels with different nanopillar cross-sectional areas and heights using molecular dynamics simulation. Results show that two phases containing vapor in the middle of the channel and liquid near walls can be distinguished by obvious interfaces when the fluid is at a stable state. The condensation performance can be promoted by adding nanopillars. With the increase in nanopillar cross-sectional areas or heights, the time that the fluid spends to reach stability will be put off, while the condensation performance enhances. Different from the small enhancement of nanopillar cross-sectional areas, the condensation heat transfer performance improves significantly at a higher nanopillar height, which increases the heat transfer rates by 11.6 and 35.8% when heights are 6a and 8a, respectively. The preeminent condensation heat transfer performance is ascribed to the fact that nanopillars with a higher height disturb the vapor–liquid interface and vapor region, which not only allows vapor atoms with strong Brownian motion to collide with nanopillar atoms directly but also increases deviations of vapor–liquid potential energy to facilitate condensation heat transfer in nanochannels

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells

Polarization sensitive laser intensity inside femtosecond filament in air

Polarization dependence on clamping intensity inside femtosecond filament was experimentally measured in air. By tuning the laser pulse ellipse from linear polarization to circular polarization, the measured clamping intensity inside laser filament is gradually increased up to 1.7 times. The experimental results are in good agreement with the simulation results by solving the extended nonlinear Schrodinger equation (NLSE). The polarization sensitive clamping intensity inside filaments offers an important factor towards fully understanding the polarization related phenomenon observed so far

Laser guided ionic wind

We report on a method to experimentally generate ionic wind by coupling an external high voltage electric field with an intense femtosecond laser induced air plasma filament. The measured ionic wind velocity could be as strong as >4 m/s. It could be optimized by changing the applied electric field and the laser induced plasma channel. The experimental observation was qualitatively confirmed by a numerical simulation of spatial distribution of the electric field. This technique is robust and free from sharp metallic electrodes for coronas; it opens a way to optically generate ionic wind at a distance