scholarly journals Ultrafast and long-range self-transport of droplets on multi-bioinspired surface with periodic gradient structure

Author(s):  
Dongdong Xie ◽  
Guilian Wang ◽  
Yunna Sun ◽  
Chaofeng Wu ◽  
Guifu Ding

Abstract Droplet self-transport is crucial in various fields ranging from physics to biochemistry. Despite extensive progress, existing systems for droplet self-transport still perform at low transport velocity or limited transport range. Here, a multi-bioinspired surface comprising two-dimensional gradient structures is proposed innovatively, which integrates the heterogeneous wettability with the shape gradient morphology. Droplet transport behaviors are systematically investigated from experiment, theory, and simulation. The fabricated structure achieves the ultrafast (over ~ 450 mm/s) and long-range (over ~ 200 mm) self-transport of droplets. Moreover, the fantastic scalability of this structure enables it to pump numerous multi-scale droplets from one site to the preset region with ultralow loss. Drawing inspirations, two systems have been designed to complete complex tasks on open surface. This work provides a reliable droplet manipulation strategy for various applications, such as water collection, microfluidics, and biomedicine, etc.

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.


2021 ◽  
Vol 27 (S1) ◽  
pp. 952-954
Author(s):  
Suk Hyun Sung ◽  
Yin Min Goh ◽  
Noah Schnitzer ◽  
Ismail El Baggari ◽  
Kai Sun ◽  
...  

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