Enhanced Condensation on Liquid-Infused Nanoporous Surfaces by Vibration-Assisted Droplet Sweeping

ACS Nano ◽  
2020 ◽  
Vol 14 (10) ◽  
pp. 13367-13379
Author(s):  
Inkyu Oh ◽  
Hyeongyun Cha ◽  
Jiehao Chen ◽  
Shreyas Chavan ◽  
Hyunjoon Kong ◽  
...  
Keyword(s):  
2008 ◽  
Vol 20 (4) ◽  
pp. 717-723 ◽  
Author(s):  
V. Bansal ◽  
H. Jani ◽  
J. Du Plessis ◽  
P. J. Coloe ◽  
S. K. Bhargava

2016 ◽  
Vol 19 (3) ◽  
pp. 845-857 ◽  
Author(s):  
M. C. Connelly ◽  
G. S. Reddy ◽  
Mallikarjuna N. Nadagouda ◽  
J. A. Sekhar
Keyword(s):  

2001 ◽  
Vol 15 (28n29) ◽  
pp. 1391-1401 ◽  
Author(s):  
P. B. JOHNSON ◽  
P. W. GILBERD ◽  
YVONNE MORRISON ◽  
C. R. VAROY

The nanoporous surfaces that can be produced by ion implantation of helium into metals have considerable potential for applications. The structures are characterised by random nanoscale cavities of uniform size, a high degree of swelling and very thin metal "walls" separating the neighbouring cavities. These structures are "seeded" by an earlier stage - a gas-bubble superlattice - comprising small helium bubbles again of uniform size (~ 2 nm diameter) ordered on a space lattice having the same symmetry as the host. For the fcc metals the bubble superlattice always contains structural variants - that is, regions where the ordered bubble array has an orientation that is rational with, but different from, that of the crystal lattice of the host metal. In contrast for bcc metals previous work has suggested that the bubble lattice aligns solely parallel with the host crystal. Here we report the first clear demonstration of the presence of structural variant bubble lattices in a bcc metal. A combination of techniques including helium depth-profiling using HERDA is enabling the helium dose requirements for producing ordered bubble structures to be investigated more closely. These techniques are reviewed.


Langmuir ◽  
2006 ◽  
Vol 22 (11) ◽  
pp. 4978-4984 ◽  
Author(s):  
Krisanu Bandyopadhyay ◽  
Eric Tan ◽  
Lin Ho ◽  
Sarah Bundick ◽  
Shenda M. Baker ◽  
...  

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3383
Author(s):  
Uzair Sajjad ◽  
Imtiyaz Hussain ◽  
Muhammad Imran ◽  
Muhammad Sultan ◽  
Chi-Chuan Wang ◽  
...  

The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.


Langmuir ◽  
2009 ◽  
Vol 25 (20) ◽  
pp. 12374-12379 ◽  
Author(s):  
Eyal Bittoun ◽  
Abraham Marmur ◽  
Mattias Östblom ◽  
Thomas Ederth ◽  
Bo Liedberg
Keyword(s):  

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