Neural network modelling of the wettability of a surface grooved with the nanoscale pillars

Choi, Seyong; Byun, Kisang; Jang, Joonkyung

doi:10.1016/j.cplett.2021.138360

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most significant influences on wettability are found in the surface shape and surface chemical characteristics. The ANN model was used to predict the CA of raised texture (width, W ; height, H ; and spacing, P ) and groove texture, and the predicted CAs exceeded 150°. The ANN model was used to characterize the generated superhydrophobic surface structures.…”

Section: Introductionmentioning

confidence: 99%

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Wang

Di³

et al. 2023

Langmuir

View full text Add to dashboard Cite

The three-phase contact line best reflects the sliding ability of droplets on solid surfaces. Most studies on the sliding angle (SA) of superhydrophobic surfaces are limited to regularly arranged microtextured surfaces, lacking definite models and effective methods for a complex surface of a random texture. In this study, random pits with an area ratio of 19% were generated on 1 mm × 1 mm subregions, and the subregions formed arrays on a sample surface of 10 mm × 10 mm to obtain a randomly distributed microtexture surface with no pit overlaps. Although the contact angle (CA) of randomly pitted texture was the same, the SA was different. The SA of surfaces was affected by the pit location. The location of random pits increased the complexity of the three-phase contact line movement. The continuity of the three-phase contact angle (T) can reveal the rolling mechanism of the random pit texture and predict the SA, but the relationship between the T and SA is a relatively poor linear relation (R 2 = 74%), and the SA of the random pit texture can only be roughly estimated. The quantized pit coordinates and SA were used as the input and output labels for the PNN model, respectively, and the accuracy of the model convergence was 90.2%.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Wang

Di³

et al. 2023

Langmuir

View full text Add to dashboard Cite

show abstract

Synthesis and evaluation of depressurization and injection treatment agent suitable for low-permeability reservoirs

Nie

et al. 2022

Chemical Physics Letters

View full text Add to dashboard Cite

Neural network modelling of the wettability of a surface grooved with the nanoscale pillars

Cited by 2 publications

References 19 publications

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Synthesis and evaluation of depressurization and injection treatment agent suitable for low-permeability reservoirs

Contact Info

Product

Resources

About