Discontinuous dewetting dynamics of highly viscous droplets on chemically heterogeneous substrates

Jiang, Jiatong; Jackson, Frankie F.; Tangparitkul, Suparit; Wilson, Mark C. T.; Harbottle, David

doi:10.1016/j.jcis.2022.09.064

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…Less hydrophilic system (Package II – hydrophobic cases) retarded the v and the dynamics process terminated at longer time ( t ∼ 10 s → t ∼ 100 s for 60% ethanol mixture), as it contributed to a decrease in a driving capillary force, , assuming an increase in the θ. The hydrophilicity influence observed is similar to that of previous work by Lee et al Uneven or fluctuate-like plots in hydrophobic are thought to be due to chemical heterogeneity on the hydrophobized tube surface, where crude oil components were adsorbed, as confirmed by surface imaging using atomic force microscopy (Figure S1).…”

Section: Resultssupporting

confidence: 87%

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Suetrong,

Tosuai,

Vo Thanh

et al. 2024

Energy Fuels

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Immiscible fluid−fluid displacement dynamics is a crucial element to understanding and engineering many subsurface flow applications, including enhanced oil recovery and carbon dioxide geological sequestration. Although there are several interfacial properties that govern such a displacement dynamic, the wettability has been considered a dominant factor. Owing to its complex coaffinity among the three phases (i.e., solid−fluid−fluid) and difficulty to be characterized accurately and efficiently, the wettability (defined as the contact angle: θ) determination is of interest in the current study with aim toward machine learning (ML) approach.In the current research, four experimental packages of fluid displacement at 1D capillary scale served as data sets for ML examination on the θ predictability. Via digital image processing, fluid traveling length at a given time was extracted, and the theoretical θ was calculated as ground truth for the modeling, with input features being fluid traveling length, displacing velocity, and the interfacial tension. Random forest (RF) and multilayer perception (MLP) were selected for the modeling due to their appropriate characteristics to the investigated data (being nonlinear relation). The prediction results showed that RF apparently outperformed MLP on the θ prediction, reflecting its best ability to manage missing values and outliers. Although more input features analyzed (from two to three features) did yield a better prediction, the best model remains RF. Sensitivity on the key parameters of displacing velocity and the interfacial tension was also analyzed, where the results confirm the model prediction agreement with theories. The study demonstrated how ML model can be an alternative tool to elucidate the fluid displacement in subsurface, with additional potential for autonomously improving the deep underground flows, converging a new concept of "augmented" artificial intelligence.

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Section: Resultssupporting

confidence: 87%

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Suetrong,

Tosuai,

Vo Thanh

et al. 2024

Energy Fuels

Self Cite

View full text Add to dashboard Cite

show abstract

“…A 10 μL sample of crude oil was deposited on a cleaned glass substrate and then placed in an insulated thermal cell. To allow only chemical additives to be examined, the model glass substrate was used in the current work to eliminate physical and chemical heterogeneities since strong influences of such heterogeneities have been found in the droplet dewetting process [30]. The glass substrate was prepared by rinsing in Milli-Q water and drying using N2 gas before being placed in a UV/Ozone Procleaner TM (Bioforce Nanosciences, USA) for 15 min to remove any organics.…”

Section: Crude Oil Droplet Dewetting Dynamics and The Steady-state Co...mentioning

confidence: 99%

Crude oil-water interface partitioning of polyvinylpyrrolidone-coated silica nanoparticles in low-salinity brine

Tangparitkul

2022

Journal of Petroleum Science and Engineering

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Spontaneous process of dispersed salt water droplets in lubricant oil establishing wetted areas: Settling, spreading, coalescing and de-wetting

He,

Zeng,

Pang

et al. 2024

Colloids and Surfaces A: Physicochemical and Engineering Aspect

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Discontinuous dewetting dynamics of highly viscous droplets on chemically heterogeneous substrates

Cited by 7 publications

References 56 publications

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Predicting Dynamic Contact Angle in Immiscible Fluid Displacement: A Machine Learning Approach for Subsurface Flow Applications

Crude oil-water interface partitioning of polyvinylpyrrolidone-coated silica nanoparticles in low-salinity brine

Spontaneous process of dispersed salt water droplets in lubricant oil establishing wetted areas: Settling, spreading, coalescing and de-wetting

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