Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

Deng, Jiandong; Jia, Guozhu

doi:10.1016/j.fluid.2022.113545

Cited by 9 publications

(3 citation statements)

References 70 publications

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“…Dielectric property is another important factor in the choice of solvent for making highly uniform and dense monolayers on a given substrate . When considering both the protic solvents, ethanol shows a lower dielectric constant than methanol, which are, respectively, 24.55 and 32.70.…”

Section: Resultsmentioning

confidence: 99%

“…32,33 Dielectric property is another important factor in the choice of solvent for making highly uniform and dense monolayers on a given substrate. 31 When considering both the protic solvents, ethanol shows a lower dielectric constant than methanol, which are, respectively, 24.55 and 32.70. Ethanol shows the highest contact angle of 151.35°, and methanol shows a contact angle of around 150.04°(Table 1) due to the formation of the SAM layer.…”

Section: Morphological Analysis Of the Tiomentioning

confidence: 99%

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Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate

et al. 2022

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The self-assembled monolayer (SAM) on inorganic metal oxides is highly applicable in making different kinds of surface phenomena such as superhydrophobicity, functional group-modified surfaces, corrosion resistance, and so on. The formation of stearic acid SAMs on the TiO2 substrate depends on a few factors, and the cleanability of the substrate surface can be considered as the critical criterion for the formation of the SAM layer. The solvent, concentration of the adsorbate, immersion time, and temperature can be identified as other factors that are crucial for growing a uniform and highly dense monolayer. SAM layers always build up spontaneously on a suitable substrate, but the growth rate and arrangement can be changed by varying the external factors. These factors highly affect the chemisorption of stearic acid molecules onto the TiO2 substrate and building a well-ordered pattern on the surface without defects. This study mainly focuses on identifying the critical conditions of the external factors in obtaining a high-performance superhydrophobic surface. The crystal structure and surface morphologies of the substrate materials are characterized by powder X-ray diffraction and scanning electron microscopy, and the surface wettability is characterized by contact angle measurements. High superhydrophobicity is observed at the optimum conditions of the factors. Ethanol is used as the solvent; the temperature is about 40 °C; and 600 ppm of stearic acid is the critical concentration in obtaining a superhydrophobic surface with 100 min of immersion time, while the contact angle is 151.38°. Simultaneously, if the concentration is 1000 ppm and the immersion time is 120 min, the surface shows high superhydrophobicity with a contact angle of 162.06°. These critical conditions are found to be adequate for building well-ordered stearic acid SAMs on the TiO2 substrate.

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Section: Resultsmentioning

confidence: 99%

Section: Morphological Analysis Of the Tiomentioning

confidence: 99%

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate

et al. 2022

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“…Based on z, the original model, f (x), can be expressed as a linear function of binary variables, and the calculation formula is as follows [52,53]:…”

Section: Shapley Additive Explanationsmentioning

confidence: 99%

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Qiu,

Xu,

Tang

et al. 2024

Land

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Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

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Buckwheat waste depolymerization using a subcritical ethanol solution for extraction of bioactive components: from the laboratory to pilot scale

Yuan¹,

Shimizu²,

Li³

et al. 2023

Journal of Environmental Chemical Engineering

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Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

Cited by 9 publications

References 70 publications

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Buckwheat waste depolymerization using a subcritical ethanol solution for extraction of bioactive components: from the laboratory to pilot scale

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Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

Cited by 9 publications

References 70 publications

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO2 Substrate

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO2 Substrate

Interpretable Landslide Susceptibility Evaluation Based on Model Optimization

Buckwheat waste depolymerization using a subcritical ethanol solution for extraction of bioactive components: from the laboratory to pilot scale

Contact Info

Product

Resources

About

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate

Factors Affecting the Hydrophobic Property of Stearic Acid Self-Assembled on the TiO₂ Substrate