Literature DB >> 19334746

Asphaltene adsorption onto self-assembled monolayers of mixed aromatic and aliphatic trichlorosilanes.

Salomon Turgman-Cohen1, Matthew B Smith, Daniel A Fischer, Peter K Kilpatrick, Jan Genzer.   

Abstract

The adsorption of asphaltenes onto flat solid surfaces modified with mixed self-assembled monolayers (SAMs) of aliphatic and aromatic trichlorosilanes with varying wettabilities, aromaticities, and thicknesses is tested. The mixed SAMs are characterized by means of contact angle to assess hydrophobicity and molecular and chemical uniformity, spectroscopic ellipsometry to measure the thickness of the films, and near edge X-ray absorption fine structure (NEXAFS) spectroscopy to assess chemical and molecular composition. The molecular characteristics of the adsorbed asphaltene layer and the extent of asphaltene adsorption are determined using NEXAFS and spectroscopic ellipsometry, respectively. The SAMs are formed by depositing phenyl-, phenethyl-, butyl-, and octadecyl- trichlorosilanes from toluene solutions onto silica-coated substrates; the chemical composition and the wettability of the SAM surface is tuned systematically by varying the trichlorosilane composition in the deposition solutions. The adsorption of asphaltenes on the substrates does not correlate strongly with the SAM chemical composition. Instead, the extent of asphaltene adsorption decreases with increasing SAM thickness. This observation suggests that the leading interaction governing the adsorption of asphaltenes is their interaction with the polar silica substrate and that the chemical composition of the SAM is of secondary importance.

Entities:  

Year:  2009        PMID: 19334746     DOI: 10.1021/la9000895

Source DB:  PubMed          Journal:  Langmuir        ISSN: 0743-7463            Impact factor:   3.882


  1 in total

1.  Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks.

Authors:  Mohammad Sadegh Mazloom; Farzaneh Rezaei; Abdolhossein Hemmati-Sarapardeh; Maen M Husein; Sohrab Zendehboudi; Amin Bemani
Journal:  Nanomaterials (Basel)       Date:  2020-05-06       Impact factor: 5.076

  1 in total

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