使用双子表面活性剂和功能化二氧化硅纳米颗粒优化油类从二氧化硅表面的脱离:分子动力学与机器学习相结合的方法。

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Gourav Chakraborty, Keka Ojha, Ajay Mandal and Niladri Patra
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Optimizing oil detachment from silica surfaces using gemini surfactants and functionalized silica nanoparticles: a combined molecular dynamics and machine learning approach†

Optimizing oil detachment from silica surfaces using gemini surfactants and functionalized silica nanoparticles: a combined molecular dynamics and machine learning approach†

The decline in the exploration of new oil sites necessitates the development of efficient strategies to maximize recovery from existing reservoirs. This study employs a molecular dynamics (MD) approach to investigate oil detachment from silica surfaces of varying hydrophobicity using a combination of bis-cationic gemini surfactants (GS) and functionalized silica nanoparticles (SNPs). Density profiles and radial distribution function (rdf) plots revealed a multilayered oil adsorption model. A reduction in oil–silica interaction energy was observed with an increase in surface hydrophobicity, highlighting the importance of polar interactions. Standard waterflooding studies, involving oil detachment solely with water, were conducted to assess baseline recovery efficiency. All the GS–SNP combinations outperformed standard waterflooding methods. SNPs significantly mitigated GS adsorption on reservoir beds, as evidenced by center-of-mass measurements. However, the effectiveness of the added injectants (GS–SNP) went downhill with increasing surface hydrophobicity, further validating the existence of a potential barrier for oil detachment, as known previously. Finally, supervised machine learning (ML) models were generated to predict the GS–SNP combination for a given silica surface, with MD generated descriptors. In most cases, boosting models, viz., XGBoost and AdaBoost yielded the best correlation with the observed data. However, for the complex oil model, ridge regression and support vector regression (SVR) outperformed other ML models in SNP prediction, pointing to the existence of a simpler correlation between the descriptors and the output variable. With these findings, the study attempts to streamline the data-driven design of chemical injectants for enhanced oil recovery purposes.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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