Modeling and analysis of synergistic phenolic antioxidants in edible oil: Integrating molecular simulation and artificial neural network approach

Wu, Zongya; Shi, Xian; Wang, Xueting; Luo, Yanlong; Wang, Xiujuan

doi:10.1016/j.molstruc.2024.138959

Journal of Molecular Structure

2024

DOI: 10.1016/j.molstruc.2024.138959

|View full text |Cite

Modeling and analysis of synergistic phenolic antioxidants in edible oil: Integrating molecular simulation and artificial neural network approach

Zongya Wu,

Xian Shi,

Xueting Wang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Molecular Modeling, Simulation, and Design of Base Oils and Additives in Lubricating Oils: A Review

Zhou,

Che,

Wei

et al. 2024

Processes

View full text Add to dashboard Cite

Lubricating oils play a crucial role in modern industrial production, mechanical manufacturing, aerospace, and other fields. This paper provides a review and summary of the entire workflow for molecular simulations of lubricating oils, from molecular modeling, especially the molecular representation of base oils, to simulation calculation methods and result analysis. The application prospects and values of the relevant simulation techniques are discussed in detail. The simulation methods, force fields, and software involved in the modeling and simulation process are also introduced, aiming to provide guidance and insights for more rigorous, rational, and accurate lubricant molecular simulations, so as to accelerate the modification and development of new high-quality lubricants.

show abstract

The Molecular Modeling, Simulation, and Design of Base Oils and Additives in Lubricating Oils: A Review

Zhou,

Che,

Wei

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Modeling and analysis of synergistic phenolic antioxidants in edible oil: Integrating molecular simulation and artificial neural network approach

Cited by 1 publication

References 35 publications

The Molecular Modeling, Simulation, and Design of Base Oils and Additives in Lubricating Oils: A Review

The Molecular Modeling, Simulation, and Design of Base Oils and Additives in Lubricating Oils: A Review

Contact Info

Product

Resources

About