45 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author하산 르가즈-
dc.date.accessioned2022-08-11T01:50:48Z-
dc.date.available2022-08-11T01:50:48Z-
dc.date.issued2021-09-
dc.identifier.citationPhysical Chemistry Chemical Physics, v. 23, NO 36, Page. 19987-20027en_US
dc.identifier.issn14639076-
dc.identifier.issn14639084-
dc.identifier.urihttps://pubs.rsc.org/en/content/articlelanding/2021/CP/D1CP00244A-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/172378-
dc.description.abstractMolecular modelling of organic compounds using computational software has emerged as a powerful approach for theoretical determination of the corrosion inhibition potential of organic compounds. Some of the common techniques involved in the theoretical studies of corrosion inhibition potential and mechanisms include density functional theory (DFT), molecular dynamics (MD) and Monte Carlo (MC) simulations, and artificial neural network (ANN) and quantitative structure-activity relationship (QSAR) modeling. Using computational modelling, the chemical reactivity and corrosion inhibition activities of organic compounds can be explained. The modelling can be regarded as a time-saving and eco-friendly approach for screening organic compounds for corrosion inhibition potential before their wet laboratory synthesis would be carried out. Another advantage of computational modelling is that molecular sites responsible for interactions with metallic surfaces (active sites or adsorption sites) and the orientation of organic compounds can be easily predicted. Using different theoretical descriptors/parameters, the inhibition effectiveness and nature of the metal-inhibitor interactions can also be predicted. The present review article is a collection of major advancements in the field of computational modelling for the design and testing of the corrosion inhibition effectiveness of organic corrosion inhibitors.en_US
dc.description.sponsorshipEEE thanks the National Research Foundation of South Africa for Incentive Funding for Rated Researchers, and LG thanks the National Natural Science Foundation of China (21706195, 22062022) for the funding.en_US
dc.language.isoenen_US
dc.publisherRoyal Society of Chemistryen_US
dc.titleMolecular modelling of compounds used for corrosion inhibition studies: a reviewen_US
dc.typeArticleen_US
dc.relation.no36-
dc.relation.volume23-
dc.identifier.doi10.1039/d1cp00244a-
dc.relation.page19987-20027-
dc.relation.journalPhysical Chemistry Chemical Physics-
dc.contributor.googleauthorEbenso, Eno E.-
dc.contributor.googleauthorVerma, Chandrabhan-
dc.contributor.googleauthorOlasunkanmi, Lukman O.-
dc.contributor.googleauthorAkpan, Ekemini D.-
dc.contributor.googleauthorVerma, Dakeshwar Kumar-
dc.contributor.googleauthorLgaz, Hassane-
dc.contributor.googleauthorGuo, Lei-
dc.contributor.googleauthorKaya, Savas-
dc.contributor.googleauthorQuraishi, M. A.-
dc.relation.code2021032046-
dc.sector.campusE-
dc.sector.daehakOFFICE OF ACADEMIC AFFAIRS[E]-
dc.sector.departmentCENTER FOR CREATIVE CONVERGENCE EDUCATION-
dc.identifier.pidhlgaz-
Appears in Collections:
OFFICE OF ACADEMIC AFFAIRS[E](교무처) > Center for Creative Convergence Education(창의융합교육원) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE