Computational Prediction of Superlubric Layered Heterojunctions
Structural superlubricity has attracted increasing interest in modern tribology. However, experimental identification of superlubric interfaces among the vast number of heterojunctions is a trial-and-error and time-consuming approach. In this work, based on the requirements on the in-plane stiffness...
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Published in | ACS applied materials & interfaces Vol. 13; no. 28; pp. 33600 - 33608 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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American Chemical Society
21.07.2021
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Abstract | Structural superlubricity has attracted increasing interest in modern tribology. However, experimental identification of superlubric interfaces among the vast number of heterojunctions is a trial-and-error and time-consuming approach. In this work, based on the requirements on the in-plane stiffnesses of layered materials and the interfacial interactions at the sliding incommensurate interfaces of heterojunctions for structural superlubricity, we propose criteria for predicting structural superlubricity between heterojunctions. Based on these criteria, we identify 61 heterojunctions with potential superlubricity features from 208 candidates by screening the data of first-principles calculations. This work provides a universal route for accelerating the discovery of new superlubric heterojunctions. |
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AbstractList | Structural superlubricity has attracted increasing interest in modern tribology. However, experimental identification of superlubric interfaces among the vast number of heterojunctions is a trial-and-error and time-consuming approach. In this work, based on the requirements on the in-plane stiffnesses of layered materials and the interfacial interactions at the sliding incommensurate interfaces of heterojunctions for structural superlubricity, we propose criteria for predicting structural superlubricity between heterojunctions. Based on these criteria, we identify 61 heterojunctions with potential superlubricity features from 208 candidates by screening the data of first-principles calculations. This work provides a universal route for accelerating the discovery of new superlubric heterojunctions. |
Author | Liu, Ze Wang, Yelingyi Gao, Enlai Jia, Xiangzheng Wu, Bozhao Ouyang, Wengen |
AuthorAffiliation | Department of Engineering Mechanics, School of Civil Engineering |
AuthorAffiliation_xml | – name: Department of Engineering Mechanics, School of Civil Engineering |
Author_xml | – sequence: 1 givenname: Enlai orcidid: 0000-0003-1960-0260 surname: Gao fullname: Gao, Enlai – sequence: 2 givenname: Bozhao surname: Wu fullname: Wu, Bozhao – sequence: 3 givenname: Yelingyi surname: Wang fullname: Wang, Yelingyi – sequence: 4 givenname: Xiangzheng surname: Jia fullname: Jia, Xiangzheng – sequence: 5 givenname: Wengen orcidid: 0000-0001-8700-1978 surname: Ouyang fullname: Ouyang, Wengen – sequence: 6 givenname: Ze orcidid: 0000-0002-9906-5351 surname: Liu fullname: Liu, Ze email: ze.liu@whu.edu.cn |
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CitedBy_id | crossref_primary_10_3390_lubricants12040138 crossref_primary_10_1103_RevModPhys_96_011002 crossref_primary_10_1016_j_triboint_2024_109896 crossref_primary_10_1016_j_apsusc_2022_155760 crossref_primary_10_1016_j_apsadv_2021_100175 crossref_primary_10_1515_nanoph_2022_0185 crossref_primary_10_1016_j_mtnano_2023_100361 crossref_primary_10_1002_adma_202305072 crossref_primary_10_1016_j_jmst_2023_12_080 crossref_primary_10_1007_s40544_024_0901_8 crossref_primary_10_1016_j_cej_2022_138249 crossref_primary_10_1016_j_commatsci_2022_111302 crossref_primary_10_1016_j_susc_2022_122207 crossref_primary_10_1088_1361_6528_ac475a |
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Title | Computational Prediction of Superlubric Layered Heterojunctions |
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