凝聚态物理
材料科学
石墨烯纳米带
带隙
费米能级
石墨烯
费米能量
电导
电子
物理
纳米技术
量子力学
作者
Jin-Ting Ding,Pei-Jia Hu,Aimin Guo
出处
期刊:Chinese Physics
[Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences]
日期:2023-01-01
卷期号:72 (15): 157301-157301
被引量:3
标识
DOI:10.7498/aps.72.20230502
摘要
Bandgap engineering in graphene has been a hot topic in condensed matter physics. Although several line defects have been experimentally reported in graphene, the relationship between the bandgap engineering and the line defects has not yet been discussed. In this work, by combining the Green’s function method with the Landauer-Büttiker formula, we study theoretically the electron transport along disordered ZGNRs through taking into account three types of line defects which arise from random distribution of 4-8 rings. Our results show that although there exist electronic states around the Fermi energy of the disordered ZGNRs with randomly distributed line defects, all these electronic states are localized and a transmission gap appears around the Fermi energy. This localization phenomenon originates from the structural disorder induced by the randomly distributed line defects. To demonstrate the robustness of transmission gaps, we further calculate the conductance values of disordered ZGNR with different insertion probabilities and widths, finding that the size of transmission gap strongly depends upon the types of disorder, disorder degree, and width. When the disorder degree of line defects is low or the width of the nanoribbon is narrow, there is a notable difference in the size of the transmission gaps among the three types of disordered ZGNRs. As the width or disorder degree increases, the transmission gap size tends to be consistent. Like armchair ZGNRs, the transmission gap size decreases with the increase of width or disorder of ZGNR. Nonetheless, the openings of the transmission gaps in three types of disordered ZGNRs remain robust, regardless of variations in degree of disorder or width. These results are helpful in designing line-defect based nanodevices.
科研通智能强力驱动
Strongly Powered by AbleSci AI