Literature DB >> 24111487

Understanding scanning tunneling microscopy contrast mechanisms on metal oxides: a case study.

Harry Mönig1, Milica Todorović, Mehmet Z Baykara, Todd C Schwendemann, Lucía Rodrigo, Eric I Altman, Rubén Pérez, Udo D Schwarz.   

Abstract

A comprehensive analysis of contrast formation mechanisms in scanning tunneling microscopy (STM) experiments on a metal oxide surface is presented with the oxygen-induced (2√2×√2)R45° missing row reconstruction of the Cu(100) surface as a model system. Density functional theory and electronic transport calculations were combined to simulate the STM imaging behavior of pure and oxygen-contaminated metal tips with structurally and chemically different apexes while systematically varying bias voltage and tip-sample distance. The resulting multiparameter database of computed images was used to conduct an extensive comparison with experimental data. Excellent agreement was attained for a large number of cases, suggesting that the assumed model tips reproduce most of the commonly encountered contrast-determining effects. Specifically, we find that depending on the bias voltage polarity, copper-terminated tips allow selective imaging of two structurally distinct surface Cu sites, while oxygen-terminated tips show complex contrasts with pronounced asymmetry and tip-sample distance dependence. Considering the structural and chemical stability of the tips reveals that the copper-terminated apexes tend to react with surface oxygen at small tip-sample distances. In contrast, oxygen-terminated tips are considerably more stable, allowing exclusive surface oxygen imaging at small tip-sample distances. Our results provide a conclusive understanding of fundamental STM imaging mechanisms, thereby providing guidelines for experimentalists to achieve chemically selective imaging by properly selecting imaging parameters.

Entities:  

Year:  2013        PMID: 24111487     DOI: 10.1021/nn4045358

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  7 in total

1.  Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

Authors:  Rama K Vasudevan; Kamal Choudhary; Apurva Mehta; Ryan Smith; Gilad Kusne; Francesca Tavazza; Lukas Vlcek; Maxim Ziatdinov; Sergei V Kalinin; Jason Hattrick-Simpers
Journal:  MRS Commun       Date:  2019       Impact factor: 2.566

2.  Dynamic transformation between bilayer islands and dinuclear clusters of Cr oxide on Au(111) through environment and interface effects.

Authors:  Zhiyu Yi; Le Lin; Yuan Chang; Xuda Luo; Junfeng Gao; Rentao Mu; Yanxiao Ning; Qiang Fu; Xinhe Bao
Journal:  Proc Natl Acad Sci U S A       Date:  2022-05-23       Impact factor: 12.779

3.  Atomic species identification at the (101) anatase surface by simultaneous scanning tunnelling and atomic force microscopy.

Authors:  Oleksandr Stetsovych; Milica Todorović; Tomoko K Shimizu; César Moreno; James William Ryan; Carmen Pérez León; Keisuke Sagisaka; Emilio Palomares; Vladimír Matolín; Daisuke Fujita; Ruben Perez; Oscar Custance
Journal:  Nat Commun       Date:  2015-06-29       Impact factor: 14.919

4.  Real-space Wigner-Seitz cells imaging of potassium on graphite via elastic atomic manipulation.

Authors:  Feng Yin; Pekka Koskinen; Sampo Kulju; Jaakko Akola; Richard E Palmer
Journal:  Sci Rep       Date:  2015-02-05       Impact factor: 4.379

5.  Edge reactivity and water-assisted dissociation on cobalt oxide nanoislands.

Authors:  J Fester; M García-Melchor; A S Walton; M Bajdich; Z Li; L Lammich; A Vojvodic; J V Lauritsen
Journal:  Nat Commun       Date:  2017-01-30       Impact factor: 14.919

6.  Tuning the activities of cuprous oxide nanostructures via the oxide-metal interaction.

Authors:  Wugen Huang; Qingfei Liu; Zhiwen Zhou; Yangsheng Li; Yunjian Ling; Yong Wang; Yunchuan Tu; Beibei Wang; Xiaohong Zhou; Dehui Deng; Bo Yang; Yong Yang; Zhi Liu; Xinhe Bao; Fan Yang
Journal:  Nat Commun       Date:  2020-05-08       Impact factor: 14.919

7.  Ultrafast current imaging by Bayesian inversion.

Authors:  S Somnath; K J H Law; A N Morozovska; P Maksymovych; Y Kim; X Lu; M Alexe; R Archibald; S V Kalinin; S Jesse; R K Vasudevan
Journal:  Nat Commun       Date:  2018-02-06       Impact factor: 14.919

  7 in total

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