Atomistic Probing of Defect-Engineered 2H-MoTe2 Monolayers
DC Field | Value | Language |
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dc.contributor.author | Okello, Odongo Francis Ngome | - |
dc.contributor.author | Dong-Hwan Yang | - |
dc.contributor.author | Seo, Seung-Young | - |
dc.contributor.author | Jewook Park | - |
dc.contributor.author | Gunho Moon | - |
dc.contributor.author | Shin, Dongwon | - |
dc.contributor.author | Chu, Yu-Seong | - |
dc.contributor.author | Yang, Sejung | - |
dc.contributor.author | Mizoguchi, Teruyasu | - |
dc.contributor.author | Moon-Ho Jo | - |
dc.contributor.author | Si-Young Choi | - |
dc.date.accessioned | 2024-04-11T01:30:05Z | - |
dc.date.available | 2024-04-11T01:30:05Z | - |
dc.date.created | 2024-03-11 | - |
dc.date.issued | 2024-03 | - |
dc.identifier.issn | 1936-0851 | - |
dc.identifier.uri | https://pr.ibs.re.kr/handle/8788114/15013 | - |
dc.description.abstract | Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe2 monolayers that are tactically exposed to (i) 200 degrees C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe2 samples; but interestingly, 200 degrees C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe2, respectively. While 200 degrees C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality. | - |
dc.language | 영어 | - |
dc.publisher | American Chemical Society | - |
dc.title | Atomistic Probing of Defect-Engineered 2H-MoTe<sub>2</sub> Monolayers | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.wosid | 001173669700001 | - |
dc.identifier.scopusid | 2-s2.0-85186089841 | - |
dc.identifier.rimsid | 82679 | - |
dc.contributor.affiliatedAuthor | Dong-Hwan Yang | - |
dc.contributor.affiliatedAuthor | Jewook Park | - |
dc.contributor.affiliatedAuthor | Gunho Moon | - |
dc.contributor.affiliatedAuthor | Moon-Ho Jo | - |
dc.contributor.affiliatedAuthor | Si-Young Choi | - |
dc.identifier.doi | 10.1021/acsnano.3c08606 | - |
dc.identifier.bibliographicCitation | ACS Nano, v.18, no.9, pp.6927 - 6935 | - |
dc.relation.isPartOf | ACS Nano | - |
dc.citation.title | ACS Nano | - |
dc.citation.volume | 18 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 6927 | - |
dc.citation.endPage | 6935 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordPlus | INTRINSIC STRUCTURAL DEFECTS | - |
dc.subject.keywordPlus | 2-DIMENSIONAL MATERIALS | - |
dc.subject.keywordPlus | VISUALIZATION | - |
dc.subject.keywordPlus | SCATTERING | - |
dc.subject.keywordPlus | STEM | - |
dc.subject.keywordAuthor | 2H-MoTe2 | - |
dc.subject.keywordAuthor | point defect | - |
dc.subject.keywordAuthor | vacuum-annealing | - |
dc.subject.keywordAuthor | laser-illumination | - |
dc.subject.keywordAuthor | scanningtransmission electron microscopy | - |
dc.subject.keywordAuthor | deep learning | - |