Cited 0 time in
CsPbI3NC-Sensitized SnO2/Multiple-Walled Carbon Nanotube Self-Assembled Nanomaterials with Highly Selective and Sensitive NH3 Sensing Performance at Room Temperature
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Shao, Shaofeng | - |
| dc.contributor.author | Xie, Chunyu | - |
| dc.contributor.author | Zhang, Lei | - |
| dc.contributor.author | Wei, Song | - |
| dc.contributor.author | Kim, Hyoun Woo | - |
| dc.contributor.author | Kim, Sang Sub | - |
| dc.date.accessioned | 2021-07-30T04:48:09Z | - |
| dc.date.available | 2021-07-30T04:48:09Z | - |
| dc.date.created | 2021-07-14 | - |
| dc.date.issued | 2021-03 | - |
| dc.identifier.issn | 1944-8244 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1342 | - |
| dc.description.abstract | It is an effective strategy to enhance the sensitivity of semiconductor metal oxides (SMOs) being sensitized with CsPbI3 nanocrystals (NCs) by adjusting the heterostructure between CsPbI3NC and SMO nanomaterials. In this work, for the first time, a porous 3D multiple-walled carbon nanotube (MWCNT) network uniformly coated with SnO2 quantum nanoparticles (QNPs) and CsPbI3 nanocrystals were prepared via a simple solvent vapor-induced self-assembly method. The fabricated CsPbI3NC-SnO(2)QNP/MWCNT nanocomposite with vapor-induced self-assembly exhibits superior stability against the moisture as well as an excellent sensing response. The results imply that the rational design of the metal halide perovskite NC/SMO heterostructure can not only improve the stability but also meet the requirements of sensing application. The self-assembled SnO(2)QNP/MWCNT can facilitate the dispersion of small-sized nanoparticles and efficaciously prevent the detachment of CsPbI3NC. Compared with pristine SnO(2)QNP and SnO2/MWCNT sensors, the CsPbI3NC-modified SnO(2)QNP/MWCNT nanostructure exhibited a remarkable sensitivity of 39.2 for 0.2 ppm NH3, rapid response/recovery time of 17/18 s, and excellent selectivity towards NH3. In particular, we applied machine learning methods, including principal component analysis (PCA) and support vector machines (SVMs), to analyze the sensing performance of the CsPbI3NC-SnO(2)QNP/MWCNT sensor and found that the combined effects of CsPbI3NC-SnO(2)QNP/MWCNT heterointerfaces contributed to the improvement of selectivity of sensors. The excellent NH3 for sub-ppm level concentration is ascribed to the high sensing activity of the CsPbI3 NC-based heterojunction. This work may not only enrich the family of high-performance breath detection materials but also provide a good example for designing reasonable composite materials with specific properties in the field of metal halide perovskite/SMO heterojunctions. | - |
| dc.language | 영어 | - |
| dc.language.iso | en | - |
| dc.publisher | AMER CHEMICAL SOC | - |
| dc.title | CsPbI3NC-Sensitized SnO2/Multiple-Walled Carbon Nanotube Self-Assembled Nanomaterials with Highly Selective and Sensitive NH3 Sensing Performance at Room Temperature | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Kim, Hyoun Woo | - |
| dc.identifier.doi | 10.1021/acsami.0c20566 | - |
| dc.identifier.scopusid | 2-s2.0-85103682447 | - |
| dc.identifier.wosid | 000636686200056 | - |
| dc.identifier.bibliographicCitation | ACS APPLIED MATERIALS & INTERFACES, v.13, no.12, pp.14460 - 14470 | - |
| dc.relation.isPartOf | ACS APPLIED MATERIALS & INTERFACES | - |
| dc.citation.title | ACS APPLIED MATERIALS & INTERFACES | - |
| dc.citation.volume | 13 | - |
| dc.citation.number | 12 | - |
| dc.citation.startPage | 14460 | - |
| dc.citation.endPage | 14470 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Article | - |
| dc.description.journalClass | 1 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
| dc.relation.journalResearchArea | Materials Science | - |
| dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
| dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
| dc.subject.keywordPlus | Ammonia | - |
| dc.subject.keywordPlus | Carbon nanotubes | - |
| dc.subject.keywordPlus | Chemical sensors | - |
| dc.subject.keywordPlus | Heterojunctions | - |
| dc.subject.keywordPlus | Learning systems | - |
| dc.subject.keywordPlus | Metal halides | - |
| dc.subject.keywordPlus | Nanocrystals | - |
| dc.subject.keywordPlus | Nanoparticles | - |
| dc.subject.keywordPlus | Perovskite | - |
| dc.subject.keywordPlus | Self assembly | - |
| dc.subject.keywordPlus | Support vector machines | - |
| dc.subject.keywordPlus | Applied machine learning | - |
| dc.subject.keywordPlus | Multiple-walled carbon nanotubes | - |
| dc.subject.keywordPlus | Self-assembly method | - |
| dc.subject.keywordPlus | Semiconductor metal oxides | - |
| dc.subject.keywordPlus | Sensing applications | - |
| dc.subject.keywordPlus | Sensing performance | - |
| dc.subject.keywordPlus | Specific properties | - |
| dc.subject.keywordPlus | Support vector machine (SVMs) | - |
| dc.subject.keywordPlus | Semiconducting lead compounds | - |
| dc.subject.keywordAuthor | gas-sensing composite materials | - |
| dc.subject.keywordAuthor | vapor-induced self-assembly | - |
| dc.subject.keywordAuthor | metal halide perovskite | - |
| dc.subject.keywordAuthor | heterojunction | - |
| dc.subject.keywordAuthor | machine learning method | - |
| dc.subject.keywordAuthor | breath detection materials | - |
| dc.identifier.url | https://pubs.acs.org/doi/10.1021/acsami.0c20566 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1366
COPYRIGHT © 2024 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
