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Gu J, Shi W, Zheng H, Chen G, Wei B, Wong WY. The Novel Organic Emitters for High-Performance Narrow-Band Deep Blue OLEDs. Top Curr Chem (Cham) 2023; 381:26. [PMID: 37632653 DOI: 10.1007/s41061-023-00436-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023]
Abstract
Narrow-band deep-blue organic light-emitting diodes (OLEDs) have played a key role in the field of high-quality full-color displays. However, because of the considerable challenges of inherent band gaps, unbalanced carrier injection and the lack of molecular structures, narrow-band deep-blue emitters develop slowly compared with red- and green-emitting materials. Encouragingly, with the continuous efforts of scientists in recent years, great progress has been made in the molecule design and material synthesis of highly efficient narrow-band deep-blue emitters. The typical deep-blue emitters which exhibit narrow emission with a full width at half maximum of < 50 nm are summarized in this article. They are divided into the three categories: fluorescence, phosphorescence and thermally activated delayed fluorescence. The methods of molecular design for realizing narrow-band deep-blue emission are described in detail and future research directions are also discussed in this article.
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Affiliation(s)
- Jialu Gu
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Wei Shi
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Haixia Zheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Guo Chen
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Bin Wei
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
| | - Wai-Yeung Wong
- Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 100872, China.
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, 518057, Guangdong, China.
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Su X, Cao M, Wang L, Gui X, Zhang M, Huang Y, Zhao Y. Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Sci Total Environ 2023; 884:163794. [PMID: 37127154 DOI: 10.1016/j.scitotenv.2023.163794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/11/2023] [Accepted: 04/24/2023] [Indexed: 05/03/2023]
Abstract
MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.
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Affiliation(s)
- Xin Su
- School of Future Technology, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
| | - Mengdan Cao
- Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
| | - Lunche Wang
- School of Future Technology, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
| | - Xuan Gui
- Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
| | - Ming Zhang
- Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
| | - Yuhang Huang
- Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China
| | - Yueji Zhao
- Hulun Buir Meteorological Bureau, Hulun Buir Inner Mongolia 021008, China
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Su X, Wei Y, Wang L, Zhang M, Jiang D, Feng L. Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia. Sci Total Environ 2022; 832:155048. [PMID: 35390389 DOI: 10.1016/j.scitotenv.2022.155048] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/21/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR, SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study first evaluated and compared the accuracy, stability, and continuity of four DB aerosol optical depth (AOD) products in Asia using AErosol RObotic NETwork measurements. Then, the regional AOD spatial distributions, coverages, and series trends are analyzed. The results show that VIIRS DB has the highest accuracy and stability, with an expected error (EE, ±(0.05 + 20%)) of 76.59% and stability of approximately 0.027 per decade. The performance of MODIS DB is slightly worse than that of VIIRS. However, their AOD pattern, coverage, and trend are comparable. The performance of AVHRR (EE = 58.10%) and the stability of SeaWiFS (0.093 per decade) are less good. Therefore, SeaWiFS DB data should be used with caution for trend analysis. The AOD accuracy and coverage together determine the AOD pattern and the continuity of multi-sensor data. In addition to consistent algorithm accuracy, it is necessary to consider the influences in sensor sampling and inappropriate-pixel screening schemes in the joint multi-sensor analysis. Encouragingly, although multiple DB products have different AOD averages of regional series, their changing trends are consistent. Error analysis shows that the AOD bias characteristic is different in different surface conditions. This indicates that the surface reflectance estimated by the DB algorithm using different techniques is divergent, which may be the direction for the improvement of the algorithm.
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Affiliation(s)
- Xin Su
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
| | - Yifeng Wei
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
| | - Lunche Wang
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
| | - Ming Zhang
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
| | - Daoyang Jiang
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
| | - Lan Feng
- Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China.
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