A series of heteroleptic cyclometalated Ir (III) complexes for OLEDs application have been investigated theoretically to explore their electronic structures and spectroscopic properties. The geometries, electronic structures, and the lowest-lying singlet absorptions and triplet emissions of (piq)₂Ir(acac) (labeled 1) and theoretically designed models (piq)₂Ir(dpis) (labeled 2), (4Fpiq)₂Ir(dpis) (labeled 3), (4F5M-piq)₂Ir(dpis) (labeled 4), (4,5-2F-piq)₂Ir(dpis) (labeled 5) and (5-F-piq)₂Ir(dpis) (labeled 6) were investigated with density functional theory (DFT)-based approaches, where, piq=1-phenylisoquinolato, acac=acetylacetonate and dpis=diphenylimidodisilicate. Their structures in the ground and excited states have been optimized at the DFT/B3LYP/LANL2DZ and TDDFT/B3LYP/LANL2DZ levels, and the lowest absorptions and emissions were evaluated at B3LYP and M062X level of theory, respectively. Furthermore, the energy-transfer mechanism of these complexes also be analyzed here, and the result shown that the complexes 1-6 are having the low efficiency roll-off property. Except that, the oscillator strength analyze shown that the complexes 2-6, which were designed by theory, are suitable for OLED since their high oscillator strength property.
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http://dx.doi.org/10.1016/j.saa.2014.06.088 | DOI Listing |
Sci Rep
December 2024
College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
In light of the Chinese government's dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2001 to 2022. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China.
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December 2024
Faculty of Education, Shinawatra University, Bangkok, Thailand.
This study aims to reduce engine emissions while maintaining engine performance and providing a sustainable fuel source for long-term use. It introduces a novel approach by combining pine oil (PO) and lemon grass oil (LGO) with diesel fuel in a specific ratio (10% PO + 10% LGO + 80% Diesel). This work is innovative in that it employs these two distinct low-viscosity biofuel blends in conjunction with diesel fuel in an agricultural engine, resulting in reduced carbon footprints in the tailpipe.
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December 2024
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
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December 2024
Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires/CONICET, Paseo Colón 850 CABA, Buenos Aires, Argentina.
The oil and gas industry faces two significant challenges, including rising global temperatures and depletion of reserves. Enhanced recovery techniques such as polymer flooding have positioned themselves as an alternative that attracts international attention thanks to increased recovery factors with low emissions. However, existing physical models need further refinement to improve predictive accuracy and prevent design failures in polymer flooding projects.
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December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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