Non-cadmium" dual emissive QDs have been directly synthesized using a one-pot hot-injection technique. A white LED was successfully fabricated using a commercial blue-LED chip combined with the optimal QDs.
ABSTRACTThe global demand for resource sustainability is growing. Thus, the development of single-source environment-friendly colloidal semiconductor nanocrystal (NC) phosphors with broadband emission is highly desirable for use as color converters in white light-emitting diodes (WLEDs). We report herein the gram-scale synthesis of single-source, cadmium-free, dual-emissive Mn-doped Zn-Cu-In-S NCs (d-dots) by a simple, non-injection, and low-cost approach in one-pot fashion. This synthesis approach led to the formation of NCs with continuously varying compositions in a radial direction because the reactivity of precursors totally differed among these precursors. Consequently, d-dots exhibited two emission bands, one of which was due to Mn-related emissions and the other was due to the band edge of Zn-Cu-In-S NCs. Emission peaks assigned to band edge were tunable by simple control of particle size and composition. The prepared d-dots also exhibited the characteristic zero self-absorption, a quantum yield of 46%, and good thermal stability. A combination of a commercial blue LED chip with optimal d-dots as color converters gave a high color rending index of up to 90, Commission International de l'Eclairage color coordinates of (0.332, 0.321), and correlated color temperature of 5680 K. These results suggested that this cadmium-free, excellent thermally stable single-phase d-dot phosphor had potential applications in WLEDs.
Nano Research
DOI () Research Article| www.editorialmanager.com/nare/default.asp 2 Nano Res.
Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.
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