On Doping Eu³⁺ in Sr_0.99La_1.01Zn_0.99O_3.495: The Photoluminescence, Population Pathway, De-Excitation Mechanism, and Decay Dynamics

Eu³⁺, with the 4f⁶ electronic configuration, generally exhibits bright red f-f emissions arising from its ⁵D₀ multiplet, and Eu³⁺ doped phosphors have attracted lots of attention for applications in lighting and display fields. However, the electron population mechanisms between relevant Eu³⁺ excited states as well as charge-transfer state (CTS) still need to be further clarified since the puzzles on these issues limit the exploration of new luminescent materials and the improvement of the luminescence efficiency of the potential phosphors. In this work, a series of Sr_0.99[La_{(1–<i>x</i>)}Eu_<i>x</i>]_1.01Zn_0.99O_3.495 phosphors was prepared by a high-temperature solid-state reaction technique and was characterized by X-ray diffraction (XRD) measurements at different temperatures, infrared (IR) spectrum, and diffusion reflectance spectra (DRS) at room temperature (RT). The temperature-, doping concentration-, and excitation wavelength-dependent luminescence properties were systematically studied to clarify the population pathway, de-excitation mechanism, and decay dynamics of Eu³⁺ in this low-phonon-frequency compound. The impacts of cross relaxation (CR) and multiphonon relaxation (MPR) processes on the luminescence and decay spectra were investigated in detail. The special coordination polyhedron around Eu³⁺ played a dominant role in the intense Eu^3+ 5D₀–⁷F₄ emission. The CTS peaks shifted to longer wavelengths with increasing temperatures, which seems to relate to the lattice expansion at higher temperatures

Controllable Synthesis of NaLu(WO₄)₂:Eu³⁺ Microcrystal and Luminescence Properties for LEDs

The phosphors of trivalent rare-earth ion Eu³⁺-activated alkaline double tungstates NaLu­(WO₄)₂ were prepared by the EDTA-assisted hydrothermal method. The crystal structure and morphology of the as-synthesized phosphors were determined by powder X-ray diffraction (XRD) and electron microscopes (SEM and TEM), respectively. The phase and morphology can be controlled by different Eu³⁺-doping concentrations. Not less than 40 at % of Eu³⁺ doping concentration pledged the NaLu­(WO₄)₂:Eu³⁺ structure, and the microcrystal changed from hexahedron to tetrahedron with increasing Eu³⁺ doping concentration. It was found that the final product of NaLu_1–<i>x</i>Eu_<i>x</i>(WO₄)₂ (<i>x</i> ≥ 0.4) can not be obtained directly, intermediated by WO₃, Na₂W₂O₇, and REO­(OH). The photoluminescence and electroluminescence properties were also investigated. Because of an intense red emission and a good excitation in the region of near-ultraviolet (n-UV), the samples NaLu­(WO₄)₂:Eu³⁺ can be served as alternative red phosphors in the n-UV chip-based LED application

Mask re-segmentation.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.</div

Extraction performance of AMBBEM and three FCN models at the sella turcica layer.

Extraction performance of AMBBEM and three FCN models at the sella turcica layer.</p

This is the label of the FCN training set.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.</div

Flowchart of AMBBEM.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.</div

Diagram of the process of skull closure.

A) Original image b) Skull image c) Images after skull closure d) Image of mask 1.</p

Process diagram for mask re-segmentation.

a) Initial segmented image b) Image of Mask 2 c) Final mask image d) Final brain extraction image.</p

Analysis of head CT images.

a) Original image b) Mesh surface of the gray value c) Percent bar plot of gray value from 1 to 254.</p

Test results of each algorithm at the sella turcica layers.

Test results of each algorithm at the sella turcica layers.</p