Abstract In the world of light-emitting diodes, Tm 3+ -doped glass ceramics are a crucial fluorescent material. In this study, melt-crystallization was used to create glass ceramics that were Tm 3+ -doped and included crystalline NaLa(MoO 4 ) 2 . X-ray diffraction (XRD), scanning electron microscopy (SEM), transmittance, and photoluminescence spectroscopy were used to examine the structure, morphology, and luminescence characteristics of glass ceramics (PL). According to the findings, keeping the precursor glass at 660 °C for two hours produced microcrystals with an average size of 280 nm. Also covered is how the concentration of Tm 2 O 3 doping affects the luminous characteristics of glass ceramics. The strongest blue light is produced at 454 nm ( 1 D 2 → 3 F 4 ) when Tm 2 O 3 concentration is 0.8 mol%. The findings demonstrate that Tm 3+ -doped glass ceramics containing NaLa(MoO 4 ) 2 crystalline phase have promise for use in the area of color displays.
The effect of heat treatment temperature on crystallization behavior of Na2O-Y2O3-SiO2 silicate glass system was studied by melting and crystallization method, and a kind of glass-ceramics containing Na3YSi2O7 crystal phase was obtained. The structure and morphology of the glass-ceramics were studied by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and transmittance. The results show that the average size of 280nm microcrystals can be obtained by holding the precursor glass at 610 °C for 2 h. With the increase of heat treatment temperature from 610 °C to 640 °C, the permeability of glass-ceramics began to decline, and the glass gradually changed into glass-ceramics, and finally emulsified into ceramics. The results show that Na2O-Y2O3-SiO2 silicate glass system is sensitive to temperature, and its microstructure changes obviously in a short time. Therefore, the glass containing Na3YSi2O7 crystal phase is expected to become a new material with high performance, low price and wide application market because of its excellent performance, simple preparation technology and low manufacturing cost.
Abstract Tb 3+ ‐doped and Eu 3+ /Tb 3+ co‐doped transparent glass ceramics containing Na 3 Gd(PO 4 ) 2 crystal phase were prepared by melt crystallization. The types of crystals, grain size, and optical transmittance of the samples were investigated by X‐ray diffraction (XRD), scanning electron microscope (SEM), and UV visible absorption spectrometer. The effect of different heat treatment time on grain size, crystal morphology, and light transmittance was also studied. The luminescence performance of transparent glass ceramics was systematically studied by the excitation spectrum, emission spectrum, and the fluorescence lifetime. The optimal doping concentration of Tb 3+ doped and Eu 3+ /Tb 3+ co‐doped were determined. When the mole percent concentration of Tb 4 O 7 was 0.4%, the strongest green light was obtained at 546 nm ( 5 D 4 → 7 F 5 ). Meanwhile, the energy transfer process between Eu 3+ and Tb 3+ was discussed. Under the excitation of 374 nm, white light can be obtained by adjusting the concentration ratio of Eu 3+ and Tb 3+ .
Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets.
Abstract Fluorophosphate glass is the core of laser materials and also has an important reference value for an optical lens. According to Kirchhoff’s law, this paper focuses on the optical loss of incident light entering fluorophosphate glass. The transmittance and refractive index of optical glass were measured by an ultraviolet-visible spectrophotometer (Uv) and V-prism tester. With the sample thickness increasing from 10 mm to 50 mm, the transmittance of grade 4 fluorophosphate glass decreases from 85.32 % to 76.91 %, and the scattering rate increases from 8.4704 % to 17.0591 %. The microstructure and internal images of macroscopic defects were obtained by projective optical microscopy. In addition, an electron probe (EPMA) was used to analyze the defective components and identify the main components of the defects. The results show that the high content of P/Al/O element is the main cause of optical loss.
Abstract In this work, the sintering kinetics of Nd: YAG transparent ceramics under the vacuum environment was studied by the high-temperature solid phase method. The effects of different sintering temperatures and TEOS content on the densification process of Nd: YAG ceramics were studied. Meanwhile, the crystal structure of Nd: YAG transparent ceramics doped with different contents of TEOS was studied by X-ray diffractometer (XRD) and differential thermal analysis (DTA). The micrographs of ceramics samples were analyzed by scanning electron microscope (SEM). The green sample is heated to 1450~1650°C for 0~2 h at a heating rate of 20°C/min in a vacuum environment (P T ≤ 10 -6 Pa), and the shrinkage of ceramic samples was analyzed in the vacuum atmosphere by the Johnson sintering model. The sintering activation energy of Nd: YAG transparent ceramics doped with different contents of TEOS was obtained. The results indicated that the addition of TEOS promoted the densification of ceramic samples. The grain size and relative density of ceramic samples increased, and the sintering activation energy decreased with the increase of TEOS content.
Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and the El Niño phenomenon. This paper proposes an improved double-output residual neural network (DO-ResNet) model to concurrently estimate the subsurface temperature (ST) and subsurface salinity (SS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), sea surface wind (SSW), and geographical information (including longitude and latitude). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R2) were employed to evaluate the model’s performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R2 values for estimating ST (SS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model’s ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. In conclusion, this research introduces a novel artificial intelligence technique for estimating ST and SS in the tropical Western Pacific Ocean.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)