Glasses in the 30La2O3‐40TiO2‐30Nb2O5 system are known to have excellent optical properties such as refractive indices over 2.25 and wide transmittance within the visible to mid‐infrared (MIR) region. However, titanoniobate glasses also tend to crystallize easily, significantly limiting their applications in optical glasses due to processing challenges. Therefore, the 30La2O3‐40TiO2‐(30−x) Nb2O5‐xAl2O3 (LTNA) glass system was successfully synthesized using a aerodynamic containerless technique, which improves glass thermal stability and expands the glass‐forming region. The effects of Al2O3 on the structure, thermal, and optical properties of base composition glasses were investigated by XRD, DSC, NMR, Raman spectroscopy, and optical measurements. DSC results indicated that as the content of Al2O3 increased, the thermal stability of the glasses and glass‐forming ability increased, as the 30La2O3‐40TiO2‐25Nb2O5‐5Al2O3 (Nb‐Al‐5) glass obtained the highest ΔT value (103.5°C). Structural analysis indicates that the proportion of [AlO4] units increases gradually and participates in the glass network structure to increase connectivity, promoting more oxygen to become bridging oxygen and form [AlO4] tetrahedral linkages to [TiO5] and [NbO6] groups. The refractive index values of amorphous glasses remained above 2.1 upon Al2O3 substitution, and a transmittance exceeding 65% in the visible and mid‐infrared range. The crystallization activation energies of 30La2O3‐40TiO2‐30Nb2O5 (Nb‐Al‐0) and Nb‐Al‐5 glasses were calculated to be 611.7 and 561.4 kJ/mol, and the Avrami parameters are 5.28 and 4.96, respectively. These results are useful to design new optical glass with good thermal stability, high refractive index and low wavelength dispersion for optical applications such as lenses, endoscopes, mini size lasers, and optical couplers.
Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.
The W-Nb co-doped VO2 films are prepared through hydrothermal method. The effects of the Nb and W dopants are investigated respectively on the phase transition temperature (θc) and optical properties of VO2 by keeping the concentration of partner dopant at 1.0 at.%. The Nb doping induces a reduction of θc at a rate of ~ -13.0 °C per at.% Nb as Nb is less than ~1.5 at.%. For more than 1.5 at.% Nb, the θc shows a slight increase from ~23.0 °C. The W doping leads to a linear decrease of θc with a rate of ~ -17.2 °C per at.% W, more effective in reducing θc than the Nb dopant. However, the heavy W doping results in more serious deterioration of the solar energy modulation (ΔTsol) than the Nb doping. Therefore, taking use of the complementary advantages of W and Nb dopants, the 1.0 at.% W + 1.5 at.% Nb co-doped VO2 realizes the room-temperature transition at 23.0 °C with a satisfactory ΔTsol of ~9.6%, much better than the 1.5 at.% W + 1.0 at.% Nb co-doped which has a θc of ~22.1 °C and ΔTsol of ~5.3%. This work demonstrates the W-Nb co-doping is an effective doping formula in improving the performance of VO2 for smart window applications.
Thermal error stability (STE) of the spindle determines the machining accuracy of a precision machine tool. Here we propose a thermal error feedback control based active cooling strategy for stabilizing the spindle thermal error in long-term. The strategy employs a cooling system as actuator and a thermal error regression model to output feedback. Structural temperature measurements are considerably interfered by the active cooling, so the regression models trained with experimental data might output inaccurate feedbacks in unseen work conditions. Such inaccurate feedbacks are the major cause for excessive fluctuations and failures of the thermal error control processes. Independence of the thermal data is analyzed, and a V-C (Vapnik-Chervonenkis) dimension based approach is presented to estimate the generalization error bound of the regression models. Then, the model which is most likely to give acceptable performance can be selected, the reliability of the feedbacks can be pre-estimated, and the risk of unsatisfactory control effect will be largely reduced. Experiments under different work conditions are conducted to verify the proposed strategy, the thermal error is stabilized to be within a range smaller than 1.637μm, and thermal equilibrium time is advanced by more than 78.3%.
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