Yuanjia Xia, Fang Zhao^*, Zhizun Li, Zhaogang Cheng, Jianwei Hu

Journal of Renewable Materials, Vol.11, No.2, pp. 921-936, 2023, DOI:10.32604/jrm.2022.022840

Abstract Sn_1−xEr_xO₂ (x = 0%, 8%, 16%, 24%) micro/nanofibers were prepared by electrospinning combined with heat treatment using erbium nitrate, stannous chloride and polyvinylpyrrolidone (PVP) as raw materials. The target products were characterized by thermogravimetric analyzer, X-ray diffrotometer, fourier transform infrared spectrometer, scanning electron microscope, spectrophotometer and infrared emissivity tester, and the effects of Er³⁺ doping on its infrared and laser emissivity were studied. At the same time, the Sn_1−xEr_xO₂ (x = 0%, 16%) doping models were constructed based on the first principles of density functional theory, and the related optoelectronic properties such as their energy band structure, density of states,… More >

Meng Da¹, Huiqin Wang^1,*, Ke Wang¹, Zhan Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 513-527, 2022, DOI:10.32604/cmes.2022.019040

Abstract The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the… More >