Handmade paper is a major carrier and restoration material of traditional Chinese ancient books, calligraphies, and paintings. In this study, we carried out a Raman spectroscopy analysis of 18 types of handmade paper samples. The main components of the handmade paper were cellulose and lignin, according to the wavenumber and Raman vibration assignment. We divided its Raman spectrum into eight subbands. Five machine learning models were employed: principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), k‐nearest neighbors (KNN), and random forest (RF). The Raman spectral data were normalized, and the fluorescence envelope was subtracted using the airPLS algorithm to obtain four types of data, raw, normalized, defluorescence, and fluorescence data. An RF variable importance analysis of data processing showed that data normalization eliminated the intensity differences of fluorescence signals caused by lignin, which contained important information of raw materials and papermaking technology, let alone the data defluorescence. The data processing also reduced the importance of the average variables in almost all spectral bands. Nevertheless, the data processing is worthwhile because it significantly improves the accuracy of machine learning, and the information loss does not affect the prediction. Using the machine learning models of PCA, PLS, and SVM combined with linear regression (LR), KNN, and RF, the classification and prediction of handmade paper samples were realized. For almost all processed data, including the fluorescence data, PCA‐LR had the highest classification and prediction accuracy (R2 = 1) in almost all spectral bands. PLS‐LR and SVM‐LR had the second‐highest accuracies (R2 = 0.4–0.9), whereas KNN and RF had the lowest accuracies (R2 = 0.1–0.4) for full band spectral data. Our results suggest that the abundant information contained in Raman spectroscopy combined with powerful machine learning models could inspire further studies on handmade paper and related cultural relics.

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A novel hollow-core anti-resonant fiber (HC-ARF) with glass-sheet conjoined nested tubes that supports five core modes of LP01-LP31 with low mode couplings, large differential group delays (DGDs), and low bending losses (BLs) is proposed. A novel cladding structure with glass-sheet conjoined nested tubes (CNT) is induced for the proposed HC-ARF which can suppress mode couplings between the LP01-LP31 modes and the cladding modes. The higher-order modes (HOMs) which are LP11-LP31 modes also have very low loss by optimizing the radius of the nested tube and the core radius. Moreover, the large effective refractive index differences Δneff between HOMs are all larger than 1 × 10−4 which contributes to a large DGD in the wavelength range from 1.3 to 1.7 µm. The bending loss of the HC-ARF is analyzed and optimized emphatically. Our calculation results show that bending losses of LP01-LP31 modes are all lower than 3.0 × 10−4 dB/m in the wavelength range from 1.4 to 1.61 µm even when the fiber bending radius of the HC-ARF is 6 cm.

A novel highly birefringent and low transmission loss hollow-core anti-resonant (HC-AR) fiber with a central strut is proposed for terahertz waveguiding. To the best of our knowledge, it is the first time that a design of a highly birefringent terahertz fiber based on the hybrid guidance mechanism of the anti-resonant mechanism and the total internal reflection mechanism is provided. Several HC-AR fibers with both ultra-low transmission loss and ultra-low birefringence have been achieved in the near-infrared optical communication band. We propose a HC-AR fiber design in terahertz band by introducing a microstructure in the fiber core which leads to tremendous improvement in birefringence. Calculated results indicate that the proposed HC-AR fiber achieves a birefringence higher than 10−2 in a wide wavelength range. In addition, low relative absorption loss of 0.8% (8.6%) and negligible confinement loss of 1.69×10−4 dB/cm (9.14×10−3 dB/cm) for x-polarization (y-polarization) mode at 1THz are obtained. Furthermore, the main parameters of the fiber structure are evaluated and discussed, proving that the HC-AR fiber possesses great design and fabrication tolerance. Further investigation of the proposed HC-AR fiber also suggests a good balance between birefringence and transmission loss which can be achieved by changing strut thickness to cater numerous applications ideally.