|
|
|
|
|
|
A NIR Study on Hydrogen Bonds of Bamboo-Based Cellulose Ⅱ |
DONG Pei-jie1, ZHANG Wen-bo1*, LU Wei2 |
1. College of Material Science and Technology, Beijing Forestry University, Beijing 100083, China
2. China Information Center of Light Industry, Beijing 100833, China |
|
|
Abstract Cellulose is a renewable natural hydrophilic polymer, and its huge hydrogen bond grid forms a variety of different crystal structures. There are five crystalline variants of cellulose (cellulose Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅹ), of which cellulose Ⅱ is formed from cellulose Ⅰ (natural cellulose) after regeneration or mercerization, is the lowest surface free energy and the most stable performance among the five crystal varieties, mainly due to the antiparallel chain structure of cellulose Ⅱ, which is opposite to the parallel chain structure of cellulose I, and has additional intermolecular hydrogen bonds compared with cellulose I. Therefore, in view of the sensitivity of near-infrared spectroscopy (NIRS) to the hydrogen-containing group, and the crystalline structure of cellulose contains a large number of hydrogen bonds, this makes it possible for NIRS to analyze the degree of hydrogen bond destruction of cellulose by hydrogen containing functional groups, and to detect and quantitatively evaluate the crystalline structure of cellulose qualitatively. So far, there are very few studies on the hydrogen bonding of cellulose crystal variants, and the hydrogen bonding of bamboo cellulose Ⅱ and its derivatives has not been reported at home and abroad. In the study, cellulose Ⅰ was prepared from bamboo, and bamboo-based cellulose Ⅱ was obtained through mercerization, which NIRS investigated hydrogen bonds, the results were compared with bamboo powder and bamboo-based cellulose Ⅰ. Besides, the crystallinity of bamboo powder and bamboo-based cellulose was quantitatively evaluated by NIRS. The results can be drawn as follows: (1) the differences of NIRS among bamboo powder, bamboo-based cellulose Ⅰ and Ⅱ varied little, hydrogen bonding were quantitatively remarkable, but were qualitatively slight in the amorphous region; (2) compared with bamboo powder, the crystal structure of bamboo-based cellulose I remained unchanged, while bamboo-based cellulose Ⅱ occurred two absorbance peaks in the semi-crystalline region; (3) in the crystalline region a strong hydrogen bonding absorbance peak reflected the first overtone of hydroxyl group stretching vibration was observed at the wavenumber of 6 292 cm-1 assigned to the intermolecular bond of O2—H2···O6 of cellulose Ⅰ, which shifted to 6 354 cm-1 for bamboo-based cellulose Ⅱ. We deduced the absorbance peak in cellulose Ⅱ was assigned to the intermolecular bond of O2—H2···O2 due to anti-parallel structure of cellulose confirmation; (4) a good correlation among crystallinity was obtained by NIRS with the results of XRD analysis. The above research shows that the hydrogen bonding in the crystalline region of cellulose shifts in the near-infrared characteristic band and forms double peaks in the semi-crystalline region, which were the main characteristics of bamboo-based cellulose Ⅱ different from bamboo-based cellulose Ⅰ. Simultaneously, it is feasible to use NIRS to study the hydrogen bonding of various celluloses and predict their crystallinity.
|
Received: 2020-08-02
Accepted: 2020-12-07
|
|
Corresponding Authors:
ZHANG Wen-bo
E-mail: kmwenbo@bjfu.edu.cn
|
|
[1] Nagarajan S, Skillen N C, Irvine J T S, et al. Renewable & Sustainable Energy Reviews, 2017, 77(9): 182.
[2] Wang H Y, Li D G, Yano H, et al. Cellulose, 2014, 21(3): 1505.
[3] Wang H Y, Chen C C, Fang L, et al. Cellulose, 2018, 25(3): 7003.
[4] Horikawa Y. Cellulose, 2017, 24(5): 1.
[5] Inagaki T, Siesler H W, Mitsui K, et al. Biomacromolecules, 2010, 11(9): 2300.
[6] Segal L, Creely J J, Martin A E, et al. Textile Research Journal, 1959, 29: 786.
[7] French A D. Cellulose, 2014, 21(2): 885.
[8] Schwanninger M, Rodrigues J, Fackler K. Journal of Near Infrared Spectroscopy, 2011, 19(5): 287.
[9] Tsuchikawa S, Siesler H W. Applied Spectroscopy, 2003, 57(6): 667.
[10] Mitsui K, Inagaki T, Tsuchikawa S. Biomacromolecules, 2008, 9(1): 286.
[11] Frackler K, Schwanninger M. Journal of Near Infrared Spectroscopy, 2011, 19(5): 359.
[12] Sandak A, Sandak J, Negri M. Wood Science and Technology, 2011, 45(1): 35.
[13] Watanabe A, Morita S, Ozaki Y. Applied Spectroscopy, 2006, 60(6): 611.
[14] Tsuchikawa S, Yonenobu H, Siesler H W. Analyst, 2005, 130(3): 379.
[15] Yonenobu H, Tsuchikawa S. Applied Spectroscopy, 2003, 57(11): 1451. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
[6] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[7] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[8] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[9] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[10] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[15] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
|
|
|
|