|
|
|
|
|
|
Liquefaction Pathway of Corn Stalk Cellulose in the Presence of
Polyhydric Alcohols Under Acid Catalysis |
ZHANG Yan1, 2, GAO Zhuang-zhi1, QIAO Wen-pu1, YANG Yu-jie1, CHANG Zi-yang1, LIU Zhong2 |
1. Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, Zhejiang University of Science and Technology, Hangzhou 310023, China
2. Tianjin Key Lab of Pulp & Paper, Tianjin University of Science & Technology, Tianjin 300457, China
|
|
|
Abstract The development of high-value utilization paths for agricultural and forestry waste was highly consistent with the major strategic demand to “further promote green and low-carbon energy transformation”. In this paper, the objective was to investigate the pathway of propanediol (PG) and diethylene glycol (DEG) liquefaction catalyzed by phosphoric acid of cellulose from corn stalk at atmosphere pressure, aiming at understanding the mechanism of lignocellulosic biomass liquefaction reaction under the action of acid-catalyzed polyhydroxy alcohols. The chemical groups, molecular weight and distribution, molecular structure, and pyrolyzation of cellulosebiofuels were analyzed by Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectra (NMR), gelpermeation chromatography (GPC), and thermo gravimetric analysis (TGA). FTIR showed that the biofuels had similar FTIR characteristics. At the early liquefaction stage, cellulose degradation produced more hydrocarbons, ethers, and carbonyl compounds. At the later stage of liquefaction, the carbohydrate degradation products, hydroxyl or olefin in cellulose, reacted with PG/DEG to generate organic matter insoluble in 1, 4-dioxane. GPC explained that with the progress of the reaction, the breakage degree of the cellulose molecular chain would be aggravated, and more and more low molecular weight (LMW) substances were generated by degradation. However, when the reaction time reached a certain value, the degradation products reacted with PG/DEG to produce larger molecular weight substances, resulting in the molecular weight of biofuel no longer being reduced. Results from 1H- and 13C-NMR presented that cellulose was degraded under liquefaction, and the molecular chain was broken, but part of the glucose structure was still preserved. With the reaction, these structural units were transformed again to produce LMW compounds. When the reaction continues, polymerization reactions could occur between these products or with PG/DEG, forming new substances with consistent structures and gradually stable properties. The results of TGA showed that the biofuel contained 70%~85% compounds with carbon number less than 25 and 5%~10% compounds with carbon number more than 25. In conclusion, this paper revealed the liquefaction reaction process of cellulose by studying the structural changes in the liquefaction process of polyhydric alcohols, which laid a theoretical foundation for exploring the liquefaction mechanism of corn stalk whole components.
|
Received: 2023-04-18
Accepted: 2023-11-27
|
|
|
[1] Kosmela P, Gosz K, Kazimierski P, et al. Cellulose, 2019, 26(10): 5893.
[2] ZHANG Yan, WANG Hui-le, LIU Zhong, et al(张 妍,王慧乐,刘 忠,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2023, 43(3): 911.
[3] Shi Y, Feng X, Yang R. Materials Research Express, 2021, 8(1): 015506.
[4] Fraga A D C, Almeida M B B D, Sousa-Aguiar E F. Cellulose, 2021, 28: 2003.
[5] Fraga A, Almeida M, Sousa-Aguiar E F. Cellulose, 2021, 28: 2003.
[6] Yamada T, Ono H. J. Wood Sci., 2001, 47(6): 458.
[7] Zhang Y, Wang H L, Sun X D, et al. Bioresources, 2021, 16(4): 7205.
[8] Zhang Y, Liu Z, Liu H T, et al. BioResources, 2019, 14(2): 2684.
[9] Bahceli S, Sarikaya E K, Dereli Ö. Chemistry Select, 2024, 9(12): e202400054.
[10] Zhang Y, Wang H L, Zhao H F, et al. Bioresources, 2022, 17(3): 4262.
[11] Biswas B, Bisht Y, Kumar J, et al. Biomass Conversion and Biorefinery, 2022, 12(1): 91.
[12] Zhang Y, Liu Z, Hui LF, et al. Bioresources, 2018, 13(3): 6818.
[13] Volpe M, Messineo A, Mkel M, et al. Fuel Process Technol., 2020, 206: 106456.
[14] García T, Vesés A, López J M, et al. ACS Sustain Chem. Eng., 2017, 10: 8710.
[15] Ghl A, Zmz A, Fjl A, et al. Fuel Process Technol., 2019, 185: 18.
[16] Zhang Y, Liu Z, Liu H T, et al. Carbohydrate Polymers, 2019, 215: 170.
|
[1] |
WANG Pei-lian1, YUE Su-wei2*, LI Jia-yan1. Spectral Characteristics and Color Mechanism of Heat-Treated
Gem-Quality Yellow Sphene[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2545-2550. |
[2] |
LU Si, CHEN Xiao-li, SU Qiu-cheng, QI Wei, XIA Sheng-peng, LI Ming, FU Juan*. The Study of Experimental Method on the Characterization of Acidic Properties of Zeolites by in Situ FTIR-Pyridine Adsorption[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2488-2493. |
[3] |
LÜ Shu-xian. A Study on the Non-Destructive Method of Identifying Chinese Traditional Handmade Paper With Attenuated Total Reflection Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2450-2458. |
[4] |
ZHU Yu-kang1, LU Chang-hua1, ZHANG Yu-jun2, JIANG Wei-wei1*. Quantitative Method to Near-Infrared Spectroscopy With Multi-Feature Fusion Convolutional Neural Network Based on Wavelength Attention[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2607-2612. |
[5] |
MAO Ya-chun1, WEN Jie1*, CAO Wang1, DING Rui-bo1, WANG Shi-jia2, FU Yan-hua3, XU Meng-yuan1. Fusion Algorithm Research Based on Imaging Spectrum of Anshan Iron Ore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2620-2625. |
[6] |
ZHANG Xiao-dong1, KANG Hong-dong1, LI Bing-hui2, ZHANG Shuo1*, HAN Lei1. Spectroscopic Differences in Different Solvent Pretreated Coals in the Presence of ScCO2 and Their Mechanisms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2657-2666. |
[7] |
WENG Ding-kang1, FAN Zheng-xin1, KONG Ling-fei1, SUN Tong1*, YU Wei-wu2. Rapid Identification of Shelled Bad Torreya Grandis Seeds Based on
Visible-Near Infrared Spectroscopy and Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2675-2682. |
[8] |
XIAO Nan1, LI Han-lin1, WENG Ding-kang1, HU Dong1, SUN Tong1*, XIONG Yong-sen2. Rapid Identification of Apple Moldy Core Disease by Near Infrared
Spectroscopy With Information Fusion of Different Illumination
Patterns[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2388-2394. |
[9] |
WU Bin1, XIE Chen-ao2, CHEN Yong2, WU Xiao-hong2, JIA Hong-wen1. Discrimination of Chuzhou Chrysanthemum Tea Grades Using Noise
Discriminant C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2202-2207. |
[10] |
HUANG Ya-hao1, XUE Yi-fan1, WEN Zhi-gang1, CHEN Jun-lin2, QIAO Zhan-feng3, 4*, LIU Yi-cheng1. Quantitative Fourier Infrared Spectroscopy Model of a Single CO2 System Under High Temperature and Pressure and Its Application to Natural
Inclusions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2256-2261. |
[11] |
WANG Shu-tao1, WAN Jin-cong1*, LIU Shi-yu2, ZHANG Jin-qing1, WANG Yu-tian1. Qualitative Modeling Method of Mango Species in Near Infrared Based on Attention Mechanism Residual Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2262-2267. |
[12] |
HU Cai-ping1*, FU Zhao-min2*, XU Hong-jia2, WU Bin3, SUN Jun4. Discrimination of Lettuce Storage Time Based on Near-Infrared Spectroscopy Combined With Fuzzy Uncorrelated QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2268-2272. |
[13] |
LI Zhen-yu1, ZHAO Peng1, 2*, WANG Cheng-kun3. Tree Class Recognition in Open Set Based on an Improved Fuzzy
Reasoning Classifier[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1868-1876. |
[14] |
SUN Bai-ling, WANG Xiao-qing, CHAI Yu-bo*. Effects of Vacuum Heat Treatment on Morphology and Structure of Larch Wood Cellulose[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1928-1933. |
[15] |
XU Xiao-dong, ZHANG Hui-min, LIU Jia-le, HAN Lu-jia, YANG Zeng-ling, LIU Xian*. Study on Infrared Spectral Recognition of Microplastics in Fishmeal Based on XGBoost Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 1835-1842. |
|
|
|
|