|
|
|
|
|
|
| Study on Polyols Liquefaction Process of Chinese Sweet Gum (Liquidambar formosana) Fruit by FTIR Spectra With Principal Component Analysis |
| KAN Yu-na1, LÜ Si-qi1, SHEN Zhe1, ZHANG Yi-meng1, WU Qin-xian1, PAN Ming-zhu1, 2*, ZHAI Sheng-cheng1, 2* |
1. College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, China
2. Collaborative Innovation Center for Efficient Utilization of Forestry Resources of Jiangsu Province, Nanjing 210037, China
|
|
|
|
|
Abstract The conversion of lignocellulosic biomass with high content of hydroxyl groups to liquid substances with high reactivity through liquefaction was considered a promising route for realizing their high-value utilization. The competitive reaction of degradation and polycondensation in lignocellulosic biomass’s liquefaction process directly affects the liquefaction product’s characteristics. The liquefaction of Chinese Sweet Gum’s (Liquidambar formosana) fruit was carried out at various times (30, 60, 90, 120, and 150 min) using polyethylene glycol and glycerin (4∶1 V/V) as liquefaction reagents to investigate the degradation and polycondensation reaction process in liquefaction. Fourier infrared spectroscopy (FTIR) combined with principal component analysis (PCA) and X-ray diffraction (XRD) were used to characterize the liquefied residues and liquefied products. The results showed that the liquefaction efficiency gradually increased with the extension of the reaction time, and the highest liquefaction efficiency was 88.79%. The optimal liquefaction time was 120 min when the liquefaction efficiency was 87.91%, and the hydroxyl value of the liquefied product was 280 mg KOH·g-1. FTIR and XRD analysis showed that lignin and hemicellulose were priority degraded at the initial stage of the liquefaction reaction. The crystalline cellulose began degrading at a later stage, accompanied by a polycondensation reaction. Principal component analysis results suggested that the distribution of functional groups of the liquefied residues obtained at different liquefaction times was relatively independent, which could be used as the basis for judging the degradation time of each component in the liquefaction process. Moreover, the polycondensation reaction gradually became dominant after 90 min of liquefaction. In addition, to explore the feasibility of liquefaction products as biomass polyols in the polyurethane foam field, polyurethane foams were successfully prepared by adding different contents of liquefaction products (10%, 20%, and 50%). FTIR showed that liquefaction products could replace polyols in the preparation of polyurethane foam, and the addition of liquefaction products did not change the chemical structure of polyurethane foam. The study would provide a theoretical basis for further exploring the liquefaction reaction of lignocellulosic resources and the liquefaction utilization of L. formosana fruit.
|
|
Received: 2022-02-07
Accepted: 2022-07-05
|
|
|
|
Corresponding Authors:
PAN Ming-zhu, ZHAI Sheng-cheng
E-mail: zhais@njfu.edu.cn；mzpan@njfu.edu.cn
|
|
[1] Hu S J, Luo X L, Li Y B, et al. ChemSusChem, 2014, 7(1): 66.
[2] ZUO Zhi-yue, JIANG Jian-chun, XU Jun-ming(左志越，蒋剑春，徐俊明). Journal of Cellulose Science and Technology(纤维素科学与技术), 2010, 18(4): 55.
[3] Amran U A, Roslan R, Chia CH, et al. Cellulose, 2019, 26(5): 3231.
[4] Chang C, Liu L, Li P, et al. Industrial Crops and Products, 2021, 160: 113098.
[5] Kosmela P, Hejna A, Formela K, et al. Cellulose, 2016, 23(5): 2929.
[6] Shao H, Zhao H, Xie J, et al. International Journal of Polymer Science, 2019, 2019: 1.
[7] LI Gai-yun, ZHU Xian-chao, ZOU Xian-wu, et al(李改云，朱显超，邹献武，等). Chemistry and Industry of Forest Products(林产化学与工业), 2015, 35(1): 107.
[8] LIU Cheng-qian, WANG Chuan-gui, LI Yuan-yuan, et al(刘成倩，王传贵，李媛媛，等). Journal of Anhui Agricultural University(安徽农业大学学报), 2017, 44(6): 1043.
[9] Zhang Q, Zhang G, Han D, et al. Journal of Applied Polymer Science, 2016, 133(44): 44162.
[10] ZHANG Yan, LIU Zhong, HUI Lan-feng(张 妍，刘 忠，惠岚峰). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(5): 1517.
[11] YUAN Cheng, CHEN Bing-wei, HUANG Cao-xing, et al(袁 诚，陈冰炜，黄曹兴，等). Journal of Forestry Engineering(林业工程学报), 2019, 4(3): 52.
[12] Kobayashi M, Kajiyana T A, Tomata B, et al. Journal of Wood Science, 2004, 50: 407.
[13] Kan Y, Zhai S, Chen B, et al. Journal of Polymers and the Environment, 2022, 30: 2355.
[14] Rastegarfar N, Behrooz R, Barikani M. Journal of Polymer Research, 2018, 25(7): 154.
|
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
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. |
| [3] |
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. |
| [4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [5] |
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. |
| [6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
| [7] |
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. |
| [8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
| [9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
| [10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
| [11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
| [12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
| [13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
| [14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
| [15] |
XU Qi-lei, GUO Lu-yu, DU Kang, SHAN Bao-ming, ZHANG Fang-kun*. A Hybrid Shrinkage Strategy Based on Variable Stable Weighted for Solution Concentration Measurement in Crystallization Via ATR-FTIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1413-1418. |
|
|
|
|
