Effect of sulfonation pretreatment on the extraction of hemicellulose from poplar wood
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Tiantian Li
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Wenguang Li
Abstract
Although a variety of hemicellulose extraction methods have been reported, there is a shortage of processes that can reliably produce high molecular weight hemicellulose from fast growing hardwoods such as poplar under mild conditions while maintaining economic viability and reagent recyclability. To address this gap, we developed and optimized a two-stage sulfation–KOH treatment for selective delignification and mild solubilization of hemicellulose from poplar. The optimized sulfonation conditions were: Na2SO3 dosage of 19.77 %, temperature of 147.15 °C, and NaOH dosage of 2.34 %, resulting in a sulfonic acid group content of 114.78 mmol/kg. After recycling the sodium sulfite from the waste solution three times, the sulfonic acid group content of the reused sulfonated slurry remained at 104.09 mmol/kg. Following treatment of the sulfonated solids with 15 wt% KOH solution at room temperature (25 °C) for 1 h, the hemicellulose removal rate reached 69.01 %, lignin removal rate 11.36 %, and cellulose retention rate 95.79 %. Meanwhile, the extracted hemicellulose exhibited a high molecular weight (92,058 g/mol), indicating that the process can recover high-quality hemicellulose under mild conditions. This method therefore offers a feasible pathway for upgrading traditional papermaking feedstocks toward higher-value bio-based materials and supports more sustainable biomass refining strategies with potential economic and environmental benefits.
Acknowledgments
We are grateful for the financial support of Guangdong Basic and Applied Basic Research Foundation (2024A1515011941) and National Natural Science Foundation of China (No. 32271807). Thanks to Yili Liang of Beijing Tianyao Technology Co., Ltd. for his assistance with SEM testing.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: National Natural Science Foundation of China (No. 32271807) and Guangdong Basic and Applied Basic Research Foundation (2024A1515011941).
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Data availability: The raw data can be obtained on request from the corresponding author.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/npprj-2025-0047).
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