Applications of cationic bamboo fibers for the effective reinforcements of secondary fibers
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Tianhao Huang
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Xiangxing Liu
Abstract
Bamboo, with its long fibers, fast growth, high abundance, and short harvesting cycle, is a promising alternative to traditional wood pulp, particularly in reinforcing secondary fibers during paper recycling. In this study, different from direct addition with poor combination concerning the negative charge in recycled pulp fibers, bamboo fibers were firstly modified with 3-chloro-2-hydroxypropyltrimethylammonium chloride (CHPTAC) to get cationic bamboo fibers, the characterizations by FTIR and N contents (maximum 1.05 %) classified that the hydroxyl groups in bamboo fibers were etherified effectively with greater zeta potentials (maximum −13.2 mV) in mixed pulps. The cationic modification of bamboo fibers resulted in a significant increase in the tensile and burst index of the secondary pulp, by 49.75 % and 61.54 % with only 10 %addition, respectively, as compared to the unmodified bamboo fibers, which was contributed to the mutual attraction between positively charged modified bamboo fibers and negatively charged secondary fibers, besides the original hydrogen bonding inside fibers, which leads to the enhancements for secondary fibers strength. These findings provide valuable insights into the effective applications of bamboo fibers for reinforcing recycled paper and offer promising solutions for the sustainable development of the paper industry.
Funding source: State Key Project of Research and Development Plan of Guangdong Province
Award Identifier / Grant number: 2022B0202020002
Funding source: Nine Dragons Paper
Award Identifier / Grant number: 2106030200502
Acknowledgments
This work is partially supported by High Performance Computing Platform of South China University of Technology.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: The author has 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: To improve language.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: The authors are appreciative of the support of the Key Project of Research and Development Plan of Guangdong Province (2022B0202020002) and Nine Dragons Paper (based on the contract of 2106030200502).
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Data availability: The raw data can be obtained on request from the corresponding author.
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