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Preparation of mycelium paper sheets and study on their adsorption properties

  • Weifeng Chen ORCID logo und Xiwen Wang EMAIL logo
Veröffentlicht/Copyright: 12. September 2025
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Abstract

With the progression of society, aquatic pollution has intensified, prompting a search for greener materials for wastewater treatment. Here, we present a mycelium-based paper developed via alkaline deacetylation of fungal mycelial fibers to chitosan, followed by vacuum filtration, and reinforcement with long plant fibers to enhance mechanical performance. The resulting mycelium–paper composite exhibits a highly dense structure, with very low air permeability (0.42 mL/min), tensile strength of approximately 14 MPa, and tear resistance up to 1,384 mN. In filtration tests, a single pass of 30 mg/L Cu2+ solution yielded a removal efficiency of 98 %, corresponding to an adsorption capacity of 8.75 mg/g; for methylene blue dye, the adsorption capacity was 7.42 mg/g. Compared to conventional cellulose-based powder adsorbents, which often suffer from material loss and require post-treatment, the mycelial paper can be formed directly into membrane-like filters, simplifying handling. Furthermore, this mycelial membrane displayed strong adsorption across various heavy metal ions and dyes, underscoring its significant potential for sustainable water purification.


Corresponding author: Xiwen Wang, College of Light Industry Science and Engineering, South China University of Technology, Guangzhou 510641, China; and Engineer State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China, E-mail:

Acknowledgments

Mr. Yin (Analytical & Testing Center of South China University of Technology) was acknowledged to provide characterizations.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable – no individual participants were involved in this study.

  3. Author contributions: Weifeng Chen contributed to methodology, writing-original draft, investigation and conceptualization; Xiwen Wang contributed to supervision, conceptualization and formal analysis.

  4. Use of Large Language Models, AI and Machine Learning Tools: Large Language Models, Artificial Intelligence, or Machine Learning Tools (e.g., ChatGPT) were used in [describe purpose, e.g., language editing or figure generation]. The authors retained full control and responsibility for all content. Such tools are not credited as authors and do not take responsibility for the work.

  5. Conflict of interest: The authors declare no conflicts of interest.

  6. Research funding: This research received no external funding.

  7. Data availability: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

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Received: 2025-06-25
Accepted: 2025-09-02
Published Online: 2025-09-12
Published in Print: 2025-12-17

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 18.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/npprj-2025-0042/html?lang=de
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