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Effects of dispersion hydrophobized MgO nanoparticles in low polarity solvent on aged paper

  • Baihe Yun , Huiming Fan EMAIL logo , Zhuoyao Ma , Jianan Liu , Binyi Lin and Wanqi Zhou
Published/Copyright: June 4, 2025
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Abstract

Paper documents hold significant cultural and historical value, with acidification being a primary factor in their degradation. To protect acidic paper, a deacidifying dispersion was developed using oleyl alcohol-modified magnesium oxide (MgO) nanoparticles in a mixture of hexamethyldisiloxane and ethanol (8:2). The results showed that ethanol can form a solvation layer on the particle surface, provided short-range solvation repulsion, and improved the dispersion stability of the particles. The modified nano MgO changed from hydrophilic to lipophilic in hexamethyldisiloxane and ethanol (8:2) for 12 h with good dispersibility. The use of these sustainable dispersions was implemented for paper deacidification, and experiments were performed to evaluate the effectiveness of this innovative approach. The results showed that the pH value of the paper surface increased to about 8 after treatment, and the pH value of the paper was retained to 7 after aging, and the tensile strength did not decrease significantly under the condition of accelerated aging. It is important to note that the deacidification process has a negligible effect on the visual appearance of the paper as well as the integrity of the ink on its surface and the paper’s hydrophobicity.


Corresponding author: Huiming Fan, State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, 510641, China; and National Engineering Research Center for Pulp, Paper and Pollution Control, South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, 510641, China, E-mail:

Award Identifier / Grant number: No. 2022YFF0904200

  1. Research ethics: This study was approved by the Ethics Committee of South China University of Technology. All participants provided written informed consent after being fully informed of the research purpose and data usage.

  2. Informed consent: Informed consent was obtained from all individuals participants included in the study.

  3. Use of Large Language Models, AI and Machine Learning Tools: This study did not use Large Language Models, AI and Machine Learning Tools.

  4. Conflict of interest: There is no conflict of interest in this study.

  5. Research funding: National Key Research and Development Program of China (No. 2022YFF0904200).

  6. Data availability: All data are provided in raw or processed format as specified in the Methods section.

  7. Author contributions: Baihe Yun: Conceptualization, Methodology, Writing, original draft, Writing review & editing, Data curation, Software, Validation. Huiming Fan: Conceptualization, Methodology, Funding acquisition. Zhuoyao Ma: Conceptualization, Data curation, Software, Writing review and editing. Jianan Liu: Conceptualization, Methodology, Supervision. Binyi Lin: Methodology. Conceptualization, Supervision. Wanqi Zhou: Conceptualization, Supervision.

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Received: 2024-08-29
Accepted: 2025-03-10
Published Online: 2025-06-04
Published in Print: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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