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Preparation of MgO/CaCO3 nanocomposites and their deacidification properties for paper documents

  • Chun Gui , Xiaoguang Jiao , Kun Long , Zhipei Yan , Huanhuan Zhu , Caihong Feng , Qingze Jiao , Yun Zhao EMAIL logo and Zhouling Tian EMAIL logo
Published/Copyright: April 30, 2025
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Abstract

Acidification is an important factor in the damage of paper documents. At present, it is recognized that deacidification is an effective means to solve the problem. In this work, aiming at the problems of the traditional single deacidification agent, such as uncontrollable alkali strength, insufficient alkaline reserve, uneven deacidification effect, and high local alkalinity, MgO/CaCO3 nanocomposites were prepared as a new paper deacidification agent. The composites combined the immediate and efficient deacidification of MgO and the long-term mild deacidification of CaCO3. The morphologies and structures of MgO/CaCO3 nanocomposites were characterized by TEM and XRD. When applied to the deacidification of paper documents, the MgO/CaCO3 nanocomposites showed excellent deacidification performances and high alkaline reserve. The alkali strength of the MgO/CaCO3 nanocomposites can be controlled by adjusting the ratio of MgO to CaCO3 so that the pH value and alkaline reserve of the paper after deacidification are within a reasonable range.


Corresponding authors: Yun Zhao, Beijing Key Laboratory for Chemical Power Source and Green Catalysis, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China, E-mail: ; and Zhouling Tian, National Library of China, 33 Zhongguancun Nandajie, Haidian District, Beijing 100081, China, E-mail:
Chun Gui and Xiaoguang Jiao contributed equally to this work.
  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All other authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: All data generated or analyzed during this study are included in this published article.

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Received: 2024-12-13
Accepted: 2025-04-16
Published Online: 2025-04-30
Published in Print: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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