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Chitosan surface coating modified submicron ordered porous zirconia

  • Zhi-cheng Zhao EMAIL logo , Li-li Wang EMAIL logo , Xu-ri Hou , Xiao-he He and Hong-tao Jiang
Published/Copyright: September 22, 2025
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Abstract

Ordered porous ZrO2 has good biocompatibility and chemical stability, however, it exhibits low bioactivity and insufficient drug loading. In this study, submicron ordered porous ZrO2 skeleton was constructed using polystyrene colloidal crystal with an average size of 820 ± 10 nm as the template. It was then modified by chitosan (CS) surface coating to change its surface functional groups, thereby improving its adsorption capacity and obtaining surface bioactivity to expand its potential application in the field of drug slow release. The factors affecting ordered porous morphology of the composite and their influencing rules were studied in detail. The results indicated that morphology of the composite was greatly affected by the concentration of CS, coating time, molecular weight of CS, and degree of deacetylation (DD). When the CS concentration was 1 wt%, the coating time was 1 h, the CS molecular weight was 200 kDa, and the DD was 90 %, the composite had regular ordered porous morphology and uniform thickness, which is expected to become a high performance drug sustained-release carrier material.


Corresponding authors: Zhi-cheng Zhao and Li-li Wang, School of Materials Science and Engineering, Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials, Shaanxi University of Science and Technology, Xi’an, 710021, Shaanxi, China, E-mail: (Z.-c. Zhao), (L.-l. Wang).

Funding source: Natural Science Foundation of Science and Technology Department of Shaanxi Province

Award Identifier / Grant number: 2024JC-YBMS-444

  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. Zhi-cheng Zhao: investigation, visualization, writing - original draft and revision. Li-li Wang: methodology, conceptualization, formal analysis, funding acquisition, resources. Xu-ri Hou: investigation, data curation, supervision. Xiao-he He: data curation, improvement of paper. Hong-tao Jiang: supervision, revision guidance.

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

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by Natural Science Foundation of Science and Technology Department of Shaanxi Province (2024JC-YBMS-444), China.

  7. Data availability: The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Received: 2024-12-27
Accepted: 2025-06-27
Published Online: 2025-09-22

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