The increasing demand for clean water, coupled with growing concerns over energy consumption and environmental impact, has intensified the search for sustainable materials and fabrication strategies for water treatment technologies. Polymer composites have emerged as highly promising candidates due to their tunable chemistry, lightweight nature, and compatibility with functional fillers. At the same time, additive manufacturing (AM) offers unique advantages in terms of design freedom, material efficiency, and customizable architectures. This review provides a comprehensive assessment of sustainable polymer composites fabricated via additive manufacturing for advanced water treatment applications. Major AM techniques, including material extrusion, vat photopolymerization, material jetting, powder bed fusion, binder jetting, and sheet lamination, are critically evaluated with respect to their printability, design flexibility, and environmental footprint. Emphasis is placed on sustainable polymer matrices such as polylactic acid, polyhydroxyalkanoates, cellulose-based polymers, and recycled plastics, as well as eco-friendly fillers and functional additives, including biochar, lignin, chitosan, nanocellulose, clays, zeolites, hydroxyapatite, and functional nanomaterials (e.g., AgNPs, TiO2, ZnO, and graphene). The role of composite architecture, surface modification, and hierarchical porosity enabled by AM in enhancing adsorption, catalytic activity, and antimicrobial performance is highlighted. This review demonstrates that integrating sustainable materials with additive manufacturing enables the development of multifunctional, energy-efficient, and circular water treatment systems. The findings support the advancement of purification technologies aligned with the United Nations Sustainable Development Goals, particularly SDG 6, SDG 12, and SDG 13.
Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process
During the preparation of this manuscript, the authors used Google Gemini and ChatGPT to improve language clarity and correct grammatical errors. In addition, Google Gemini and Canva were partially used to generate the graphical abstract,
Figure 1,
Figure 2,
Figure 3, and
Table 5. Following the use of these tools, the authors reviewed all content and took full responsibility for the accuracy, originality, and integrity of the published work.
Acknowledgements
The authors would like to acknowledge the financial support provided by Universiti Sains Malaysia through the Academic Research Grant (Grant Account: R502-KR-ARU005-0000000598-K134).
Author Contributions
Y.S.O.: Writing original draft and manuscript preparation; W.S.C.: Conceptualization, review and editing.
Ethics Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data sharing is not applicable to this article.
Funding
This work was funded by Universiti Sains Malaysia Academic Research Grant (Geran Penyelidikan Akademik; Grant Number: R502-KR-ARU005-0000000598-K134).
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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