Fused deposition modeling of functional nanohybrids: a transformative approach to sustainable water purification
Bibliographic record
Abstract
Fused Deposition Modeling (FDM) has revolutionized the production of customized polymer-based components across industries. The integration of nanohybrid (NH) materials into polymer matrices has led to notable improvements in mechanical strength (up to 60 MPa), thermal stability (20–40 °C increase in degradation temperature), and adsorption efficiency (up to 210 mg/g for Pb²⁺ removal). This review analyzes the synergy between FDM and nanotechnology for creating multifunctional, sustainable polymers designed for wastewater treatment applications. NH-enhanced composites such as TiO₂–PLA and GO–PBS have demonstrated over 95% dye degradation and >99% antibacterial activity, offering potential for scalable 3D-printed filters, catalytic reactors, and membranes. Furthermore, Multi-Criteria Decision-Making (MCDM) frameworks, including Analytic Hierarchy Process (AHP) and TOPSIS, are discussed as tools to select optimal material combinations balancing performance, cost, and environmental safety. The review outlines a roadmap for translating laboratory-scale NH–FDM innovations into real-world environmental solutions, emphasizing sustainability, safety, and long-term functionality in advanced water treatment systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".