Sustainable 3D‐Printed Platforms with Durable Photocatalytic Coatings for Efficient Water Treatment
Bibliographic record
Abstract
Abstract This study presents a robust and sustainable 3D‐printed scaffold with engineered surface properties for durable and wear‐resistant coating of photocatalytic nanocomposites. Copper‐doped titanium dioxide/reduced graphene oxide nanocomposites are synthesized to enable visible‐light activation, achieving 89% methylene blue removal within 60 min under visible light illumination. The coating's mechanical and chemical stability is systematically evaluated under UV exposure, sonication‐induced vibration, and cyclic regeneration using chemical washing. Scaffold design parameters, including pore architecture, surface topology, and chemistry, are optimized to enhance nanocomposite loading and retention. Among the tested infill designs, the gyroid structure provides the highest surface area (3259.2 mm2) and supports the largest nanocomposite mass. Incorporation of polydopamine as a bioadhesive significantly improves coating adhesion (378% increase in nanocomposite loading) and stability (200% reduction in leaching). Surface engineering also facilitates the formation of uniform, few‐layer coatings, resulting in a removal efficiency of 93% within 120 min, which is comparable to that of colloidal nanocomposites reported in the literature. The nano‐enabled scaffold maintains excellent performance across 30 regeneration and reuse cycles, with a final‐cycle removal efficiency of 91.4%, outperforming existing systems by more than fourfold in terms of reusability.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".