Nano-enabled 3D-Printed Structures for Water Treatment
Bibliographic record
Abstract
Water scarcity caused by climate change has become a growing global concern, affecting the quality of life of billions of people. An effective approach to overcoming the challenges posed by water scarcity is to integrate nanotechnology into water infrastructure. Nanomaterials have multifunctional properties that can improve the efficiency of water treatment plants and remove both legacy and emerging contaminants with less energy consumption, increased capacity, and enhanced flexibility. However, incorporating nanomaterials into the existing water treatment infrastructure may have drawbacks such as leaching of nanomaterials into treated water, leading to a decreased overall efficiency. Various strategies have been proposed for the fabrication of nanomaterials in higher dimensions, with three-dimensional (3D) printing techniques being particularly notable due to their durability, material flexibility, and ease of fabrication. In this review, we focus on 3D-printed nanomaterials for water treatment applications. Possible enhancement pathways of conventional water treatment methods using 3D printing as well as different strategies for nano-enabling 3D-printed structures have been critically discussed. We conclude by summarizing the challenges associated with utilizing 3D printing in environmental applications, especially water treatment, and providing future directions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".