3D-printed, flow-through water filters for microplastic capture: The effect of surface porosity, column height, and pressure-sensitive adhesives on removal efficiency
Bibliographic record
Abstract
Growing concern over microplastic pollution in the environment has led to an increase in exploration over how to prevent them from entering the environment, as well as the food and water we consume. Fused deposition modeling (FDM) or three-dimensional (3D) extrusion printing offers an easy, scalable, and customizable approach to the generation of customized and innovative material designs, including filtration systems to combat microplastics. In this work, we report a tortuous, layer-based, 3D printed, flow-through filtration column for the easy capture of microplastics from wastewater. Through the addition of polyethylene glycol (PEG) as a sacrificial additive to polylactic acid (PLA), the printed filters can be etched using hot water to achieve a micro- and nano-porous surface with better microplastic capturing capabilities. Further improvement was made via the deposition of pressure-sensitive adhesives, polydopamine (PDA) and poly (2-ethylhexyl acrylate), to improve physical non-covalent interactions with the microplastics on the surface. Finally, through analysis of the filtration column height, a high-efficiency filter capable of 90% microplastic removal was achieved. This work highlights the possibility of using simple, inexpensive, customization, and efficient flow-through filtration systems for high-efficiency microplastic capture from drinking water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".