Reduction of Plastic Waste through the Development of a 3D-Printed Water Filter
Bibliographic record
Abstract
Accompanying the accumulation of plastic waste is the growing demand for reusable and biodegradable alternatives. In addition, many commercial water filters consist of a plastic outer shell and replacement of these filters contribute to the progress of plastic pollution. Not only does plastic in the environment negatively and physically impact wildlife, additives of the plastic, including phthalates, can leach into waterways and end up in tap or drinking water. As contaminants in water pose a risk to wildlife and human health, more efficient and environmentally friendly alternatives must be considered. To combat these waste related issues, a 3D-printed water filter is proposed here. The structure of this filter is comprised of polylactic acid (PLA), which is a biodegradable polyester and is comparable in strength and toughness to petroleum-based plastics. A composite mixture of graphene oxide and nanocellulose is added to the inside of the PLA cartridge, supplying its filtration properties. These materials allow the refinement of commercial water filters into a device that is biodegradable, cost-effective, and environmentally friendly. The purpose of this study is to find an alternative method to commercially purify water and reduce the growth of plastic waste in the environment. Faculty Mentor: Samuel Mugo Department: Biological Science
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".