The use of graphene nanoplatelet‐embedded <scp>PA</scp> ‐6 nanofibres to remove turbidity from water
Bibliographic record
Abstract
Abstract The global challenge of providing clean water at an affordable cost has led to the need for the development of low‐cost and non‐toxic materials for the treatment and recycling of waste water. Nanofibres have emerged as a promising solution due to their superior properties. To this end, composite polyamide‐6 (PA‐6) nanofibres embedded with graphene nanoplatelets (GNPs) were prepared by electrospinning. The study investigated the effect of the ratio of GNPs, which ranged from 0.1 to 1.0 wt.%, on the mechanical properties of nanofibres and the removal of turbidity. The results showed that PA‐6 nanofibres with 0.5 wt.% GNP exhibited enhanced mechanical properties, and increasing the GNP ratio led to lower turbidity values. To the best of our knowledge, GNP‐embedded PA‐6 nanofibres have not been used for turbidity removal before, and these filter materials are promising due to their excellent fibre structure, mechanical strength, and high level of turbidity removal.
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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 | 0.000 | 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 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".