A low pressure SWCNT‐ENM sandwich membrane system for the removal of PPCPs from water
Bibliographic record
Abstract
Abstract While carbon nanotubes are known as efficient adsorbents for removal of a number of contaminants from water, the possibility of their leaching into drinking water has prevented their application in water treatment. In this study, single walled carbon nanotubes (SWCNT) were sandwiched between two electrospun nanofibre membranes (ENM). The relatively small pore size of the ENM prevented the mechanically entangled nanotubes from passing through and contaminating the water. The performance of the SWCNT‐ENM was evaluated in a lab‐scale setup for the removal of PPCPs. For this purpose, a feed solution spiked with known concentrations of six PPCPs was passed through the membrane system. The target substances included acetaminophen (ACT), bezafibrate (BZF), iopromide (IOP), diclofenac (DCF), carbamazepine (CBZ), and sulphamethoxazole (SMX). The same test was also conducted using a single contaminant (ACT). Results demonstrated a decrease in the overall percent removal of PPCPs as feed flow rate and PPCP concentration increased. For multi‐component feeds containing equal amounts of the aforementioned PPCPs, the overall percent removal decreased from 90.8% to 71.0% when increasing the feed concentration from 30 to 600 μg/L. Experiments using sandwiched powdered activated carbon (PAC) showed that the dynamic adsorption capacity of PPCPs by SWCNT‐ENM was higher than that of PAC‐ENM, and remained unaffected by the feed composition. In addition, the high porosity of this novel membrane allowed for flow of water with low resistance such that the trans‐membrane pressure was found to be as low as 4 kPa at a pure water flux of 330 L/m 2 h.
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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 | 0.001 | 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".