Closed-circuit RO membrane coupled with vacuum UV enhances pharmaceutical degradation via molecular uncoupling in soluble fractions
Bibliographic record
Abstract
Trace pharmaceuticals in water pose environmental and health risks that conventional water treatment struggles to address. Reverse osmosis (RO) rejects a broad range of these compounds, but shifts them from the bulk stream to the concentrate, requiring further treatment. Ultraviolet (UV)-based advanced oxidation processes (AOPs), such as vacuum UV (VUV), are capable of degrading persistent compounds, yet their high energy demand can make application economically challenging, particularly in matrices with high radical scavenging capacity. This study presents an innovative treatment strategy based on the concept of physical decoupling of RO permeate and retentate molecules subjected to differential VUV doses. This innovative treatment approach leverages the membrane system as a separation process to enable “decoupling” of two soluble fractions (controlled by the membrane weight cut off (MWCO)), providing process intensification and more compact reactor design. A pilot-scale hybrid RO-VUV system was used to treat tap water, spiked with acetaminophen (ACE), caffeine (CAF), carbamazepine (CBZ), and sulfamethoxazole (SMX)as model contaminants. Benchmarking against RO alone and VUV alone configurations, the integrated system demonstrated superior degradation efficiency and faster reaction rates within the same treatment time. Specifically, the same performance with VUV alone required more than twice the treatment time and energy. The integrated approach required 3.7 kWh per order versus 3.1 for RO alone, adding ∼0.066 CAD m −3 , while VUV alone showed higher EEO due to slower kinetics. Permeate from the integrated system had the lowest concentration of pharmaceuticals, generating the highest quality product when compared to RO/VUV alone.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".