Performance of PES/LSMM-OGCN Photocatalytic Membrane for Phenol Removal: Effect of OGCN Loading
Bibliographic record
Abstract
In designing a photocatalytic oxidation system, the immobilized photocatalyst technique becomes highly profitable due to its promising capability in treating organic pollutants such as phenols in wastewater. In this study, hydrophiLic surface modifying macromolecules (LSMM) modified polyethersulfone (PES) hybrid photocatalytic membranes incorporated with oxygenated graphitic carbon nitride (OGCN) was successfully developed using phase inversion technique. The effectiveness of the hybrid photocatalytic membrane was determined under different loading of OGCN photocatalyst (0, 0.5, 1.0, 1.5, 2.0, and 2.5 wt%). The best amount of OGCN in the casting solution was 1.0 wt% as the agglomeration did not occur considering the stability of the membrane performance and morphology. The highest flux of 264 L/m²·h was achieved by PES/LSMM-OGCN1.5wt% membrane. However, the highest flux performance was not an advantage in this situation as the flux reduced the rejection value due to open pores. The membrane with the highest photocatalytic performance was obtained at 1.0 wt% of OGCN loading with 35.78% phenol degradation after 6 h. Regardless of the lower rejection value, the performance shown by the PES/LSMM-OGCN1.0wt% membrane was still competent because of the small difference of less than 1% to that of the PES/LSMM-OGCN0wt% membrane. Based on the findings, it can be concluded that the optimisation of the OGCN loading in the PES hybrid photocatalytic membrane indeed plays an important role towards enhancing the catalyst distribution, phenol degradation, and acceptable rejection above all considerations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".