Synthesis and characterization of mixed matrix membranes incorporated with hydrous manganese oxide nanoparticles for highly concentrated oily solution treatment
Bibliographic record
Abstract
Abstract In this work, ultrafiltration (UF) mixed matrix membranes (MMMs) incorporated with hydrophilic hydrous manganese oxide (HMO) nanoparticles were synthesized and used for the treatment process of a highly concentrated oily solution. The MMMs were fabricated via a phase inversion process and were characterized with respect to physiochemical properties and filtration performances. The results showed that the MMMs demonstrated higher water flux and better oil rejection rates compared to the control membrane (without HMO incorporation) when tested at three different oily solutions with oil concentration in the range of 5000–15 000 ppm. The membrane incorporated with the highest amount of HMO (labelled as MMM‐2) in particular showed the best results even when subjected to 15 000 ppm oily solution filtration, recording water flux of 32.2 L/m 2 · h · bar and oil rejection of 82 %. The enhanced performance can be attributed to improved surface hydrophilicity coupled with better structural integrity and higher porosity that contributed to lower degree of oil molecules deposition and greater water transport rate. It was also reported that with the use of a feed spacer during the filtration process, the degree of flux deterioration of MMMs could be further reduced without compromising rejection. The findings of this work indicated the potential of MMMs for the treatment of highly concentrated oily effluent produced from industries, by producing permeate of high quality at reasonably good water permeability.
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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 | 0.000 | 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 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".