Facile morphological tuning of thin film composite membranes for enhanced desalination performance
Bibliographic record
Abstract
Abstract Polyamide (PA) membranes with a thin selective layer have been widely investigated for desalination and water treatment. Several modifications have been proposed over the years to tailor the morphology of such thin film composite (TFC) membranes by altering the support and/or selective layers to achieve superior performance. In this study, a facile approach towards fabricating a highly wrinkled selective layer has been demonstrated through bio-inspired modification of the support layer with Y-type zeolites. Results showed that incorporating zeolites in a smaller dimension (200 nm) produced by a unique ball milling technique is favorable for a defect-free selective layer in comparison to larger commercial zeolites. PA membranes formed by the interfacial polymerization (IP) of Piperazine (PIP) and 1,3,5-Benzenetricarbonyl trichloride (TMC) revealed highly wrinkled morphology due to the presence of zeolites in the TFC interlayer. At optimum fabrication conditions, the membrane exhibited a fast transport of 22.5 ± 2.2 Lm-2h-1bar-1 with a salt rejection of 48.6, 91.3, 99.1, and 99.5% for NaCl, MgCl2, MgSO4, and Na2SO4, respectively. Besides the unique preparation of zeolites in smaller dimensions, the novelty of this study lies in the facile membrane pretreatment before IP to achieve wrinkled PA membranes for enhanced nanofiltration performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".