Comparing the desalination performance of SMM blended polyethersulfone to SMM blended polyetherimide membranes by direct contact membrane distillation
Bibliographic record
Abstract
This study aims to compare the effect of host hydrophilic polymer on novel hydrophobic/ hydrophilic composite membrane characteristics and desalination performance by direct contact membrane distillation (DCMD). Two different polymers are used for the host polymer: polyethersulfone (PES) and polyetherimide (PEI). The membranes were prepared by the phase inversion method by blending surface modifying macromolecules (SMM) into the host hydrophilic polymer (PES and PEI). The membranes were characterized using a wide variety of characterization techniques including the gas permeation test, measurement of the liquid entry pressure of water (LEPw), scanning electronic microscopy (SEM), atomic force microscopy (AFM) and contact angle measurement. Furthermore, the membranes were tested by DCMD for desalination of 0.5 M NaCl solution and the results were compared to commercial polytetrafluoroethylene (PTFE) membranes (FGLP 1425, Millipore). The effects of the type of host polymer on membrane morphology and characteristics were identified, which enabled us to link membrane morphology to membrane performance. The PES membrane yielded superior flux to that of the commercial membrane and the PEI membrane when their performance was compared. This result could be attributed to the fact that the nSMM/PES had a higher pore size/porosity ratio and lower LEPw than the nSMM/PEI membrane. It is worth mentioning that all prepared membranes were tested successfully for the desalination application. In other words, NaCl concentrations in the permeate were below 200 ppm.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".