Clay Nanoparticles Composite Membranes Prepared with Three Different Polymers: Performance Evaluation
Bibliographic record
Abstract
This paper presents the results obtained from the evaluation of clay nanoparticles as an additive for improving the characteristics and performance of composite membranes cast with polysulfone (PS), polyethersulfone (PES), and polyvinylidene fluoride (PVDF). Different concentrations of clay nanoparticles, ranging from 1 to 10% based on the polymer mass, were used to prepare all dope solutions. The addition of clay nanoparticles changed the internal pore morphology of membranes, which resulted in significant changes on their performance, regarding its water permeability, and fouling potential. The optimum nanoclay concentration for permeability enhancement was different for each polymer, 1.5%, 2.0%, and 6.0% for PS, PES, and PVDF, respectively. This difference can be attributed to the differences of polymer’s hydrophobicity, based on the contact angle of a sessile water drop, which is higher for PVDF (PVDF is more hydrophobic than PS and PES). The flow improvement changed based on the main polymer. Significant changes in internal pore structure were observed for all membranes. The proportion of macrovoids was decreased and pores had a better connectivity across the cross section for PES and PS membranes. For PVDF membranes, the addition of nanoclay had a different effect on their microstructure. In this case, internal pores were 20% wider, factor that increased the average membrane porosity. The simultaneous evaluation of the clay nanoparticles used as an additive have clearly demonstrated its potential application for composite membrane production. It is also worth to note that the best way for identifying and evaluating the potential for an additive for membrane casting is considering its effects for different polymers, under the same casting conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".