Enhanced water transport and salt rejection through hydrophobic zeolite pores
Bibliographic record
Abstract
The potential of improvements to reverse osmosis (RO) desalination by incorporating porous nanostructured materials such as zeolites into the selective layer in the membrane has spurred substantial research efforts over the past decade. However, because of the lack of methods to probe transport across these materials, it is still unclear which pore size or internal surface chemistry is optimal for maximizing permeability and salt rejection. We developed a platform to measure the transport of water and salt across a single layer of zeolite crystals, elucidating the effects of internal wettability on water and salt transport through the ≈5.5 Å pores of MFI zeolites. MFI zeolites with a more hydrophobic (i.e., less attractive) internal surface chemistry facilitated an approximately order of magnitude increase in water permeability compared to more hydrophilic MFI zeolites, while simultaneously fully rejecting both potassium and chlorine ions. However, our results also demonstrated approximately two orders of magnitude lower permeability compared to molecular simulations. This decreased performance suggests that additional transport resistances (such as surface barriers, pore collapse or blockages due to contamination) may be limiting the performance of experimental nanostructured membranes. Nevertheless, the inclusion of hydrophobic sub-nanometer pores into the active layer of RO membranes should improve both the water permeability and salt rejection of future RO membranes (Fasano et al 2016 Nat. Commun. 7 12762).
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 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 | 0.001 |
| 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 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".