Toward Scalable Nanofluidic Osmotic Power Generation from Hypersaline Water Sources with a Metal–Organic Framework Membrane
Bibliographic record
Abstract
Abstract Nanofluidic membranes have shown great promise in harvesting osmotic energy but its scalablity remains challenging since most studies only tested with a membrane area of ≈10 −2 mm 2 or smaller. We demonstrate that metal‐organic‐framework membranes with subnanometer pores can be used for scalable osmotic power generation from hypersaline water sources. Our membrane can be scaled up to a few mm 2 , and the power density can be stabilized at 1.7 W m −2 . We reveal that the key is to improve the out‐of‐membrane conductance while keeping the membrane's charge selectivity, contradicting the previous conception that the ionic conductivity of the membrane plays the dominating role. We highlight that subnanometer pores are essential to ensure the charge selectivity in hypersaline water sources. Our results suggest the importance to engineer the interplay between the in‐membrane and out‐of‐membrane ion transport properties for scalable osmotic power generation.
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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 | 0.000 |
| 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".