Smart dynamic hybrid membranes with self-cleaning capability
Bibliographic record
Abstract
The growing freshwater scarcity has caused increased use of membrane desalination of seawater as a relatively sustainable technology that promises to provide long-term solution for the increasingly water-stressed world. However, the currently used membranes for desalination on an industrial scale are inevitably prone to fouling that results in decreased flux and necessity for periodic chemical cleaning, and incur unacceptably high energy cost while also leaving an environmental footprint with unforeseeable long-term consequences. This extant problem requires an immediate shift to smart separation approaches with self-cleaning capability for enhanced efficiency and prolonged operational lifetime. Here, we describe a conceptually innovative approach to the design of smart membranes where a dynamic functionality is added to the surface layer of otherwise static membranes by incorporating stimuli-responsive organic crystals. We demonstrate a gating effect in the resulting smart dynamic membranes, whereby mechanical instability caused by rapid mechanical response of the crystals to heating slightly above room temperature activates the membrane and effectively removes the foulants, thereby increasing the mass transfer and extending its operational lifetime. The approach proposed here sets a platform for the development of a variety of energy-efficient hybrid membranes for water desalination and other separation processes that are devoid of fouling issues and circumvents the necessity of chemical cleaning operations.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".