Pervaporative desalination of high salinity water using chitosan‐based thin film composite membranes
Bibliographic record
Abstract
Abstract Pervaporation is regarded as one of the most promising technologies for desalination due to its extremely low energy consumption and ability to process high salinity solutions. As an easily accessible natural material with various advantages, chitosan is a promising material for pervaporative desalination. However, the research in this regard is scarce, and all existing works only focus on the desalination of NaCl solutions. Since chitosan has the potential to chelate with various ions, its desalination performance for different salt solutions might vary. In this study, we applied chitosan‐based membranes to pervaporative desalination for various salt solutions. The effects that feed salts, feed concentration, and temperatures had on their performance were comprehensively investigated. Moreover, two types of chitosan (Chitosan‐K and Chitosan‐D) were utilized to investigate the impact of chitosan types and confirm the universality of the conclusion. It was found that the feed salts significantly impact the desalination performance of chitosan membrane depending on their chelation level with chitosan polymers, and the cations (rather than anions) of the salts have a more significant effect. The salt rejection was almost complete at all tested conditions, demonstrating chitosan to be a promising material for pervaporative desalination.
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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) | 0.001 | 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) | 0.001 | 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".