Mass transfer fundamentals in pervaporation, perstraction and sorption: A unified approach
Bibliographic record
Abstract
The high affinity between an organic solute and a polymeric sorbent or membrane is the basis for separating organic contaminants from water via sorption, pervaporation and perstraction. These processes all share the same features of dissolution and diffusion, and thus their mass transfer characteristics can be correlated. However, they are often dealt with independently, and the research findings from one process are rarely applied to the other. The permeability coefficient in perstraction (for liquid permeation) based on concentration gradient as the driving force for mass transfer and the permeability coefficient for pervaporative transport expressed customarily in analog to vapor permeation can hardly be compared directly, and sometimes seemingly contradictive trends in temperature dependencies of the permeability coefficients for the two process modes may result. Looking into the mass transfer fundamentals pertaining to sorption, pervaporation and perstraction, this work attempted to provide a unified approach to the mass transfer in all these processes. While the permeability coefficient in pervaporation uses equivalent vapor pressure gradient across the membrane as the driving force, this work provided a generalized treatment by taking into account the sorption constants embedded in the permeability coefficients of the different processes so that the performance parameter for one process could be applied to another, which would be especially useful for screening and developing appropriate membrane/sorbent materials. In addition, the relationships among the selectivities of these separation processes were elucidated, which allowed for an intuitive illustration of how the process selectivity changed due to the process mode. Aniline removal from water via pervaporation, perstraction and sorption using a poly(ether-b-amide) membrane/sorbent was used as an example to illustrate and validate the approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".