Fibroin/chitosan blend membranes for perstractive removal of Hg(II) from aqueous solutions
Bibliographic record
Abstract
Mercury (Hg(II)) contamination of water poses a significant environmental threat due to its high toxicity and bioaccumulation. In this study, fibroin/chitosan blend membranes were developed for the efficient removal of Hg(II) from aqueous solutions via perstraction using thiourea as a stripping agent at the downstream side to enhance Hg(II) removal. A resistance-in-series model was used to quantify and analyze the contributions of the individual resistance component in the overall mass transfer process. The research findings emphasized that while the membrane imparted a significant resistance to perstractive Hg(II) removal, the external mass transfer resistances (i.e., liquid phase boundary layer resistances, interfacial mass transfer resistance for Hg(II) release from the membrane at the downstream side) were also significant. The latter aspect was especially important when a thin membrane was used. While the liquid boundary effects could be reduced by proper management of the liquid phase hydrodynamics, the use of thiourea as a stripping agent was shown effective to facilitate Hg(II) release from the membrane, thereby minimizing the mass transfer resistance to Hg(II) perstraction at the downstream side of the membrane. This work offers a sustainable eco-friendly approach to valorization of renewable biopolymers derived from biomass wastes for the treatment of Hg(II) containing wastewater.
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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 | 0.001 |
| 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".