Trypan blue removal from water with zein sorbents and laccase
Bibliographic record
Abstract
Abstract Zein-based materials were used to remove Trypan blue from water under flow conditions and in batch tests. In flow tests, zein dissolved at pH = 13 was injected in sand columns and subsequently coagulated with CaCl2, to create an adsorbent filter which removed over 99% of Trypan blue. Batch tests were conducted using zein powder, zein dissolved at pH = 13 and coagulated with CaCl2, Fe2Cl3 or citric acid, and zein dissolved in ethanol and then coagulated with water. The highest Trypan blue removal was achieved with zein powder (4000 mg Trypan blue/kg sorbent, as determined through spectrophotometry), followed by zein coagulated with Fe2Cl3 (500 mg Trypan blue/kg sorbent) and with other salts (140 mg Trypan blue/kg sorbent). Differences in the sorption efficiency are attributed to differences in the surface area. The sorption isotherm of Trypan blue onto zein-based sorbents was a Type II isotherm, suggesting physisorption. Desorption of Trypan blue was limited when zein-based coagulated sorbents were immersed in pure water. Trypan blue could be degraded by free laccase in water, as determined through spectrophotometry and electrospray ionization mass spectroscopy (ESI-MS). Trypan blue could also be degraded by laccase when zein-based laccase-containing sorbents were prepared at pH = 10, using Fe2Cl3 as coagulant. Graphic abstract
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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".