Evaluation of some new hyperbranched polyesters as binding agents for heavy metals
Bibliographic record
Abstract
Abstract Different generations of hydroxyl and carboxyl terminated hyperbranched polyesters were synthesised and used as heavy metals chelating compounds. The adsorptive capacity of the 3rd generation of the polyesters (G3‐OH, G3‐COOH) as well as that of nanoclay (Nanofil 116) for cadmium removal was determined through adsorption isotherm studies. The highest metal ion removal capacity was observed for G3‐COOH sample. The extent of binding (EOB) values of various generations of hyperbranched polyesters having the same core structure, but different terminal groups, indicated that, irrespective of the type of terminal group, the higher generations are more effective than the lower ones. EOB data can be adequately described by a bidentate coordination model for carboxyl terminated polyesters [each Cd(II) ion coordinates with two carboxyl groups] and tetradentate coordination model for hydroxyl terminated polyesters [1Cd(II)/4OH]. The EOB and selectivity properties of all prepared polyesters towards the heavy metal ions Cd(II), Cu(II), and Pb(II), were investigated under competitive condition. The results showed that the carboxyl terminated polymers exhibit higher binding capacities than those of hydroxyl terminated ones and the selectivity follows the order: Cu(II) > Cd(II) > Pb(II) for all polymer samples, irrespective of the type of terminal group. © 2011 Canadian Society for Chemical Engineering
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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 | 0.000 |
| 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".