Removal of aluminum (III) from environmental samples by siliceous support grafted with poly[1‐(N,N‐<i>bis</i>‐carboxymethyl)amino‐3‐allylglycerol‐<i>co</i>‐dimethylacrylamide] brushes
Bibliographic record
Abstract
Silica gel was grafted with polymer containing a functional monomer for metal chelating, poly[1‐(N,N‐bis‐carboxymethyl)amino‐3‐allylglycerol‐co‐dimethylacrylamide] (poly(AGE/IDA‐co‐DMAA)). A system for preconcentration and measurement of aluminum as complexed with iminodiacetic acid was developed. The effects of the analytical parameters, such as sample pH, contact time on sorption capacity of the chelating sorbent and matrix ions, were investigated. The optimum pH value for sorption of the aluminum ions was 6. A recovery of 91.6 % was obtained for the metal ion with 0.5 mol L−1 sulphuric acid as eluting agent. The chelating resin can be reused for five cycles without any significant changes in sorption capacity. The sorption capacity of functionalised sorbent was 25.3 mg g−1. The developed method was utilized for preconcentration and determination of aluminum in environmental water samples by flame atomic absorption spectrometry with satisfactory results. The equilibrium adsorption data of Al(III) on modified sorbent were analysed by Langmuir, Freundlich and Temkin. Based on equilibrium adsorption data the Langmuir, Freundlich, Temkin and Redlich–Peterson constants were determined to be 0.076, 3.21, and 3.46, respectively, at pH 6 and 20 °C.
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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) | 0.000 | 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".