Dithiocarbamate functionalized <scp>A</scp>l(<scp>OH</scp>)<sub>3</sub>‐polyacrylamide adsorbent for rapid and efficient removal of <scp>C</scp>u(II) and <scp>P</scp>b(II)
Bibliographic record
Abstract
ABSTRACT Heavy metal ions such as Cu2+ and Pb2+ impose a significant risk to the environment and human health due to their high toxicity and non‐degradable characteristics. Herein, Al(OH)3‐polyacrylamide chemically modified with dithiocarbamates (Al‐PAM‐DTCs) was synthesized using formaldehyde, diethylenetriamine, carbon disulfide, and sodium hydroxide for rapid and efficient removal of Cu2+ and Pb2+. The synthesized adsorbent was characterized by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis, scanning electron microscopy–energy dispersive X‐ray spectroscopy analysis, and transmission electron microscope measurements. Al‐PAM‐DTCs showed rapid removal of Cu2+ (<30 min) and Pb2+ (<15 min) with high adsorption capacities of 416.959 mg/g and 892.505 mg/g for Cu2+ and Pb2+ respectively. Al‐PAM‐DTCs also had high capacities in removing suspended solids and metal ions simultaneously in turbid bauxite suspensions. FTIR, thermodynamic study, and elemental mapping were used to determine the adsorption mechanism. The rapid, convenient, and effective adsorption of Cu2+ and Pb2+ indicated that Al‐PAM‐DTCs has great potential for practical applications in purification of other heavy metal ions from aquatic systems. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45431.
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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".