A Novel Metal-Free Robust Recyclable Electrosorbent for Removal Pb(II) from Low Concentrated Solutions in Complex Aqueous Matrices
Bibliographic record
Abstract
We have developed a novel metal-free highly robust recyclable electrosorbent for removal Pb(II) from water. It is based on a cost-efficient and biocompatible conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) which is synthesized through a facile green chemistry route. This electrosorbent demonstrates superior adsorption-desorption properties and recyclability as compared to conventional lead adsorbents and the same sorbent but without electrostimulation. Specifically, its Pb(II) uptake (>800 mg/g without saturation in the presence of excessive NaCl, Ca and Fe) is three-fold higher, while adsorption and desorption kinetics at 400 ppb Pb(II) are faster than without electrostimulation. Importantly, the electrosorbent has 100% reversibility and excellent stability for at least 30 adsorption-desorption cycles and after one-month storage in water. Using XPS and Raman spectroscopy, we link the outstanding properties of the electrosorbent to its unique cation-exchange properties which are enhanced by applied potential. Our results demonstrate a novel strategy for facile manufacturing of cost-effective robust recyclable electrosorbents from conjugated conducting polymers, as well as advantages of electrochemical stimulation for achieving highly repeatable electrosorption and reclamation of valuable metals from their dilute solutions in complex aqueous matrices.
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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 | 0.001 |
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".