Novel Ti-Coordination Polydopamine Nanocomposite with a Combination of Adsorption, Reduction, and Ion Exchange for Rapid Cr(VI) Removal
Bibliographic record
Abstract
Adsorption is one of the most economical and effective technologies for the remediation of Cr(VI) pollution. Due to the high toxicity and mobility of Cr(VI), however, Cr(VI)-laden adsorbents are still very toxic and dangerous solid pollutants after usage and require special attention. To tackle this issue, herein, we present a facile yet simple strategy to prepare a novel advanced adsorbent based on Ti-coordination polydopamine nanocomposites (TCPNs) for rapid Cr(VI) adsorption, reduction, and ion exchange to achieve better Cr(VI) detoxification and fixation. The TCPNs were synthesized by combining Ti and dopamine coordination with sequent self-polymerization of the resulting Ti–dopamine complexes under hydrothermal conditions. The as-prepared TCPNs were characterized with a combination of scanning electron microscopy (SEM) and energy-dispersive spectrometry (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Special efforts have been devoted to investigating the effect of solution pH, contact time, TCPN dosage, and initial Cr(VI) concentration on the adsorption kinetics and isotherm of TCNPs to Cr(VI). The adsorption results show that the developed TCPN adsorbent possesses a high Cr(VI) adsorption capacity of 370 mg/g at a low Cr(VI) concentration of 50 mg/L and can rapidly reduce the Cr(VI) concentration from 10 mg/L to an extremely low level of smaller than 0.02 mg/L in 15 min. In addition, TCPNs have obviously high selectivity and preference to Cr(VI) when compared to other coexisting competing ions. Finally, the mechanism studies reveal that TCPNs achieve this satisfactory Cr(VI) detoxification and fixation through a combination of rapid adsorption, reduction, and ion exchange.
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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".