Separation of mercuric ions using 2-thienylbenzimidazole/cucurbit[7]uril/iron-oxide nanoparticles by pH control
Bibliographic record
Abstract
Abstract 2-Thienylbenzimidazole (TBI)/cucurbit[7]uril (CB7) host–guest complex was used as a motif to significantly improve the turnover of γ-Fe3O4 magnetic nanoparticles for potential application in the separation of toxic mercuric ions in polluted water samples. The mechanism of restoring the original solid materials is based on applying the pH-controlled preferential binding of the CB7 host to the TBI guest. The analytical application of this concept has not been realized in the literature. The pH-controlled stimuli-responsive abilities were confirmed in aqueous solution by the three-order of magnitudes higher stability constant of the protonated TBIH+/CB7 complex (e.g., K = 4.8 × 108 M−1) when compared to neutral TBI/CB7 complex (e.g., K = 2.4 × 105 M−1), also manifested in an increase in pKa values by ~ 3.3 units in the ground state. The supramolecular interaction and adsorption on iron oxide nanoparticles (NPs) were also spectroscopically confirmed in the solid state. The excited-state lifetime values of TBI/CB7NPs increased upon lowering the pH values (e.g., from 0.6 to 1.3 ns) with a concomitant blue shift of ~ 25 nm because of polarity effects. The time-resolved photoluminescent behaviors of the final solids in the presence of CB7 ensured pH-driven reusable systems for capturing toxic mercuric ions. The study offers a unique approach for the controllable separation of mercury ions using an external magnet and in response to pH through preferential binding of the host to guest molecules on the top of magnetic surfaces.
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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".