Synthesis of FeO@SiO2-DNA core-shell engineered nanostructures for rapid adsorption of heavy metals in aqueous solutions
Bibliographic record
Abstract
<p>Creating novel and innovative nanostructures is a challenge, aiming to discover nanomaterials with promising properties for environmental remediation. In this study, the physicochemical and adsorption properties of a heterogeneous nanostructure are evaluated for the rapid removal of heavy metal ions from aqueous solutions. Core–shell nanostructures are prepared using iron oxide cores and silica dioxide shells. The core is synthesized <em>via</em> the co-precipitation method and modified <em>in situ</em> with citric acid to grow a carboxyl layer. The shell was hydrolyzed/condensed and then functionalized with amine groups for ds-DNA condensation <em>via</em> electrostatic interaction. The characterization techniques revealed functional FeO@SiO2–DNA nanostructures with good crystallinity and superparamagnetic response (31.5 emu g−1). The predominant superparamagnetic nature is attributed to the citric acid coating. This improves the dispersion and stability of the magnetic cores through the reduction of the dipolar–dipolar interaction and the enhancement of the spin coordination. The rapid adsorption mechanism of FeO@SiO2–DNA was evaluated through the removal of Pb(II), As(III), and Hg(II). A rapid adsorption rate is observed in the first 15 min, attributed to a heterogeneous chemisorption mechanism based on electrostatic interactions. FeO@SiO2–DNA shows higher adsorption efficiency of 69% for Pb(II) removal compared to As(III) (51%) and Hg(II) (41%). The selectivity towards Pb(II) is attributed to the similar acid nature to ds-DNA, where the ionic strength interaction provides good affinity and stability. The facile synthesis and rapid adsorption suggest a promising nanostructure for the remediation of water sources contaminated with heavy metal ions and can be extended to other complex molecules.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".