Electrochemical Detection of Paraquat Using Fe<sub>3</sub>O<sub>4</sub> Nanoparticles Coated with Silica Shells and Modeling of Its Adsorption by Molecular Dynamics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Among pesticides, paraquat (PQ) is well recognized as extremely poisonous and harmful to human health when ingested since it can damage the nervous system and induce organ failure. Increasing PQ concentrations in contaminated water and agricultural goods are currently causing concern in several countries. This article addresses an adsorbent of silicon dioxide magnetic nanoparticles (SiMNPs) that was made of magnetic bead nanoparticles (Fe 3 O 4 ) decorated with silicon dioxide (SiO 2 ), which was used to investigate PQ detection via electrochemical methods and molecular dynamics simulation. The adsorption kinetics were analyzed to optimize the adsorbent conditions via Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherms. The best fit through the isotherms suggested that multilayer adsorption was central to PQ detection. The obtained Freundlich isotherm had a surface heterogeneity slope of approximately 0.92 and a K F of 4.10 (L/mg) with a wide-range detection of 0.4–876 μM and a limit of detection (LOD) of 0.22 μM. With a mean free energy of 13.13 kJ/mol obtained by the Dubinin–Radushkevich isotherm, ion exchange played a role in heterogeneous adsorption. The QM/MM simulation showed that the magnetic properties of the Fe 3 O 4 nanoparticles stabilized the protonation and deprotonation transition states of PQ. This led to conformable adsorption with two lowest adsorption states and adsorption energies of −12.2 and–10.9 kcal/mol. In an investigation of spiking recovery using a sample from a natural water source, the recovery was 83.79–103.09%. Interference tests of salts, herbicides, and phenolic pollutants were completed and revealed a high adsorption efficiency. Because of its unique properties in achieving a wide-range detection, this adsorbent with crystalline nanostructures holds significant promise for screening contaminated pesticide residues in a variety of fields. Wide-range detection with excellent recovery was proposed and demonstrated, leading to a promising path toward point-of-need (PON) portable sensor technologies used in resource-limited areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".