Preparation of Organophilic Nanohybrid Kaolinite and Application to the Electrochemical Detection of Organic Pesticide
Bibliographic record
Abstract
Abstract Novel organophilic nanohybrid materials (K‐TDD) were obtained by the grafting of 1,2‐tetradecanediol (TDD) onto the surface of kaolinite (K). XRD, IR, TGA‐DTG, and SEM characterization showed that TDD grafting results in a partial exfoliation of kaolinite layers. This material was used to modify a glassy carbon electrode (GCE/K‐TDD) and applied for the trace analysis of methyl parathion (MP). The signal of MP recorded on GCE/K‐TDD was more intense compared to the unmodified GCE or to one modified with a film of natural kaolinite. Several parameters that can affect the stripping response were systematically investigated to optimize the sensitivity of the organokaolinite‐modified electrode. A linear calibration curve for MP was obtained in the concentration range from 2×10−6 to 14×10−6 mol .L−1 in acetate buffer (pH 6), giving a detection limit of 9×10−8 mol .L−1. The sensitivity of the method was found to be 2.42 μA/μM for the range of concentrations that gives a linear calibration curve. The electrode was shown to be very stable, with the electrochemical response of MP decreasing by only 1.5 % after a series of nine measurements. The interference of various inorganic ions and organic compounds likely to influence the stripping determination of the MP were also examined. The results showed that the GCE/K‐TDD electrode was effective in solutions containing interfering species and could be applied for the quantification of MP pesticide in natural water.
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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 | 0.000 |
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".