Solvent Effect on the Grafting of an Organophilic Silane Onto Smectite‐type Clay: Application as Electrode Modifiers for Pesticide Detection
Bibliographic record
Abstract
Abstract The effect of a non‐swelling (toluene) and a swelling (ethylene glycol (EG)) solvent on the grafting of an organophilic silane (octyltriethoxisilane) onto a smectitic clay was investigated. XRD patterns of the resulting materials reveals that the grafting occurred exclusively on the edges of the clay particles without intercalation, as evidenced by the non‐variability of the d‐value before and after the grafting. FTIR and TGA characterizations show that higher amount of silane was grafted when toluene was used as solvent. With EG, the clay particles were well‐dispersed and the grafting well controlled. These functionalized materials were used as carbon paste electrode modifiers to evaluate their abilities for electrochemical detection of organophilic pollutants at trace level. Carbendazim (cbz), a widely used fungicide was used as model compound. The clay functionalized in EG was the most efficient modifier, due to the combined effect of the characteristics of the pristine clay and the grafted silane. The electroanalysis experimental parameters were carefully optimized (pH 6.8, 15 min of accumulation time and 10 % of the modifier in the carbon paste). By varying cbz concentration, a 0.03 μM detection limit was obtained. The sensor provide very reproducible response but strongly affected by some metal ions interferences. By varying cbz concentration in river water, used as environmental sample model, higher detection limit was obtained (0.2 μM), due to interfering species.
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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".