Sensitive Amperometric Determination of Thiocyanates at Ionic Liquid Nanohybrid Kaolinite Modified Glassy Carbon Electrode
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Electrochemical sensors have been developed by modifying a glassy carbon electrode with organo‐kaolinite hybrid materials. These materials were obtained by the grafting of four ionic liquids (1‐(2‐hydroxyethyl)‐4‐benzylpyridinium chloride, 1‐(2‐hydroxyethyl)‐4‐(tert‐butyl)pyridinium chloride, 1‐(2‐hydroxyethyl)‐4‐ethylpyridinium chloride and 1‐(2‐hydroxyethyl)‐4‐methylpyridinium chloride) on the interlayer aluminol surfaces of kaolinite. With the presence of ionic liquids in the interlayer space of kaolinite, the hybrid materials acquired anion exchange properties and were successfully applied as electrode modifier for the electroanalysis of thiocyanate (SCN − ), an anion of medical and environmental concern. A pre‐concentration/detection strategy was used to overcome the interfering effect of the electrolytic solution. After the optimisation of some key experimental parameters (sodium nitrate as electrolyte, 5 min of accumulation time) calibration curves were plotted. Excellent linearity was obtained in the low concentration region (1×10 −6 M to 4×10 −5 M). The lowest detection limit (15 nM) was obtained with the benzylpyridinium functionalized kaolinite and the highest (60 nM) with the methylpyridinium functionalized kaolinite. Interfering anions (NO 3 − , Cl − , SO 4 2− and CH 3 COO − ) present in the pre‐concentration solution were found to interfere with SCN − but the sensors remained stable and produced reproducible signals. The most sensitive sensor was successfully applied for the amperometric determination of SCN − in human saliva samples.
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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 | 0.001 | 0.003 |
| 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 it