Electrochemical sensor based on incorporation of gold nanoparticles, ionic liquid crystal, and β-cyclodextrin into carbon paste composite for ultra-sensitive determination of norepinephrine in real samples
Bibliographic record
Abstract
A novel, reliable electrochemical sensor is fabricated for direct and sensitive determination of norepinephrine (NE) based on gold nanoparticles, ionic liquid crystal, and β-cyclodextrin modified carbon paste electrode, namely AuILCCDCPE. The ionic liquid crystal (ILC) played a key role in improving the current response of electro-oxidation of NE compared with other ionic liquids modified electrodes. The ILC increased the ionic conductivity of the paste and formed noncovalent interactions with both host (CD) and guest (NE) compounds. The solid state structure of the ILC helped in the formation of ordered films in the paste. Furthermore, CD and Au nanoparticles raised the stability and the electrocatalytic ability of the proposed sensor. Under optimized conditions, the fabricated electrochemical sensor showed a good electrochemical response towards NE in human urine in the linear dynamic ranges of 0.05–10 μmol/L and 20–300 μmol/L with a correlation coefficient of 0.999 and detection limit of 3.12 × 10−9 mol/L in the low concentration range. The practical analytical performance of the sensor was attained for determination of NE in real samples with satisfied recovery results. This sensor has great ability to be extended for electrochemical applications in assays of other drugs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".