Ecofriendly Synthesis of Tenorite (CuO) Nanoparticles Composite with <i>β</i>-cyclodextrin as an Electrochemical Sensor for the Determination of the Anticancer Drug Phloretin
Bibliographic record
Abstract
The present study reports the eco-friendly synthesis of single-phase tenorite (CuO) nanoparticles using an aqueous extract of Plectranthus scutellarioides for the first time. These CuO nanoparticles were combined with β-cyclodextrin (β-CD) to form CuO@β-CD nanocomposite. The prepared CuO@β-CD nanocomposite was characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive X-ray (EDX), fourier-transform infrared (FT-IR), zeta potential and the particle size analyser techniques. The nanocomposite was further utilized to fabricate an electrochemical sensor for the electrochemical investigation of an anticancer drug, phloretin (PHL). PHL exhibited two irreversible oxidation peaks at 0.807 V and 1.126 V on CuO@β-CD/GCE in phosphate buffer solution of pH 3. A 9-fold increment in the oxidation peak current of PHL was seen at CuO@β-CD/GCE when compared to that at bare/GCE. The oxidation peak current was observed to vary linearly with the concentration of PHL in the range of 0.05–102.04 μM for square wave voltammetric (SWV) method. The values of limit of detection (LOD) and limit of quantification (LOQ) were calculated and found to be 0.012 and 0.041 μM, respectively. The low relative standard deviation (RSD) values for inter- and intra-day assays revealed the good reproducibility and stability of the proposed method.
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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) | 0.000 | 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".