Bio-synthesized copper nanoparticle anchored ultrathin petal-shaped black phosphorous nanosheets and 3D graphene decorated nanocomposite for electrochemical sensing of methotrexate and paracetamol in diverse matrices
Bibliographic record
Abstract
Creating a sensor that can concurrently monitor multiple therapeutic drugs in complex media is a challenging yet essential endeavor. This study presents a novel biomolecule-free electrochemical platform for simultaneous and highly selective detection of methotrexate (MTR) and paracetamol (PRC) in various matrices, including pharmaceutical formulations, simulated blood samples, and water samples. The platform utilizes a multi-layered petal-shaped black phosphorous structure supported on 3D graphene, along with bio-synthesized copper nanoparticles (BP-3DGp@BCuN). Prior to the sensing study, the as-prepared BP-3DGp@BCuN nanocomposite was characterized using FESEM, EDX, FTIR, UV, XPS, Raman spectroscopy, BET, and XRD. Electrochemical studies of BP-3DGp@BCuN nanocomposite reveal a considerable enhancement of the current compared to pure BP or 3DGp. The petal-shaped black phosphorous comprising of 3DGp and BCuN provides a larger surface area, effective mass transport, and more active sites for the attachment of the target analytes that amplified the current signals and detection sensitivity. Furthermore, computational analysis proves that the BP-3DGp@BCuN nanocomposite has strong interaction with the target MTR and PRC compared to other modifiers. Under optimized conditions, the proposed sensing method shows a linear detection range of 0.05–70 µM and 0.5–210 µM with limit of detection (LOD) values of 0.045 nM and 0.36 nM, with high sensitivity of 37.40 and 14.94 μA μM−1 cm−2 for PRC and MTR respectively. The real-life application of the present sensor was examined in pharmaceutical formulations, simulated blood, and water samples.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 teacher head, 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".