Electrochemical Determination of Naloxone Using Molecularly Imprinted Poly(para-phenylenediamine) Sensor
Bibliographic record
Abstract
A molecularly imprinted polymer (MIP)-based electrochemical sensor featuring an electrochemically grafted para-phenylenediamine functional monomer on a reduced graphene oxide-gold nanoparticles composite modified screen printed electrode is reported. The morphology and properties of the sensing material were characterized with microscopy, spectroscopy and electrochemical techniques. A number of factors affecting the performance of the MIP sensor were examined and optimized. Under an optimized condition, the imprinted electrochemical sensor yielded homogenous naloxone binding sites with a dissociation constant of 8.6 μ M, and responded linearly up to 8 μ M naloxone, with a limit of detection of 0.16 μ M. The sensor showed good run-to-run repeatability and batch-to-batch performance reproducibility with relative standard deviation of 5.7%–9.6% (n = 4) and <9% (n = 3), respectively. The imprinted sensor retained 95% and 85% of its performance when stored at ambient conditions for one and two weeks, respectively, demonstrating the sensor’s good stability. Selectivity experiments showed that both the MIP sensor and non-imprinted polymer electrode had minimal response (<25%) to equal concentrations of structurally similar compounds such as morphine, naltrexone and noroxymorphone, indicating good selectivity of the MIP sensor towards naloxone. The MIP sensor was successfully used to quantify naloxone in artificial urine samples, yielding recoveries greater than 92%.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".