The simultaneous voltammetric detection of guanine and inosine using a hybrid nanocomposite of multi‐walled carbon nanotubes and iron oxide nanoparticles
Bibliographic record
Abstract
Abstract In this proof‐of‐concept study, a graphite paste electrode was modified with a novel hybrid nanocomposite of multi‐walled carbon nanotubes and iron‐oxide nanoparticles for the simultaneous voltammetric detection of guanine and inosine. The nanocomposite was characterized using transmission electron microscopy. The incorporation of iron oxide nanoparticles with multiwalled carbon nanotubes to the modified graphite paste electrode provided an enhanced electrocatalytic activity as studied by cyclic voltammetry, electrochemical impedance spectroscopy and differential pulse voltammetry. The linearity ranges were 0.1–9.0 μM and 0.5–55 μM with a detection limit of 0.05 μM and 0.12 μM for guanine and inosine, respectively. Moreover, chronoamperometric studies were carried out for the determination of diffusion coefficients, and heterogenous rate constants of guanine and inosine. The sensor was also used for the simultaneous detection of guanine and inosine in commercially available bovine serum samples with recovery values ranging from 96 % to 104 %. We envisage the nanocomposite‐modified sensor provides a promising platform for the detection of guanine and inosine in pharmaceutical formulations and biological samples as biomarkers of oxidative damage related to cancer research.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".