Graphene Oxide Nanoribbons in Chitosan for Simultaneous Electrochemical Detection of Guanine, Adenine, Thymine and Cytosine
Bibliographic record
Abstract
Herein, graphene oxide nanoribbons (GONRs) were obtained from the oxidative unzipping of multi-walled carbon nanotubes. Covalent coupling reaction of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) and N-hydroxy succinimide (NHS) with amine functional groups (-NH2) of the chitosan natural polymer (CH) was used for entrapping GONRs on the activated glassy carbon electrode (GCE/GONRs-CH). The nanocomposite was characterized by high-resolution transmission electron microscopy (HRTEM), and field-emission scanning electron microscopy (FESEM). In addition, the modification steps were monitored using FTIR. The nanocomposite-modified electrode was used for the simultaneous electrochemical determination of four DNA bases; guanine (G), adenine (A), thymine (T) and cytosine (C). The nanocomposite-modified GCE displayed a strong, stable and continuous four oxidation peaks during electrochemistry detection at potentials 0.63, 0.89, 1.13 and 1.27 V for G, A, T and C, respectively. The calibration curves were linear up to 256, 172, 855 and 342 μM with detection limits of 0.002, 0.023, 1.330 and 0.641 μM for G, A, T and C, respectively. The analytical performance of the GCE/GONRs-CH has been used for the determination of G, A, T and C in real samples and obtained a recovery percentage from 91.1%–104.7%. Our preliminary results demonstrated that GCE/GONRs-CH provided a promising platform to detect all four DNA bases for future studies on DNA damage and mutations.
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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) | 0.001 | 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".