Microwave-Assisted Synthesis of a Core–Shell MWCNT/GONR Heterostructure for the Electrochemical Detection of Ascorbic Acid, Dopamine, and Uric Acid
Bibliographic record
Abstract
In this study, graphene oxide nanoribbons (GONRs) were synthesized from the facile unzipping of multiwalled carbon nanotubes (MWCNTs) with the help of microwave energy. A core-shell MWCNT/GONR-modified glassy carbon (MWCNT/GONR/GC) electrode was used to electrochemically detect ascorbic acid (AA), dopamine (DA), and uric acid (UA). In cyclic voltammograms, the MWCNT/GONR/GC electrode was found to outperform the MWCNT- and graphene-modified GC electrodes in terms of peak current. For the simultaneous sensing of three analytes, well-separated voltammetric peaks were obtained using a MWCNT/GONR/GC electrode in differential pulse voltammetry measurements. The corresponding peak separations were 229.9 mV (AA to DA), 126.7 mV (DA to UA), and 356.6 mV (AA to UA). This excellent electrochemical performance can be attributed to the unique electronic structure of MWCNTs/GONRs: a high density of unoccupied electronic states above the Fermi level and enriched oxygen-based functionality at the edge of the graphene-like structures, as revealed by X-ray absorption near-edge structure spectroscopy, obtained using scanning transmission X-ray microscopy.
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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".