Graphene Oxide-Based Nanomaterials for the Electrochemical Sensing of Isoniazid
Bibliographic record
Abstract
Isoniazid is one of the most important antibiotics used for the treatment of tuberculosis. However, the associated side effects can be quite detrimental to human health, thus necessitating careful monitoring of the drug concentration in the human system. Here, we endeavored to understand the significant roles of oxygen and the structure of graphene oxide (GO) in developing high-performance graphene-based electrochemical sensors for the detection of isoniazid. Several graphene-based materials, including GO, thermally reduced graphene oxide (TrGO), electrochemically reduced graphene oxide (erGO), and interconnected reduced graphene oxide (IC-rGO) were synthesized to test their performance in the sensing of isoniazid. We found that a lower oxygen content on graphene sheets significantly promoted the electrochemical oxidation of isoniazid. Through the use of density functional theory (DFT) calculations, it was concluded that the oxygen functional groups on the graphene oxide sensors and the carboxamide functional groups of isoniazid exhibited repulsive forces that prevented isoniazid from approaching the graphene sheet, which consequently lowered the adsorption energy. To examine the structure of GO-based materials for the sensing of isoniazid, we successfully synthesized three-dimensional (3D) IC-rGO, with the intention of maximizing the electrochemically active surface area (ECSA) to increase the current response of the sensor for the electrochemical detection of isoniazid. An improved performance was observed for IC-rGO in contrast to the other GO-based nanomaterials; the optimized IC-rGO sensor had a detection limit of 0.03 μM with a high sensitivity of 4.02 μA μM –1 cm –2 . Furthermore, the sensor exhibited a reproducible and stable response and negligible interference from the common biological species found in the blood and urine. The developed sensor was observed to successfully detect isoniazid in urine and blood serum, which confirmed its promising applicability in medical and biomedical applications.
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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 | 0.001 | 0.001 |
| 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".