Fabrication of NiSe<sub>2</sub> decorated graphene oxide nanocomposite modified electrode as a high performance electrochemical sensor for carbamazepine
Bibliographic record
Abstract
Abstract Recently, semiconductor nanomaterials have gained interest amongst researchers because of their good electrocatalytic properties. Two dimensional (2D) transition metal dichalcogenide materials, NiSe2 have good physical, chemical, and transport properties for sensing applications. The present work demonstrates the fabrication of NiSe2‐graphene oxide (NiSe2−GO) modified glassy carbon electrode for the determination of carbamazepine (CBZ). The prepared nanocomposite was characterized by XRD, SEM and FT‐IR spectral methods. CBZ exhibited an irreversible oxidation peak at 0.85 V on NiSe2−GO/GCE in phosphate buffer of pH 7.0. A 54‐fold enhancement in oxidation peak current was observed at NiSe2−GO/GCE when compared to that at bare GCE. Sensing performance of NiSe2−GO/GCE was optimized by varying peak current dependent parameters. Linear relationship between the peak current and concentration of CBZ was observed in the range of 4.50×10−8–3.22×10−5 mol L−1 for differential pulse voltammetric method and 2.32×10−8–4.79×10−5 mol L−1 for square wave voltammetric method. The practical utility of the proposed sensor, was demonstrated by determining CBZ in pharmaceutical formulations and spiked urine samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".