Amperometric Determination of Xanthine Using Nanostructured NiO Electrodes Loaded with Xanthine Oxidase
Bibliographic record
Abstract
Nickel oxide (NiO) thin films prepared by glancing angle deposition (GLAD) were investigated as electrodes for enzymatic electrochemical quantification of xanthine, a noted indicator of meat freshness. The large surface area of the macroporous GLAD NiO electrodes provided a suitable scaffold to successfully immobilize the enzyme xanthine oxidase (XO), and this XO immobilization was characterized by cyclic voltammetry, electrochemical impedance spectroscopy, and X-ray photoelectron spectroscopy. The XO-modified GLAD NiO electrodes electrochemically oxidize xanthine, with electron transfer from this adsorbed XO to the (Ni 2+ /Ni 3+ ) redox species resulting in a strong amperometric response to xanthine in a reagent-free alkaline medium. Under optimal conditions, the fabricated xanthine biosensor exhibited a rapid response (∼7 s), wide dynamic range (0.1–650 μM), good reproducibility (relative standard deviation of ∼4%, n = 18), superior limit of detection (37 nM), and very high sensitivity (1.1 μA·μM –1 ·cm –2 in the low concentration range from 0.1–5 μM and 0.3 μA·μM –1 ·cm –2 in the higher concentration range from 5–650 μM). The biosensor was also evaluated against a selection of potential interferents commonly found in fish samples (hypoxanthine, uric acid, glucose, and sodium benzoate), demonstrating good selectivity toward xanthine. A low Michaelis–Menten constant ( K m ) of 0.4 mM for xanthine signifies the high affinity of the enzymatic sensor toward the target analyte. Measurements in real fish samples were also successfully performed, revealing strongly increased xanthine sensitivity in the presence of fish matrices (from 0.085 μA μM –1 without fish extract to as much as 0.27 μA μM –1 ).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".