Preparation and characterization of nickel oxide nanoparticles and their application in glucose and methanol sensing
Bibliographic record
Abstract
In this work, a low cost glucose and methanol nonenzymatic sensor was prepared using nickel oxide (NiO) nanofilm electrodeposited on a bare Cu electrode.Electrochemical deposition was assisted with cetyl trimethylammonium bromide (CTAB) as a template.Scanning electron microscopy (SEM) was applied to observe the surface morphology of the modified electrode.Cyclic voltammetry (CV) and amperometry techniques were used to study the electrocatalytic behavior of NiO porous film in glucose and methanol detection.For glucose sensing, the electrode showed a linear relationship in the concentration range of 0.01-2.14mM with a low limit of detection (LOD) 1.7 µM (signal/noise ratio (S/N)=3).Moreover, high sensitivities of 4.02 mA mM -1 cm -2 and 0.38 mA mM -1 cm -2 respectively in glucose and methanol monitoring suggested the modified electrode as an excellent sensor.The NiO-Cu modified electrode was relatively insensitive to common biological interferers.This sensor possessed good poison resistance towards chloride ions, and long term stability and significant selectivity towards glucose and methanol.Finally the proposed sensor was successfully applied for determination of glucose in human blood serum samples.
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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".