Preparation, Characterization, and Application of Nickel Oxide Nanoparticles in Glucose and Lactose Biosensors
Bibliographic record
Abstract
Nickel oxide (NiO) nanoparticles were synthesized using the co-precipitation method. The structural, morphological, optical and magnetic properties of NiO nanoparticles were investigated by X-ray Powder Diffraction (XRD), Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FTIR), UV-vis Spectroscopy and Vibrating Sample Magnetometer (VSM). The influence of sonication time on the physical characteristics of NiO nanoparticles was illustrated. The interactions of NiO nanoparticles with both glucose and lactose have been0 studied using UV-vis Absorption Spectroscopy and Fluorescence Spectroscopy. XRD pattern reveals that NiO nanoparticles exhibit a face-centered-cubic lattice structure with a crystallite size of 22.77 nm. The magnetic hysteresis demonstrates the ferromagnetic behavior of NiO nanoparticles at room temperature. The energy band gap increases, from 2.8 eV to 3.1 eV, with increasing sonication time from 5 to 20 minutes. It is found that the interaction of lactose-NiO nanoparticles is stronger than that of glucose-NiO ones. The obtained results can be investigated in future biomedical applications, especially in non-enzymatic biosensors.
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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".