Enhanced photocatalytic and electrochemical performance of hydrothermally prepared NiO‐doped Co nanocomposites
Bibliographic record
Abstract
O) and sodium hydroxide (NaOH) as initial substances. We analyzed the characteristics of the product nanostructures, including their structure, optical properties, and magnetic properties, using various techniques such as x-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet absorption spectroscopy (UV-Vis), Fourier transform infrared (FTIR) spectroscopy, and vibrating sample magnetometers (VSM). The NiO nanoparticles doped with Co showed photocatalytic activity in degrading methylene blue (MB) dye in aqueous solutions. We calculated the degradation efficiencies by analyzing the UV-Vis absorption spectra at the dye's absorption wavelength of 664 nm. It was observed that the NiO-doped Co nanoparticles facilitated enhanced recombination and migration of active elements, which led to more effective degradation of organic dyes during photocatalysis. We also assessed the electrochemical properties of the materials using cyclic voltammetry (CV) and impedance spectroscopy in a 1 mol% NaOH solution. The NiO-modified electrode exhibited poor voltammogram performance due to insufficient contact between nanoparticles and the electrolyte solution. In contrast, the uncapped NiO's oxidation and reduction cyclic voltammograms displayed redox peaks at 0.36 and 0.30 V, respectively.
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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) | 0.001 | 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".