Enhanced Photocatalytic and Electrochemical Application of Combustion‐Synthesized Nb<sub>2</sub>O<sub>5</sub>/MgO/Fe<sub>2</sub>O<sub>3</sub> Nanocomposites
Bibliographic record
Abstract
ABSTRACT The development of advanced nanocomposites has the potential to greatly enhance the detection of hazardous chemicals in the environment. This research focuses on synthesizing and characterizing Nb2O5/MgO/Fe2O3 nanocomposites to address existing limitations and offer sustainable solutions. The X‐ray diffraction (XRD) study confirms the phase formation and crystalline nature of the nanocomposites with an average particle size of 55 nm. Fourier‐transform infrared (FTIR) spectroscopy confirms the presence of functional groups in the nanocomposite. UV–visible spectroscopy of the prepared Nb2O5/MgO/Fe2O3 nanocomposite reveals synergistic interactions among the phases, leading to a remarkable reduction in the bandgap (2.2 eV). Scanning electron microscopy (SEM) provides insights into the surface morphology of the nanocomposite. Zeta potential analysis reveals a surface charge of −32.5 mV, indicating excellent stability. Photocatalytic studies show effective dye degradation at an optimized catalyst concentration of 20 mg in 40 ppm dye solutions under UV irradiation. Furthermore, its porous structure and high surface area contribute to superior photocatalytic degradation of azo dyes and efficient electrochemical detection of mercury chloride and dextrose in 0.1 M of HCl medium. Additionally, the photocatalytic efficiency of Nb2O5/MgO/Fe2O3 was evaluated for the degradation of azo dyes, such as fast orange red and direct green dyes, under UV irradiation. Systematic optimization of variables including catalyst dosage, dye concentration, and irradiation time significantly enhanced photocatalytic performance. The results confirm the nanocomposite's strong potential in environmental remediation through effective photocatalytic degradation of organic pollutants and its advanced sensing capabilities for detecting hazardous substances, which make this a versatile material with impactful applications in wastewater treatment and electrochemical sensing.
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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".