Effect of Cr substitution in ZnFe2O4 nanoparticles on the electron transfer at electrochemical interfaces
Bibliographic record
Abstract
• Spinel oxide nanomaterials are of great interest in electrochemical sensors. • The effect of Cr 3+ ions on the electrochemical performance of ZnFe 2 O 4 nanomaterials is studied. • Cr 3+ ions have very little effect on the crystal structure and morphology of ZnFe 2 O 4 nanomaterials due to similar ionic size than the Fe 3+ ions. • Insertion of Cr 3+ ions has significantly affected the electrochemical sensitivity and kinetic rate constant of ZnFe 2 O 4 electrochemical sensor. • Energy gap and band edges are used to predict the type of electron transfer at the electrochemical interface. In this study, we explored the effect of Cr³⁺ substitution by partially and fully replacing Fe³⁺ in the normal spinel ZnFe₂O₄ crystal structure at electrochemical interfaces. The resulting ZnCrₓFe₂₋ₓO₄ nanomaterials exhibited an average particle size between 20 and 50 nm with a spherical morphology. The materials also demonstrated energy band gaps ranging from 2.1 to 3.1 eV X-ray diffraction (XRD) analysis confirmed that all the synthesized materials maintained a normal spinel structure, attributed to the octahedral site preference energy (OSPE) of Zn²⁺, Fe³⁺, and Cr³⁺ ions. Electrochemical performance assessments revealed that the ZnFe₂O₄-based sensor achieved a sensitivity of (37.8 ± 0.2) μA/mM with a kinetic rate constant of (13.1 ± 2.8) ms⁻¹, while the ZnCr₂O₄-based sensor exhibited a sensitivity of (32.4 ± 0.5) μA/mM and a kinetic rate constant of (3.73 ± 0.55) ms⁻¹ in the detection of paracetamol, whereas ZnCrFeO 4 sensor has produced the second-best sensitivity (35.7 ± 0.1 μA/mM) and the rate constant (4.53 ± 0.54 ms⁻¹) with the lowest limit of detection (1.94 ± 0.01 μM). These differences in electrochemical performance were correlated with the variations in the energy band gaps caused by the restructuring of the normal spinel structure. Our findings indicate that the ZnFe₂O₄ sensor has a higher potential for direct electron transfer, whereas the other sensors are more likely to facilitate surface-mediated electron transfer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.004 | 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 teacher head, 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".