Dual-Mode Ratiometric Fluorescence Ce-UiO-66-NH<sub>2</sub> Sensor for Hydrogen Peroxide Detection
Bibliographic record
Abstract
Hydrogen peroxide (H 2 O 2 ) is extensively used in the food industry for its effective sterilization and bleaching properties. Excessive intake of H 2 O 2 can lead to poisoning, respiratory difficulties, and even cancer. Therefore, it is crucial to establish a method for the detection of H 2 O 2 . In this study, a dual-mode ratiometric fluorescence nanozyme sensor was developed and a smart label for the RGB analysis was constructed, which can accurately detect H 2 O 2 . The sensor exhibited pronounced color changes from colorless to yellow, accompanied by a fluorescence shift from blue to yellow. Additionally, ultraviolet–visible (UV–vis) absorption demonstrated a linear correlation with H 2 O 2 concentration ranging from 10 to 120 μM, with high sensitivity (Limit of detection, LOD = 1.06 nM). Fluorescence intensity ratio also exhibited a linear correlation ranging from 10 to 500 μM, with high selectivity and sensitivity (LOD = 0.33 μM). Based on this, the sensor had been successfully applied to the detection of the H 2 O 2 concentration in leafy greens. Furthermore, we integrated the sensor with cellulose-based filter paper, which allowed for the quantitative detection of H 2 O 2 through the RGB analysis. These results demonstrated the excellent performance of the sensor in detecting H 2 O 2, which provided robust technical support for ensuring food safety.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".