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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".