A highly selective electrochemical sensor by using bimetallic metal organic framework for the detection of 3-Monochloropropane-1,2-diol esters (3-MCPDEs)
Bibliographic record
Abstract
3-Monochloropropane-1,2-diol esters (3-MCPDEs) are hazardous contaminants in heat-processed foods, raising serious food safety concerns. In this study, a high-performance Fe-Mn-MOF/NIF sensor was developed via a simple hydrothermal method for rapid and precise 3-MCPDEs detection. Structural and morphological analyses confirmed the sensor stability, with FTIR revealing strong interactions between (-CH) and (O-H) stretching vibrations, while XRD indicated slight crystal distortion upon detection. FESEM and TEM confirmed minimal aggregation, ensuring homogeneous iron (Fe) and manganese (Mn) distribution. The sensor exhibited a large surface area, significantly enhancing its detection capability. Using square wave voltammetry (SWV) method, it achieved a broad linear range (0.05–255 µM), low detection limit (0.002 µM), and exceptional sensitivity (43.71 µA µM −1 cm −2 ). Comparative analysis with GC-MS demonstrated comparable accuracy, while real-sample testing in soy sauce confirmed high recovery rates. Unlike conventional methods, this sensor enables cost-effective, rapid, and on-site detection, offering a transformative approach for food safety monitoring. With its superior performance, the Fe-Mn-MOF/NIF sensor presents a breakthrough in electrochemical detection, paving the way for enhanced food quality control and public health protection. • Fe-Mn-MOF/NIF sensor was synthesized for rapid and precise 3-MCPDE detection. • Sensor demonstrated a wide detection range of 0.05–255 µM for 3-MCPDE detection. • LOD and sensitivity were 0.002 µM and 43.71 µA µM⁻¹ cm⁻² respectively. • The recovery rate was 94.43 % in soy sauce, confirming the practical applicability.
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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".