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 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".