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Reverse AIEE/ACQ Engineering in a Carbon Dot-Quantum Dot Ratiometric Nanosensor: Machine-Learning-Driven Quantitative
Shaojie Wang1, Zuyi Zhang2, Yongbo Wang3
1School of Life Science, Biomedical and Health Laboratory in Shanxi Province, Shanxi Baijiu Ecological Brewing Technology Innovation Center, Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Xinghuacun College, Shanxi University, Taiyuan 030006, China.
This study introduces a novel dual-emissive nanosensor for rapid and accurate detection of five biogenic amines, crucial for assessing food freshness and preventing spoilage. The sensor enables visual shrimp freshness assessment using a smartphone platform.
Area of Science:
- Analytical Chemistry
- Materials Science
- Biotechnology
Background:
- Biogenic amines are key indicators of food spoilage and potential health risks.
- Accurate and rapid detection methods are needed for food safety and quality control.
- Existing methods may lack specificity, speed, or on-site applicability.
Purpose of the Study:
- To develop a novel dual-emissive nanosensor for simultaneous detection of five biogenic amines.
- To establish a rapid, sensitive, and specific method for food freshness assessment.
- To create an integrated platform for on-site, visual food quality monitoring.
Main Methods:
- Construction of a dual-emissive nanosensor using green carbon dots (G-CDs) and CdTe quantum dots (QD700) via noncovalent integration.
- Utilizing competitive modulation of excited-state relaxation pathways for ratiometric fluorescence sensing.
- Employing a custom DETR with Improved Matching (DEIM) algorithm for synchronous discrimination of biogenic amines.
- Integration into a paper-based smartphone platform for visual assessment.
Main Results:
- The nanosensor demonstrated an ultralow detection limit (0.0097 μM) and a rapid response time (within 2 s).
- Achieved high-accuracy synchronous discrimination of five biogenic amines (spermine, putrescine, cadaverine, histamine, tyramine) using unique response fingerprints.
- The paper-based smartphone platform showed excellent correlation (R² = 0.95) with the standard TVB-N assay for visual shrimp freshness assessment.
- The system exhibited distinct visual cues (red for fresh, green for spoiled) for spoiled shrimp.
Conclusions:
- The developed dual-emissive nanosensor offers a sensitive, specific, and rapid method for biogenic amine detection.
- The integrated platform provides a user-friendly, on-site solution for dynamic food freshness monitoring.
- This work presents a versatile paradigm for advanced food safety and quality assessment.
