Modeling approach for Ti₃C₂ MXene-based fluorescent aptasensor for amoxicillin biosensing in water matrices.

Area of Science:

• Analytical Chemistry
• Biotechnology
• Environmental Science

Background:

• Amoxicillin is a widely used penicillin-class antibiotic with applications in medicine, agriculture, and the food industry.
• Precise and sensitive detection of amoxicillin is crucial due to its extensive use and potential environmental impact.
• Existing detection methods may lack the sensitivity or selectivity required for complex matrices like wastewater.

Purpose of the Study:

• To develop a novel, sensitive, and selective fluorescent aptasensor for amoxicillin detection.
• To optimize the aptasensor performance using a dual optimization strategy.
• To validate the aptasensor's efficacy in real-world water samples.

Main Methods:

• Development of a fluorescence resonance energy transfer (FRET)-based aptasensor using a carboxyfluorescein-labeled aptamer (donor) and MXene (acceptor/quencher).
• Optimization of experimental conditions using Response Surface Methodology-Central Composite Design (RSM-CCD) and Artificial Neural Network-Genetic Algorithm (ANN-GA).
• Characterization of the aptasensor's linear range, detection limit, selectivity, stability, and reproducibility.

Main Results:

• The aptasensor exhibited a broad linear range from 100 to 2400 ng/mL for amoxicillin detection.
• An exceptionally low limit of detection (LOD) of 1.53 ng/mL was achieved.
• The aptasensor demonstrated excellent selectivity for amoxicillin against other common antibiotics and was validated in real water samples.

Conclusions:

• The developed FRET-based fluorescent aptasensor provides a highly sensitive and selective method for amoxicillin detection.
• The dual optimization strategy effectively enhanced the aptasensor's performance.
• This aptasensor is a promising tool for monitoring amoxicillin concentrations in environmental water samples.