An emerging machine learning-optimized MIP-ECL platform for ultrasensitive and selective detection of...

Area of Science:

• Electrochemistry
• Nanomaterials Science
• Biosensing

Background:

• Accurate 5-hydroxytryptamine (5-HT) detection is vital for diagnosing and treating clinical depression.
• Existing methods for 5-HT detection may lack the required sensitivity, selectivity, or efficiency for clinical applications.
• Developing advanced sensing platforms is crucial for improving diagnostic capabilities.

Purpose of the Study:

• To develop a highly sensitive and selective electrochemiluminescence (ECL) sensor for 5-hydroxytryptamine (5-HT) detection.
• To optimize the fabrication of a molecularly imprinted polymer (MIP) based on a nanocomposite substrate.
• To apply advanced computational methods for optimizing sensor performance.

Main Methods:

• Fabrication of a nanocomposite electrode using electropolymerized graphene oxide (GO), l-arginine (L-Arg), and gold nanoparticles (AuNPs).
• Development of a MIP-based ECL sensing interface for 5-HT detection.
• Utilizing orthogonal experimental design (OED), XGBoost regression, and Bayesian optimization (BO) to optimize MIP fabrication parameters.
• Employing a cathodic Ru(bpy)₃²⁺/K₂S₂O₈- based ECL system for 5-HT quantification.

Main Results:

• The optimized MIP sensor achieved ultrasensitive and selective detection of 5-HT.
• Linear detection of 5-HT was achieved over a concentration range of 0.01-100 μM with a detection limit as low as 7.04 nM.
• A strong inverse correlation (R² = 0.99) was observed between ECL intensity and the logarithm of 5-HT concentration.
• The sensor exhibited excellent anti-interference, repeatability, and reproducibility, and was validated in spiked fetal bovine serum.

Conclusions:

• A high-performance MIP-based ECL sensor for 5-HT detection was successfully developed and optimized.
• The sensor demonstrates significant potential for accurate and sensitive 5-HT monitoring in complex biological matrices.
• The integration of computational optimization strategies enhances the development of advanced electrochemical biosensors.