Sanghwa Jeong

This paper improves the selectivity of a serotonin-sensitive nanosensor using machine learning and Raman spectroscopy to distinguish serotonin from dopamine.

The paper uses machine learning to predict serotonin detection by DNA-SWNT sensors across multiple wavelengths, showing DNA sequence's key role and limitations due to dataset size.

This paper develops a near-infrared serotonin nanosensor through synthetic evolution, showing high selectivity and affinity for serotonin over other compounds and enabling reversible imaging in brain slices.