Defect Engineered 2D MoS2 Materials for ML-enabled Neurotransmitter SERS Detection
An attachment of catechol-containing neurotransmitter molecules is demonstrated on defect-engineered two-dimensional MoS2 platform, leading to activation of SERS due to molecular charge transfer. Mechanisms of neurotransmitters' bio-detection are discussed and Machine Learning methods are applied to distinguish spectra of structurally similar analytes. The SERS effect and selective docking of biomolecules are achieved through an optimized approach for defect engineering: namely, introducing the sulfur vacancies in MoS2 monolayer films via soft plasma etching led to molecular attachment driven by catechol functional groups. The quality of the sensor material was controlled by Raman, photoluminescence, and XPS characterization, thus allowing for optimization of the process of defect formation and achieving sensing selectivity. The sensor material showed no response to serotonin, confirming the specificity of attachment/SERS due to S-vacancies that regulate the strength of catechol-specific molecular adsorption. Defect-engineered MoS2 has enabled SERS detection of dopamine and epinephrine down to the sub-nanomolar range ($5\times10^{-10}$ M), with strong calibration reliability ($R^2$ = 0.95 and 0.99 for pure samples). PCA-LDA achieved 100$\%$ accuracy in distinguishing dopamine and epinephrine, which establishes defect-engineered MoS2 as a tunable, low-cost SERS platform for future sensing applications.