Defect engineered 2D MoS₂ materials for ML-enabled SERS detection of neurotransmitters

Mechanisms of neurotransmitter detection using a two-dimensional MoS₂ platform are discussed. Machine learning methods are applied to distinguish the spectra of structurally similar analytes. The attachment of catechol-containing neurotransmitter molecules to a defect-engineered MoS₂ is demonstrated, leading to activation of SERS through molecular charge transfer. The SERS effect and selective docking of biomolecules are boosted through introducing sulfur vacancies in MoS₂ monolayer films via soft plasma etching. The quality of the defect-engineered material was controlled by Raman, photoluminescence, AFM, and XPS characterization, thus allowing for optimization of the process of defect formation and achieving sensing selectivity. Defect-engineered MoS₂ has enabled SERS detection of dopamine and epinephrine down to the sub-nanomolar range (5 × 10^-10 M), with strong calibration reliability (R² = 0.95 and 0.99 for pure samples). The sensor material showed no response to serotonin, confirming the specificity of attachment due to S vacancies, which enables catechol-specific molecular adsorption. PCA-LDA achieved 100% accuracy in distinguishing dopamine and epinephrine, establishing defect-engineered MoS₂ as a tunable, low-cost SERS platform for future sensing applications.