DFT-Based Theoretical Study on Label-Free SERS Detection of Type B Fumonisins: New Insights into Molecular–Substrate Interactions and Quantification Strategies

In this study, we employed density functional theory
to investigate
the interactions between type B fumonisins (FB1, FB2, and FB3) and
silver-enhancing substrates in the surface-enhanced Raman scattering
effect. Theoretical calculations of the molecular electrostatic potential
reveal that the oxygen atoms at the terminal of the tricarboxylic
acid structure in all three molecules exhibit the strongest electronegativity,
suggesting these sites as potential active sites for molecular-substrate
interactions. Molecular-Ag20 vertex-binding/surface-binding
complex models were constructed based on possible docking modes between
the molecule and the substrate, and binding energies were calculated.
The binding energy results confirm the stable existence of the complexes.
By analyzing the frontier molecular orbitals and charge density difference
maps of the molecules and complexes, charge transfer excitation near
the active sites between the molecule and Ag20 was confirmed.
Analysis of the theoretical Raman spectra of vertex-binding/surface-binding
complexes revealed selective enhancement phenomena associated with
different docking modes. These findings provide a feasible strategy
for the simultaneous quantification of the overall content of type
B fumonisins and the individual components of FB1, FB2, and FB3 in
a single detection. Finally, based on the characteristic frequency
variations of the three molecules, a theoretically reproducible, highly
sensitive, and high-throughput label-free detection strategy is proposed
for analyzing type B fumonisins in complex sample systems. This study
not only enhances our understanding of the physical mechanisms underlying
molecular-substrate interactions in the SERS effect but also demonstrates
the theoretical feasibility of using SERS for simultaneous quantification
of different type B fumonisins in complex sample systems. Moreover,
it provides a promising and potentially effective label-free sensing
strategy for detecting substances with similar molecular structures.