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Sensitivity of the Au Cluster Attached to Graphene Nanopore in a SERS Sensor to Characterise Vibrational Spectra of Nucleotides
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1  Department of Mechanical Engineering, University of Toyama, Toyama 930-8555, Japan
Academic Editor: Sara Tombelli (registering DOI)

The photochemical techniques applied to the sensing of bioactive molecules have become one of the fastest-growing scientific fields. Surface-enhanced Raman scattering (SERS) measurement is highly sensitive for detecting low-concentration, single molecules or oligomers, including DNA, microRNA, and proteins. In the field of SERS biosensor design, the use of carbon-based nanomaterials as substrate materials is rapidly developing, and we intend to investigate mechanisms of the dynamic interaction of oligomers with the environment of the SERS sensor to specify the fingerprints of such interactions in the spectra to enhance resolution. We study the vibrational spectra of the nucleotides in the dynamic interaction with the Au nanoparticles (NP) relaxed at (grown on) graphene nanopore that combines (1) translocation localization by graphene nanopore and (2) nucleotide interaction enhancement by Au NP. The spectral map of the cytosine nucleotide was tested by molecular dynamics (MD) simulation with LJ interaction between components. The spectra of various bonds were compared in reaction coordinates for DNA nucleotides and Cartesian ones for Au NP. Spectra at the interaction with the Au NP were used to select a transient COM velocity of nucleotide passing along the cluster. At the edge of the graphene pore, the velocity has been set at 0.025 m/s that compared with the experimental range. We test the sensor’s system to evaluate the influence of the interaction with Au NP and graphene on the transient spectra calculated by MD. The frequencies and modes that can serve as markers of the corresponding Au – nucleotide and graphene - nucleotide interactions are estimated. The MD simulation creates spectral libraries for oligomer’s vibrations to specify the interaction’s type and strength in SERS sensors that can be further utilized as training data for the machine learning application in spectral recognition.

Keywords: vibrational spectra; molecular dynamics; nucleotides; Au nanoparticle; graphene; SERS;