4.8 Article

Bacterial DNA Recognition by SERS Active Plasma-Coupled Nanogold

期刊

NANO LETTERS
卷 22, 期 23, 页码 9757-9765

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.2c02835

关键词

DNA genomic ratio; plasma electrochemical reduction; coupled plasmonic nanogold; DNA Raman fingerprints

资金

  1. NATO [G5814, 766894]
  2. ARRS program [P1-0417, J4-1770]
  3. AD FUTURA, Public Scholarship, Development, Disability, and Maintenance Fund of the Republic of Slovenia
  4. [P1-0390]
  5. [ELIXIR-SI RI-SI-2]
  6. [J2-4490]
  7. [J2-4451]

向作者/读者索取更多资源

Surface-enhanced Raman spectroscopy (SERS) can identify bacteria based on their genomic DNA composition. Spectral differentiation of bacterial species was achieved using nanogold aggregates, allowing SERS probing at nanogram sample quantities. Molecular fingerprints and nitrogenous base content estimation confirmed the genomic composition.
It is shown that surface-enhanced Raman spectros-copy (SERS) can identify bacteria based on their genomic DNA composition, acting as a sample-distinguishing marker. Successful spectral differentiation of bacterial species was accomplished with nanogold aggregates synthesized through single-step plasma reduction of the ionic gold-containing vapored precursor. A high enhancement factor (EF = 107) in truncated coupled plasmonic particulates allowed SERS-probing at nanogram sample quantities. Simulations confirmed the occurrence of the strongest electric field confinement within nanometric gaps between gold dimers/chains from where the molecular fingerprints of bacterial DNA fragments gained photon scattering enhancement. The most prominent Raman modes linked to fundamental base-pair molecular vibrations were deconvoluted and used to proceed with nitrogenous base content estimation. The genomic composition (percentage of guanine-cytosine and adenine-thymine) was successfully validated by third-generation sequencing using nanopore technology, further proving that the SERS technique can be employed to swiftly specify bioentities by the discriminative principal-component statistical approach.

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