Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Biotechnology

Background:

• Label-free surface-enhanced Raman spectroscopy (SERS) offers direct interrogation of molecular properties.
• Complex biological samples often present overlapping spectra, hindering accurate identification.
• Current SERS methods struggle with specificity in intricate biological matrices.

Purpose of the Study:

• To develop an artificial-nose inspired SERS platform for enhanced biological sample identification.
• To investigate how varying sensor surface chemical functionality impacts SERS fingerprinting.
• To demonstrate the utility of high-dimensionality data for improved discriminatory accuracy.

Main Methods:

• Utilized surface-enhanced Raman spectroscopy (SERS) with diverse self-assembled monolayers on sensor surfaces.
• Employed molecular dynamics modeling to understand analyte-surface interactions.
• Generated high-dimensionality spectral datasets by combining multiple sensor fingerprints from cell lysates.

Main Results:

• Mildly selective self-assembled monolayers modulated analyte interaction strength and configuration.
• This modulation diversified SERS fingerprints, enhancing data dimensionality.
• Increased dimensionality through combined fingerprints reliably improved discriminatory accuracy towards 100%.

Conclusions:

• Arrayed label-free SERS platforms with tunable surface functionalities offer robust identification of complex biological samples.
• The artificial-nose concept, leveraging high-dimensionality data, significantly enhances sensing reliability.
• This approach holds broad potential for advanced biological matrix assessment.