Anisotropically Shaped Plasmonic WO_3- Nanostructure-Driven Ultrasensitive SERS Detection and Machine Learning-Based...

Area of Science:

• Materials Science
• Analytical Chemistry
• Nanotechnology

Background:

• Current surface-enhanced Raman spectroscopy (SERS) substrates rely on expensive noble metals (Au, Ag, Cu).
• Non-noble metal alternatives often lack the required sensitivity for widespread application.
• There is a need for cost-effective, high-performance SERS substrates for ultrasensitive analyte detection.

Purpose of the Study:

• To investigate oxygen-deficient tungsten oxide (WO3-) nanostructures as novel SERS substrates.
• To explore the structure-dependent SERS enhancement factor (EF) of WO3- nanostructures.
• To demonstrate the application of WO3- SERS substrates for explosive detection.

Main Methods:

• Colloidal synthesis of WO3- nanowires, nanorods, and nanoplatelets.
• SERS measurements using rhodamine 6G (R6G) as a probe molecule.
• Spectroscopic determination of electronic band structure and time-domain density functional theory (TDDFT) calculations.
• Detection of aromatic and non-aromatic nitro-explosives.
• Machine learning-driven chemometric analysis for explosive classification.

Main Results:

• WO3- nanostructures exhibited significant SERS enhancement factors (up to 5.5 × 10^7).
• A dual enhancement mechanism involving plasmonic and chemical effects was identified.
• Detection of aromatic nitro-explosives (tetryl, TNT, DNT) with a low limit of detection (10^-9 M).
• Successful detection of non-aromatic nitro-explosives (HMX, RDX, PETN).
• High classification accuracy for explosives using machine learning.

Conclusions:

• Oxygen-deficient WO3- nanostructures are effective, non-noble metal SERS substrates.
• These substrates enable ultrasensitive detection of diverse nitro-explosives.
• This work advances low-cost SERS applications in forensics, chemistry, and biomedicine.