Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuO...

Area of Science:

• Materials Science
• Nanotechnology
• Biotechnology

Background:

• Bacterial infections and contamination pose significant global health risks, amplified by antimicrobial resistance.
• Effective antibacterial strategies and rapid detection methods are crucial for public health and safety.
• Current methods often lack efficiency, speed, or cost-effectiveness.

Purpose of the Study:

• To develop novel antimicrobial and SERS-active surfaces using silver-copper oxide (Ag-Cu₂O) nanostructures.
• To utilize green synthesis and low-cost paper substrates for sustainable material fabrication.
• To evaluate the bactericidal efficacy and bacterial detection capabilities of the fabricated surfaces.

Main Methods:

• Green synthesis of Ag-Cu₂O nanostructures on paper substrates.
• Assessment of antibacterial activity against *Escherichia coli* and *Staphylococcus aureus*.
• Evaluation of surface-enhanced Raman scattering (SERS) for bacterial identification.
• Integration of SERS with machine learning for automated bacterial detection.

Main Results:

• The Ag-Cu₂O nanostructured surfaces demonstrated rapid and high bactericidal efficiency (>99.99% kill rate in 30 min).
• High SERS activity enabled sensitive, label-free bacterial detection down to 10³ CFU/mL.
• Automated bacterial identification using SERS coupled with machine learning achieved >96% accuracy.
• The nanostructures facilitated leaching of intracellular components, enhancing detection sensitivity.

Conclusions:

• The developed Ag-Cu₂O nanostructured paper platform offers a dual solution for bacterial contamination prevention and detection.
• This approach leverages sustainable, low-cost materials for effective antimicrobial and diagnostic applications.
• The combination of rapid bactericidal action and sensitive, automated SERS detection presents a promising strategy against bacterial threats.