Regenerable Membrane Sensors for Ultrasensitive Nanoplastic Quantification Enabled by A Data-driven Raman Spectral...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Materials Science

Background:

• Nanoplastic (NP) detection in natural waters is challenging due to complex matrices and analytical limitations.
• Existing methods struggle with sensitivity, accuracy, and single-particle analysis.

Purpose of the Study:

• To develop an ultrasensitive, rapid, and quantitative method for single-particle nanoplastic detection.
• To integrate a novel Raman spectral processing algorithm (Pre_seg) with regenerable anodic aluminum oxide (AAO) membrane sensors.

Main Methods:

• Utilized AAO membranes as combined filtration substrates and Raman sensors to minimize sample loss.
• Developed the Pre_seg algorithm with statistically determined thresholds for signal-to-noise ratios (SNRs) and full width at half maximums (fwhms).
• Validated the method in diverse lake water matrices after oxidation digestion pretreatment.

Main Results:

• Pre_seg achieved 93.5% prediction accuracy for nanoplastics and ≥90.4% rejection accuracy for non-NP particles.
• Quantified mixed nanoplastics at concentrations as low as 0.5 μg L⁻¹.
• Demonstrated robustness in eutrophic and oligotrophic lake water and confirmed sensor stability over multiple regeneration cycles.

Conclusions:

• The Pre_seg algorithm and AAO sensor system offer a highly sensitive and accurate approach for nanoplastic detection.
• This innovative method enables scalable, customizable, and environmentally relevant monitoring of nanoplastics in water systems.