Speaker
Description
Accurate quantification of gold nanoparticles (AuNPs) is critical for applications in nanotechnology, biomedicine, and functional materials. In this study, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) is developed as a quantitative optical sensor for direct determination of AuNP concentration using
multivariate calibration.
NELIBS relies on the deposition of metallic nanoparticles onto a substrate prior to laser irradiation. Upon laser excitation, localized surface plasmon resonance(LSPR) in the metallic nanoparticles induces strong near-field enhancement,lowering the breakdown threshold and promoting more efficient plasma formation. This localized electromagnetic amplification modifies plasma temperature,
electron density, and emission intensity, thereby enhancing spectral sensitivity compared to conventional LIBS.
Colloidal 20 nm AuNPs at ten concentration levels were deposited onto titanium and aluminum substrates and analyzed using 532 nm and 1064 nm Nd:YAG excitation. Instead of single-line intensity calibration, full-spectrum partial least squares regression (PLSR) was employed to construct predictive models. The optimized PLSR model at 532 nm exhibited excellent performance (R² = 0.999) with relative prediction error below 5% and a limit of detection of ~2.5 ppm. Although 1064 nm excitation produced stronger emission enhancement at optimal
AuNP concentrations, its quantitative stability was lower (~13% relative error).
Plasma diagnostics, including electron temperature and electron density, were evaluated to correlate enhancement behavior with predictive accuracy. The results demonstrate that coupling plasmonic enhancement with multivariate spectral modeling enables reliable and sensitive AuNP quantification.
This work establishes NELIBS combined with PLSR as a robust analytical
platform for nanoparticle concentration sensing and provides insight into the interplay between plasmonic field enhancement, excitation wavelength, and quantitative plasma spectroscopy.