Two-dimensional self-adapting fast Fourier transform algorithm for nanoparticle sizing by ultrafast image-based dynamic light scattering
Dechuan Zhang, Xiaoshu Cai*, Wu Zhou
In nanoparticle sizing using the ultrafast image-based dynamic light scattering (UIDLS) method, larger impurities and dark noise from the complementary metal-oxide-semiconductor (CMOS) detector affect measurement accuracy. To solve this problem, a two-dimensional self-adapting fast Fourier transform (2D-SAFFT) algorithm is proposed for UIDLS. Dynamic light scattering images of nanoparticles are processed using 2D fast Fourier transforms, and a high-frequency threshold and a low-frequency threshold are then set using the self-adapting algorithm to eliminate the effects of the dark noise of the CMOS detector and the impurities. The signals caused by the dark noise of the CMOS detector and the impurities are cut off using the high-frequency threshold and the low-frequency threshold. The signals without the high- and low-frequency components are then processed again using an inverse Fourier transform to obtain new images without the dark noise and impurities signals. The mean diameters of the measured nanoparticles can be obtained from images obtained using UIDLS. Five standard latex nanoparticles (46, 100, 203, 508, 994 nm) and commercial nanoparticles (antimony-doped tin oxide, indium tin oxide, TWEEN-80, nano-Fe, and nano-Al2O3) were measured using this new method. Results show that 2D-SAFFT can effectively eliminate the effects of dark noise from the CMOS detector and the impurities.
Dynamic light scattering; Nanoparticle size; Fast Fourier transform algorithm; Spectrum segmentation