- Volumes 84-95 (2024)
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Volumes 72-83 (2023)
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Volume 83
Pages 1-258 (December 2023)
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Volume 82
Pages 1-204 (November 2023)
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Volume 81
Pages 1-188 (October 2023)
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Volume 80
Pages 1-202 (September 2023)
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Volume 79
Pages 1-172 (August 2023)
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Volume 78
Pages 1-146 (July 2023)
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Volume 77
Pages 1-152 (June 2023)
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Volume 76
Pages 1-176 (May 2023)
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Volume 75
Pages 1-228 (April 2023)
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Volume 74
Pages 1-200 (March 2023)
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Volume 73
Pages 1-138 (February 2023)
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Volume 72
Pages 1-144 (January 2023)
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Volume 83
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Volumes 60-71 (2022)
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Volume 71
Pages 1-108 (December 2022)
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Volume 70
Pages 1-106 (November 2022)
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Volume 69
Pages 1-122 (October 2022)
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Volume 68
Pages 1-124 (September 2022)
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Volume 67
Pages 1-102 (August 2022)
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Volume 66
Pages 1-112 (July 2022)
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Volume 65
Pages 1-138 (June 2022)
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Volume 64
Pages 1-186 (May 2022)
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Volume 63
Pages 1-124 (April 2022)
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Volume 62
Pages 1-104 (March 2022)
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Volume 61
Pages 1-120 (February 2022)
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Volume 60
Pages 1-124 (January 2022)
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Volume 71
- Volumes 54-59 (2021)
- Volumes 48-53 (2020)
- Volumes 42-47 (2019)
- Volumes 36-41 (2018)
- Volumes 30-35 (2017)
- Volumes 24-29 (2016)
- Volumes 18-23 (2015)
- Volumes 12-17 (2014)
- Volume 11 (2013)
- Volume 10 (2012)
- Volume 9 (2011)
- Volume 8 (2010)
- Volume 7 (2009)
- Volume 6 (2008)
- Volume 5 (2007)
- Volume 4 (2006)
- Volume 3 (2005)
- Volume 2 (2004)
- Volume 1 (2003)
• A 2D-SAFFT algorithm was proposed for UIDLS based on 2D-FFT and spectrum segmentation.
• Spectrum segmentation helped remove detrimental effects of camera dark noise and large particles.
• The algorithm offered better results for particle size distributions of standard nanoparticles.
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.