Multiangle dynamic light scattering analysis using a modified Chahine method

Liu, Xiaoyan; Shen, Jin; Thomas, John C.; Clementi, Luis Alberto; Sun, Xiaojuan

doi:10.1016/j.jqsrt.2011.12.012

Cited by 18 publications

(13 citation statements)

References 26 publications

(29 reference statements)

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“…A number of different approaches have been investigated for data analysis of the MDLS data. These include the CONTIN algorithm [4], the singular value decomposition method [5], the regularization method [6], the neural network algorithm [7], the Bayesian algorithm [8], and the improved Chahine algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of scattering angle error on particle size determination by multiangle dynamic light scattering

et al. 2015

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Dynamic light scattering (DLS) is a popular method of particle size measurement. Multiangle dynamic light scattering (MDLS) collects DLS data at multiple angles and analyzes the data simultaneously to improve the particle size measurement. Using data from several scattering angles admits the possibility of introducing noise caused by scattering angle error in the measurement, which may have an impact on the performance of the MDLS technique. We investigate the effect of scattering angle noise on recovered particle size distributions (PSDs) using simulated and measured MDLS data and various levels of angular noise. Our results show that, for unimodal PSDs, those with small particle sizes are more strongly affected by the noise than are medium and large particle size systems. For bimodal PSDs, those containing small-sized particles are also more affected by the noise than the systems of larger particles. Furthermore, broad PSDs are more vulnerable to angular noise than narrow PSDs.

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Section: Introductionmentioning

confidence: 99%

Effect of scattering angle error on particle size determination by multiangle dynamic light scattering

et al. 2015

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“…For this sample, data were recorded at six scattering angles: 30°, 50°, 70°, 90°, 110°, and 130°. Measurements were made using a HeNe laser and a BI-2000AT digital correlator (Brookhaven Instruments Inc., Holtsville, New York) as described previously [15]. The sample temperature was 298.15 K, and the dispersion medium refractive index was n m 1.33 for both samples.…”

Section: Resultsmentioning

confidence: 99%

“…We have shown previously that a modified Chahine method [13][14][15] performs well in recovering both unimodal and bimodal PSDs from MDLS data and have used that method as the inversion algorithm in this paper. The Chahine method is an iterative method wherein the distribution function is given by…”

Section: Theory Of Mdlsmentioning

confidence: 99%

Analysis of noisy multi-angle dynamic light scattering data

et al. 2014

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In multi-angle dynamic light scattering measurements, due to the inevitable presence of baseline measurement noise, the normalized intensity autocorrelation function (ACF) data deviates from the true value. This leads to incorrect angular weighting estimates, which affect the accuracy of inversion results and determination of particle size distributions (PSDs). We outline a method to calculate better angular weighting coefficients from the noisy intensity ACF data. The method involves first compensating for the baseline error in the ACF data and then determining the weighting coefficients. We demonstrate the method using simulated ACF data containing baseline error for unimodal and bimodal PSDs and also for experimental data for unimodal and bimodal samples. For the unimodal PSDs ACF data were simulated for 100-900 nm and 100-650 nm particle size ranges, and for bimodal PSDs 360-900 nm and 100-900 nm particle size ranges were used. The performance of our method was shown by comparing the results of weighting coefficient and PSD determination with and without baseline compensation to the known coefficient values and PSDs. With baseline compensation the relative error of the weighting coefficients decreased significantly. Furthermore, with baseline compensation, the PSD results for the four groups of simulated data were improved. The deviations between the known and recovered PSDs were decreased, the relative error of peak position obviously decreased, and the occurrence of false peaks was reduced. The PSD results from the experimental data further validates the conclusion that the method proposed apparently reduces the relative error of peak position, effectively eliminates the false peak, and improves the accuracy of the recovered PSD.

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“…It was applied by Grassl to invert spectral transmission data [15] and was later used for the inversion of light-scattering data by Santer [16]. We have used it in the analysis of multiangle DLS data [17]. Here, we use the Chahine method as the nonnegative algorithm for solving the inverse problem min ‖Ax − g‖.…”

Section: B Chahine Methodsmentioning

confidence: 99%

Improved inversion procedure for particle size distribution determination by photon correlation spectroscopy

et al. 2012

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We propose a minimum variation of solution method to determine the optimal regularization parameter for singular value decomposition for obtaining the initial distribution for a Chahine iterative algorithm used to determine the particle size distribution from photon correlation spectroscopy data. We impose a nonnegativity constraint to make the initial distribution more realistic. The minimum variation of solution is a single constraint method and we show that a better regularization parameter may be obtained by increasing the discrimination between adjacent values. We developed the S-R curve method as a means of determining the modest iterative solution from the Chahine algorithm. The S-R curve method requires a smoothing operator. We have used simulated data to verify our new method and applied it to real data. Both simulated and experimental data show that the method works well and that the first derivative smoothing operator in the S-R curve gives the best results.

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Multiangle dynamic light scattering analysis using a modified Chahine method

Cited by 18 publications

References 26 publications

Effect of scattering angle error on particle size determination by multiangle dynamic light scattering

Effect of scattering angle error on particle size determination by multiangle dynamic light scattering

Analysis of noisy multi-angle dynamic light scattering data

Improved inversion procedure for particle size distribution determination by photon correlation spectroscopy

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