Submicron particle size distributions by dynamic light scattering with non-negative least-squares algorithm

Rafat R. Ansari, Su-Long Nyeo

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A method is proposed using the non-negative least-squares (NNLS) algorithm of Lawson and Hanson to analyze dynamic light scattering (DLS) data for the size distribution of particles in a colloidal dispersion. The NNLS algorithm gives sparse solutions, which are sensitive to the domains used for reconstructing the solutions. The method uses the algorithm to construct an optimal solution from a set of sparse solutions of different domains but of the same dimension. The sparse solutions are superimposed to give a general solution with its dimension being treated as a regularization parameter. An optimal solution is specified by a suitable value for the dimension, which is determined by either Morozov's criterion or the L-curve method. Simulated DLS data are generated from a unimodal and a bimodal distribution for evaluating the performance of the method, which is then applied to analyze experimental DLS data from the ocular lenses of a fetal calf and a Rhesus monkey to obtain optimal size distributions of the α-crystallins and crystallin aggregates in the ocular lenses.

Original languageEnglish
Pages (from-to)459-477
Number of pages19
JournalChinese Journal of Physics
Volume50
Issue number3
Publication statusPublished - 2012 Jun

Fingerprint

particle size distribution
light scattering
lenses
monkeys
calves
curves

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

@article{0fef8f844d1a4529850203f2e25ef133,
title = "Submicron particle size distributions by dynamic light scattering with non-negative least-squares algorithm",
abstract = "A method is proposed using the non-negative least-squares (NNLS) algorithm of Lawson and Hanson to analyze dynamic light scattering (DLS) data for the size distribution of particles in a colloidal dispersion. The NNLS algorithm gives sparse solutions, which are sensitive to the domains used for reconstructing the solutions. The method uses the algorithm to construct an optimal solution from a set of sparse solutions of different domains but of the same dimension. The sparse solutions are superimposed to give a general solution with its dimension being treated as a regularization parameter. An optimal solution is specified by a suitable value for the dimension, which is determined by either Morozov's criterion or the L-curve method. Simulated DLS data are generated from a unimodal and a bimodal distribution for evaluating the performance of the method, which is then applied to analyze experimental DLS data from the ocular lenses of a fetal calf and a Rhesus monkey to obtain optimal size distributions of the α-crystallins and crystallin aggregates in the ocular lenses.",
author = "Ansari, {Rafat R.} and Su-Long Nyeo",
year = "2012",
month = "6",
language = "English",
volume = "50",
pages = "459--477",
journal = "Chinese Journal of Physics",
issn = "0577-9073",
publisher = "Physical Society of the Republic of China",
number = "3",

}

Submicron particle size distributions by dynamic light scattering with non-negative least-squares algorithm. / Ansari, Rafat R.; Nyeo, Su-Long.

In: Chinese Journal of Physics, Vol. 50, No. 3, 06.2012, p. 459-477.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Submicron particle size distributions by dynamic light scattering with non-negative least-squares algorithm

AU - Ansari, Rafat R.

AU - Nyeo, Su-Long

PY - 2012/6

Y1 - 2012/6

N2 - A method is proposed using the non-negative least-squares (NNLS) algorithm of Lawson and Hanson to analyze dynamic light scattering (DLS) data for the size distribution of particles in a colloidal dispersion. The NNLS algorithm gives sparse solutions, which are sensitive to the domains used for reconstructing the solutions. The method uses the algorithm to construct an optimal solution from a set of sparse solutions of different domains but of the same dimension. The sparse solutions are superimposed to give a general solution with its dimension being treated as a regularization parameter. An optimal solution is specified by a suitable value for the dimension, which is determined by either Morozov's criterion or the L-curve method. Simulated DLS data are generated from a unimodal and a bimodal distribution for evaluating the performance of the method, which is then applied to analyze experimental DLS data from the ocular lenses of a fetal calf and a Rhesus monkey to obtain optimal size distributions of the α-crystallins and crystallin aggregates in the ocular lenses.

AB - A method is proposed using the non-negative least-squares (NNLS) algorithm of Lawson and Hanson to analyze dynamic light scattering (DLS) data for the size distribution of particles in a colloidal dispersion. The NNLS algorithm gives sparse solutions, which are sensitive to the domains used for reconstructing the solutions. The method uses the algorithm to construct an optimal solution from a set of sparse solutions of different domains but of the same dimension. The sparse solutions are superimposed to give a general solution with its dimension being treated as a regularization parameter. An optimal solution is specified by a suitable value for the dimension, which is determined by either Morozov's criterion or the L-curve method. Simulated DLS data are generated from a unimodal and a bimodal distribution for evaluating the performance of the method, which is then applied to analyze experimental DLS data from the ocular lenses of a fetal calf and a Rhesus monkey to obtain optimal size distributions of the α-crystallins and crystallin aggregates in the ocular lenses.

UR - http://www.scopus.com/inward/record.url?scp=84866772389&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866772389&partnerID=8YFLogxK

M3 - Article

VL - 50

SP - 459

EP - 477

JO - Chinese Journal of Physics

JF - Chinese Journal of Physics

SN - 0577-9073

IS - 3

ER -