Molecular characteristics of poly(1-trimethylsilyl-1-propyne) in dilute solutions

Zaitseva, I. I.; Michailova, N. A.; Rjumtsev, E. I.

doi:10.1016/j.polymer.2003.12.023

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…The scaling index R indicates that the PEG chain exists as a random coil in water. 14,27,28,39 This scaling can help understand the interactions of PEG and proteins. 3,40 Figure 6 shows the M w dependence of D 0 .…”

Section: Resultsmentioning

confidence: 99%

“…From the plot, we have K s = 6.14 × 10 −3 S and α = 0.469 ± 0.008. The scaling index α indicates that the PEG chain exists as a random coil in water. ,,, This scaling can help understand the interactions of PEG and proteins. , …”

Section: Resultsmentioning

confidence: 99%

“…An analytical ultracentrifuge (AUC) can characterize the sedimentation coefficient, diffusion coefficient, and absolute molecular weight of a sample with molar masses from 10 2 to 10 13 g/mol based on sedimentation velocity (SV) and sedimentation equilibrium (SE) analysis. − In this study, we have investigated the diffusion of PEG with molecular weights from 5 × 10 2 to 2 × 10 5 g/mol by sedimentation velocity analysis. Our aim is to understand the scaling for the diffusion of PEG in water.…”

Section: Introductionmentioning

confidence: 99%

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Scaling for Sedimentation and Diffusion of Poly(ethylene glycol) in Water

Luo

Zhang

2009

J. Phys. Chem. B

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The sedimentation and diffusion of poly(ethylene glycol) (PEG) with weight average molecular weight (M(w)) from 5 x 10(2) to 2 x 10(5) g/mol have been investigated by use of an analytical ultracentrifuge (AUC). The sedimentation velocity analysis based on the Lamm equation gives the sedimentation coefficient (s) and diffusion coefficient (D). Our study reveals that the sedimentation coefficient (s(0)) and diffusion coefficient (D(0)) at infinite dilution scale to molecular weight (M(w)), namely, s(0) = K(s)M(w)(alpha), with K(s) = 6.14 x 10(-3) and alpha = 0.469 +/- 0.008, and D(0) = K(D)M(w)(-beta), with K(D) = 1.43 x 10(-8) and beta = 0.576 +/- 0.007.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scaling for Sedimentation and Diffusion of Poly(ethylene glycol) in Water

Luo

Zhang

2009

J. Phys. Chem. B

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“…Analytical ultracentrifugation is a classical first-principle technique for the characterization of biological macromolecules, widely used in the studies of proteins, [1][2][3] carbohydrates, [4][5][6] nucleic acids, 7,8 as well as man-made macromolecules, including polymers, 9 compounds in supramolecular chemistry, 10,11 drug delivery particles, 12 and others. [13][14][15] Because of improved instrumentation and data analysis approaches, analytical ultracentrifugation has experienced a renaissance, 16 in particular, for the characterization of reversible macromolecular self-and hetero-association processes.…”

Section: Introductionmentioning

confidence: 99%

“…Analytical ultracentrifugation is a classical first-principle technique for the characterization of biological macromolecules, widely used in the studies of proteins, − carbohydrates, − nucleic acids, , as well as man-made macromolecules, including polymers, compounds in supramolecular chemistry, , drug delivery particles, and others. − Because of improved instrumentation and data analysis approaches, analytical ultracentrifugation has experienced a renaissance, in particular, for the characterization of reversible macromolecular self- and hetero-association processes. Analytical ultracentrifugation is uniquely suited to determine the number, size, and gross shape and conformation of non-reversible and reversible macromolecular complexes, to study their interaction with ligands, and to observe the assembly of multiprotein complexes.…”

Section: Introductionmentioning

confidence: 99%

Using Prior Knowledge in the Determination of Macromolecular Size-Distributions by Analytical Ultracentrifugation

2007

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Analytical ultracentrifugation has reemerged as a widely used tool for the study of ensembles of biological macromolecules to understand, for example, their size-distribution and interactions in free solution. Such information can be obtained from the mathematical analysis of the concentration and signal gradients across the solution column and their evolution in time generated as a result of the gravitational force. In sedimentation velocity analytical ultracentrifugation, this analysis is frequently conducted using high resolution, diffusion-deconvoluted sedimentation coefficient distributions. They are based on Fredholm integral equations, which are ill-posed unless stabilized by regularization. In many fields, maximum entropy and Tikhonov-Phillips regularization are wellestablished and powerful approaches that calculate the most parsimonious distribution consistent with the data and prior knowledge, in accordance with Occam's razor. In the implementations available in analytical ultracentrifugation, to date, the basic assumption implied is that all sedimentation coefficients are equally likely, and that the information retrieved should be condensed to the least amount possible. Frequently, however, more detailed distributions would be warranted by specific detailed prior knowledge on the macromolecular ensemble under study, such as, the expectation of the sample to be monodisperse or paucidisperse, or the expectation for the migration to establish a bimodal sedimentation pattern based on Gilbert & Jenkins' theory for the migration of chemically reacting systems. So far, such prior knowledge has remained largely unused in the calculation of the sedimentation coefficient or molecular weight distributions, or was only applied as constraints. In the present paper, we examine how prior expectations can be built directly into the computational data analysis, conservatively in a way that honors the complete information of the experimental data, whether or not consistent with the prior expectation. Consistent with analogous results in other fields, we find that use of available prior knowledge can have a dramatic effect on the resulting molecular weight, sedimentation coefficient and size-and-shape distributions, and significantly increase both their sensitivity and resolution. Further, the use of multiple alternative priors allows to probe the range of possible interpretations consistent with the data.

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Substituted Polyacetylenes

Sakaguchi

Masuda

2017

Membrane Materials for Gas and Vapor Separation

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Molecular characteristics of poly(1-trimethylsilyl-1-propyne) in dilute solutions

Cited by 8 publications

References 23 publications

Scaling for Sedimentation and Diffusion of Poly(ethylene glycol) in Water

Scaling for Sedimentation and Diffusion of Poly(ethylene glycol) in Water

Using Prior Knowledge in the Determination of Macromolecular Size-Distributions by Analytical Ultracentrifugation

Substituted Polyacetylenes

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