Nanoparticle Characterization by PCS: The Analysis of Bimodal Distributions

Mannosylated brush copolymers based on poly(ethylene glycol) and poly(ε-caprolactone) as multivalent lectin-binding nanomaterials

A class of linear and four-arm mannosylated brush copolymers based on poly(ethylene glycol) and poly(ε-caprolactone) is presented here. The synthesis through ring-opening and atom transfer radical polymerizations provided high control over molecular weight and functionality. A post-polymerization azide-alkyne cycloaddition allowed for the formation of glycopolymers with different mannose valencies (1, 2, 4, and 8). In aqueous media, these macromolecules formed nanoparticles that were able to bind lectins, as investigated by concanavalin A binding assay. The results indicate that carbohydrate-lectin interactions can be tuned by the macromolecular architecture and functionality, hence the importance of these macromolecular properties in the design of targeted anti-pathogenic nanomaterials.

Information-weighted constrained regularization for particle size distribution recovery in multiangle dynamic light scattering

In particle size measurement with dynamic light scattering (DLS), it is difficult to get an accurate recovery of a bimodal particle size distribution (PSD) with a peak position ratio less than ~2:1, especially when large particles (>350nm) are present. This is due to the inherent noise in the autocorrelation function (ACF) data and the scarce utilization of PSD information during the inversion process. In this paper, the PSD information distribution in the ACF data is investigated. It was found that the initial decay section of the ACF contains more information, especially for a bimodal PSD. Based on this, an information-weighted constrained regularization (IWCR) method is proposed in this paper and applied in multiangle DLS analysis for bimodal PSD recovery. By using larger (or smaller) coefficients for weighting the ACF data, more (or less) weight can then be given to the initial part of the ACF. In this way, the IWCR method can enhance utilization of the PSD information in the ACF data, and effectively weaken the effect of noise at large delay time on PSD recovery. Using this method, bimodal PSDs (with nominal diameters of 400:608 nm, 448:608 nm, 500:600 nm) were recovered successfully from simulated data and it appears that the IWCR method can improve the recovery resolution for closely spaced bimodal particles. Results of the PSD recovery from experimental DLS data confirm the performance of this method.

Sizing Individual Au Nanoparticles in Solution with Sub-Nanometer Resolution

Resistive-pulse sensing has generated considerable interest as a technique for characterizing nanoparticle suspensions. The size, charge, and shape of individual particles can be estimated from features of the resistive pulse, but the technique suffers from an inherent variability due to the stochastic nature of particles translocating through a small orifice or channel. Here, we report a method, and associated automated instrumentation, that allows repeated pressure-driven translocation of individual particles back and forth across the orifice of a conical nanopore, greatly reducing uncertainty in particle size that results from streamline path distributions, particle diffusion, particle asphericity, and electronic noise. We demonstrate ∼0.3 nm resolution in measuring the size of nominally 30 and 60 nm radius Au nanoparticles of spherical geometry; Au nanoparticles in solution that differ by ∼1 nm in radius are readily distinguished. The repetitive translocation method also allows differentiating particles based on surface charge density, and provides insights into factors that determine the distribution of measured particle sizes.

Resolving power of dynamic light scattering for protein and polystyrene nanoparticles

Dynamic light scattering (DLS) is a non-invasive, label-free technique for the characterization of particles ranging from nanometer to micrometer size. It is widely used for the analysis of proteins to assess association states and the nature of protein aggregates. Despite its frequent use, little quantitative information on its size resolution capabilities, in particular for protein material, is available. This study explores the resolving power of a standard DLS setup for binary mixtures of latex standard particles and mixtures of protein monomer and protein particles made from cross-linked protein material. At constant instrument settings, the resolving power depends on the size ratio and the mass ratio of the species in a mixture as well as on the total concentration and the scattering characteristics of the material. In this study, we provide a summary at which parameter combinations resolution of two species with varying size is possible. These data guide the quantitative evaluation of DLS results for mixtures. We found that a mixture of an antibody monomer and protein particles of an average hydrodynamic diameter of 50 nm can be resolved at a 1-20-fold excess of monomer (by mass). A mixture of monomer and 70 nm particles can be resolved at a 2-30-fold excess, a mixture of monomer and 190 nm particles at a 200-1700-fold excess of monomer. The findings allow to better judge DLS results for protein samples of unknown composition.

Measurement of lipoprotein particle sizes using dynamic light scattering

Background

A simple method for the measurement of LDL particle sizes is needed in clinical laboratories because a predominance of small, dense LDL (sd LDL) has been associated with coronary heart disease. We applied dynamic light scattering (DLS) to measure lipoprotein particle sizes, with special reference to sd LDL.

Methods

Human serum lipoproteins isolated by a combination of ultracentrifugation and gel chromatography, or by sequential ultracentrifugation, were measured for particle size using DLS.

Results

The sizes of polystyrene beads, with diameters of 21 and 28 nm according to the manufacturer, were determined by DLS as 19.3 ± 1.0 nm (mean ± SD, n = 11) and 25.5 ± 1.0 nm, respectively. The coefficients of variation for the 21 and 28 nm beads were 5.1% and 3.8% (within-run, n = 11), and 2.9% and 6.2% (between-run, n = 3), respectively. The lipoprotein sizes determined by DLS for lipoprotein fractions isolated by chromatography were consistent with the elution profile. Whole serum, four isolated lipoprotein fractions (CM + VLDL + IDL, large LDL, sd LDL and HDL) and a non-lipoprotein fraction isolated by sequential ultracentrifugation were determined by DLS to be 13.1 ± 7.5, 37.0 ± 5.2, 21.5 ± 0.8, 20.3 ± 1.1, 8.6 ± 1.5 and 8.8 ± 2.0 nm, respectively.

Conclusions

The proposed DLS method can differentiate the sizes of isolated lipoprotein particles, including large LDL and sd LDL, and might be used in clinical laboratories in combination with convenient lipoprotein separation.

Fraction estimation of small, dense LDL using autocorrelation function of dynamic light scattering

Small, dense low-density lipoprotein (sdLDL) in total LDL is strongly related with the cardiovascular risk level. An optical technique using dynamic light scattering (DLS) measurement is useful for point-of-care testing of sdLDL. However, the sdLDL fraction estimated from the particle size distribution in DLS data is sensitive to noise and artifacts. Therefore, we derived analytical solutions in a closed form to estimate the fraction of scatterers using the autocorrelation function of scattered light from a polydisperse solution. The effect of the undesired large particles can be eliminated by the pre-processing of the autocorrelation function. The proposed technique was verified using latex standard particles and LDL solutions. Results suggest the feasibility of this technique to estimate the sdLDL fraction using optical scattering measurements.

Application of a constrained regularization method to extraction of affinity distributions: proton and metal binding to humic substances

The binding of proton and metal cations to humic substances has been analyzed with a regularized fitting procedure (using the CONTIN software package) to extract conditional affinity distributions, valid at a given ionic strength, from binding (titration) curves. The procedure was previously tested with simulated titration curves using a simple bi-Gaussian model, the NICA-Donnan model, and the Stockholm humic model. Application to literature data for proton binding shows that in several cases the affinity distribution found is bimodal (carboxylic and phenolic sites) as usually assumed; however in other cases, specially for fulvic acids, a trimodal distribution is clearly discerned, with a smaller peak between the two noted above attributed to the presence of vicinal carboxylic groups. The analysis of metal binding curves has been performed in a few cases where the available data could be reliably processed, separating the proton affinity distribution and obtaining the conditional affinity spectra. For Cd(II) and Pb(II) a bimodal distribution is found, attributed in principle to mono- and bidentate binding, based on spectroscopic data. In the case of Cu(II), a more complex affinity distribution is found showing 3-4 peaks; this is consistent with spectroscopic studies, where different binding modes, up to tetradentate, have been observed.