Molar mass distributions of linear homopolymers by size exclusion chromatography with light scattering detection: A method for automatic band broadening correction.

Size exclusion chromatography (SEC) equipped with a differential refractometer (DR) and a light scattering (LS) detector is a well-known technique for determining the molar mass distribution (MMD) of many polymers. In the case of narrow polymers, correction of the band broadening (BB) effect is necessary; but unfortunately, the available BB correction methods are rather impractical for most SEC users. This work proposes an automatic BB correction method for determining the MMD of narrow linear homopolymers (or multimodal homopolymers that include narrow modes) on the basis of SEC/(DR + LS) measurements. The required data are: the baseline-corrected DR and LS chromatograms, the inter-detector volume (IDV), and a molar mass calibration independently determined from narrow standards. In comparison to other available alternatives for BB correction, the here-proposed method has the following key advantages: a) no previous knowledge on the BB function is required; b) the detectors gain constants are unnecessary; and c) no numerical regularization method is required. Moreover, if the IDV is unknown, then the proposed method could also be used for estimating the IDV from the knowledge of the dispersity index of a narrow homopolymer. The proposal was experimentally assessed by analyzing narrow standards of poly(styrene) and poly(methyl-methacrylate). The proposed method estimated the dispersity indexes of the standards with errors lower than 0.9% with respect to values reported by manufacturers (between 1.015 and 1.044); while the classical approaches based on SEC/DR and SEC/(DR + LS), produced errors of up to -11% and 3%, respectively.

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.