Treatment of Contributions of Dust to Dynamic Light Scattering Data

Abstract

Laser drifts, dust, and impurities in a sample are physical reasons for offsets in both field and intensity autocorrelation functions. In this paper, a method is described by which the two offsets are determined by a variation method. Data were analyzed with the size distribution algorithm CONTIN, using the selection method of L-curves for getting the offsets and a combination of the methods of F-test, stability plot, and L-curve for getting the final solution from the corrected data. This solution was selected according to the parsimony principle, under which the preferred solution is that with the simplest size distribution. Consistency was checked by means of two additional tools. The first is the difference between the field autocorrelation function of a regularized solution and the least-squares solution, called the difference in g1, which is compared with the residuals of the least-squares solution. The second is the relative increase in variance or the discrepancy from the best fit solution. The procedure for the determination of the offsets is described by means of experimental data of high statistical accuracy from a semidilute aqueous solution of the surfactant octaethylene glycol dodecyl monoether (C12E8). The method was tested with data containing different contributions of dust and with data from different poly(ethylene glycol) n-alkyl monoethers (C12E8, C14E8, and C16E8). After correction of the data for the two offsets, the size distributions obtained from data analysis were free of elements of unstable solutions such as side peaks at the integration limits. It is shown that differences in the size characteristics of narrow monomodal distributions of about 0.3-0.4 nm can be distinguished and that two peaks of a bimodal size distribution with a size ratio of 2:1 can be resolved.

Microemulsions with ionic liquid polar domains

“…4 demonstrates the size (hydrodynamic diameter) distributions of the microemulsions obtained from CONTIN analysis, 26 which is one of the most commonly used methods to estimate the size distribution of the aggregates in microemulsions. 27,28 Obviously, the sizes of the aggregates increase with R, which agrees reasonably with the results from FFEM, as shown in Fig. 3.…”

Improved Dynamic Light Scattering using an adaptive and statistically driven time resolved treatment of correlation data

“…Schemes to mitigate these effects might include the measurement of the sample over very long correlation times so that transient scattering from low concentrations of aggregated material, for example, is effectively averaged out of the overall result 19,21 or sufficiently detected to be incorporated into a fitting model 22,23 ; or data rejection methods such as the exclusion of aggregated, short sub-measurements, from the final averaged result 24–26 , typically based upon count rate/scattering intensity. Sub measurement durations for DLS instruments of the order of 10 s are common, Fig.…”

Post-processing noise reduction via all-photon recording in dynamic light scattering

“…However, the efficiency of the noise suppression offered by this approach strongly depends on both pollutant size and laser wavelength, which leads to incomplete removal of the noise under realistic conditions. A more effective approach is to perform measurements multiple times using an autocorrelator and take the average over "uncontaminated" signals that are free from significant noise [27][28][29][30][31][32]. For example, Bossert et al proposed the statistical moment analysis of the photon counting distribution performed by recording the temporal variation of the photon counting rate with the time scale of sub-seconds [31].…”