Taylor dispersion analysis (TDA) provides calibration-free measurements of particle or molecular size by characterizing band broadening induced by the interaction of diffusion and pressure-driven laminar flow. Appropriate flow for TDA analyses is conventionally defined by Taylor's conditions, which set thresholds related to flow velocity (Pe ≥ 69) and residence time (τ > 2.5). Optimizing these parameters is complicated by the fact that they are covariant when detector position is a fixed instrumental constraint. Previous reports have reconsidered these conditions, but consensus is lacking on appropriate threshold values and on their relative contributions to measurement performance, especially in the context of capillary TDA with two detection points. Here, we describe a statistically driven experimental study of the interaction between residence time and flow velocity and the impact of this interaction on measurement precision in two-detector TDA. A six-detector array achieving 15 unique τ values per analysis was used to generate a dataset of 1200 TDA measurements across three volumetric flow rates. Two analytes, Alexafluor 532 (AF532) and R-phycoerythrin (RPE), were selected as models to bracket a pragmatic range of hydrodynamic radii (Rh) for biomolecular sizing. A strategy of iterative regression modeling with random subsampling was applied to generate a predictive model describing the effects of flow velocity and residence time on relative measurement precision. Experimental conditions based on the predictive model showed improved measurement precision for AF532 from 9 to 6% RSD, and for RPE from 24 to 12% RSD. External validation using non-training data showed precision improvements of 9.4% to 8.5% RSD for size measurements of a synthetic ssDNA oligonucleotide. The predictive model suggests that prioritizing flow velocity when designing two-detector TDA experiments may offer an efficient route to improved measurement precision.
Bourger et al. (Mon,) studied this question.