While the linear model sufficiently describes the sulfur sol data, the CdS data require the COP model to explain the p ∞ -Q m relationship. Regardless of how the fit is performed, the linear model fails to predict numerous data points (see figure ). The fit in logarithmic space (figures 4b and 5) aligns better where the reaction rate is smaller, which is the limit in which I would expect this linear approximation to perform well, whereas fitting without logarithms does not match any one region of the relationship well (figure ). Additionally, the approximate error bars reported by Owen et al. (in their figure ) appear to increase with the magnitude of the data, which would imply that the uncertainties on the data are more uniform in logarithmic space. For these reasons, I consider the fit in logarithmic space more accurate and will only further discuss the results of this method. The relationship within the data is visibly nonlinear, and an extremely low p-value (< 10 -10 ) in the F-test confirms that the COP model provides a significant improvement over the linear model. Fitting the COP model in the logarithmic space for the CdS data demonstrates that it largely captures the physics of LaMer burst nucleation that are absent in the linear model. The graphical interpretation of the COP model in a log-log plot sheds light on the source of uncertainty in the parameters of interest. While the linear and COP models appear to approach the same limit at first glance (figure ), the log-log plot shows that they are distinct (figure ). The smallest data points do not follow a line with slope 1 or 1/2, which creates uncertainty in where the approximation of line A starts to be valid. For the CdS data, the p∞ -intercepts of lines A and B lie to the right of the data (unlike in figure ), and so uncertainty in the position of line A along line B means that the intercept could be very high (thus G m could be very low), which is illustrated by the confidence regions in figure Caution is advised regarding the precise results of the COP model for the CdS data, which serves primarily to provide a useful illustration of the COP model and its graphical interpretation. I are unable to confirm the validity of the COP model here since C m is not reported. This means that I are not sure if C m remains constant over the larger range of data that Owen et al. provide (3 orders of magnitude in reaction rates compared to < 1 order of magnitude where C m was confirmed as a constant ). Additionally, I can place a maximum bound on the critical nucleus size but cannot compare this to the fitted parameters without C m . The smallest observed CdS nanocrystal must be larger than n c,m , so I may use this and the density of CdS nanocrystals to obtain n c,m < 110. Unfortunately, a maximum bound on n c,m is not a maximum bound on n c,m C p /C m since because C p /C m < 1 (i.e. the maximum concentration of solute (C m ) cannot exceed the initial concentration of precursor (C p ) because each mole of precursor forms one mole of solute). It is noteworthy that the COP model only requires C m , not the solubility of CdS in solution, which C m should be relatively simple to obtain from differences between measurements of reacted precursor and precipitated CdS. I thus recommend measuring/estimating the maximum solute concentration when using the COP model so that results may be better interpreted.