Universal Rate Model Selector: A Method to Quickly Find the Best-Fit Kinetic Rate Model for an Experimental Rate Profile

Often, a kinetic rate equation does not adequately model an entire set of experimental data points. Traditional kinetic rate models are usually forced onto the experimental points. Traditional algorithmic approaches require additional efforts and processes to find a kinetic rate model that provides a high degree of correlation with experimental data. Furthermore, the use of kinetic rate models does not take into consideration that a set of experimental data points may require more than one type of model to fit the entire data set. That is, different chemical and physical mechanisms may occur during an experimental procedure on an analyte. Herein, we constructed a blueprint platform set of graphs that contained eight traditional, widely used kinetic rate model curves as the Universal Rate Model Selector URMS. Normalized experimental data sets that consisted of different temperatures and pH values for an analyte were overlaid directly onto the blueprint platform eight kinetic rate curves. Visual observations showed where the normalized data points most closely associated with a particular rate curves. No fitting or calculations were performed in the fit between experimental data and the URMS. Instead, a visual analysis was conducted.