The situation changes as 𝑛 increases. The MND model predicts oscillator strength transfers from A 0 to A -, as indicated by thicker lines for the A -transition in Fig. versus Fig. . The oscillator strength shift changes the A 0 :A -intensity ratio and the peak energy shifts complicate the observed spectrum. As the applied potential shifts from positive to negative, the experimentalist observes a "super peak" split into two discernable peaks (see convolved spectrum in Fig. ). A key point of Fig. is that the MND model predicts how and why the "super peak" absorbance feature at low 𝑛 (positive potentials) transitions to two separate peaks at high 𝑛 (negative potentials). Having summarized the general predictions of the model, we can now discuss the general fitting procedure to determine 𝑛 at each 𝐸 for the experimental data in Fig. . Our previous work provides complete MND model calculation details. Briefly, the model quantitatively links the experimental observable (absorbance spectrum) to 𝑛 via the Fermi doping energy parameter 𝜀 F , defined as an energy level position at or above the conduction band minimum. First, we obtain model parameters, the undoped A 0 peak (i.e., energy, width, and height), using data acquired at 𝐸 = +1.00 V and account for phenomenological peak broadening by convolving the simulated peak with a Gaussian. Then, for all other more negative 𝐸 values (i.e., higher 𝜀 F conditions), we make an initial guess at the parameters for both A 0 and A -and perform a 5-step procedure to determine 𝜀 F for every E (see ESI for details). The rationale of the steps is to model the data globally at the level of the MND peak lineshape outputs instead of guessing at the correct Hamiltonian. We can infer an effective doping density from the ratio of peak heights in the experimental absorbance data based on the series of spectra predicted from the MND model.