ESEEM Calculation

The time-domain ESEEM calculation incorporates the full density matrix diagonalization from the Mims density matrix treatment of ESEEM. Although the time evolution simulation of a pulsed magnetic resonance experiment is straightforward, we found that the explicit evaluation of the transition frequencies and intensities is computationally advantageous for optimization purposes, relative to methods that calculate the time evolution of the density matrix.  Specifically, the following adopted numerical procedure, which we have used previously in ESEEM simulations, is found to be most efficient for constructing the two-pulse and three-pulse modulation in OPTESIM.

The Hamiltonian is first partitioned into sub-matrix representations Hα and Hβ corresponding to α (s=1/2) and β (ms=-1/2) electron spin manifolds.  Hα and Hβ are diagonalized separately with unitary matrices Mα and Mβ to obtain the eigen frequencies να and νβ belonging to the α and β manifolds, respectively. The Hamiltonian, H, is transformed into H’, which  corresponds to the coupled representation, as follows:

image

where image

The modulation frequencies and amplitudes are then calculated for specific types of experiments.  For example, for the two-pulse echo envelope modulation (pulse sequence, Figure 1), the frequencies imageand amplitudes A are as follows:

image

image

The indices i, m, j, and k allow permutation through all elements of M and the eigenvalues.

The echo modulation amplitude image is formulated as follows:

image

For the three-pulse echo modulation (pulse sequence, Figure 1), the frequencies imageand amplitudes A are separated into terms representing the α and β manifolds, as follows:

image),

image),

image

image

The simulated echo modulation amplitude image is formulated as follows :

image

For flexibility, users can easily substitute their own time-domain ESEEM calculations into OPTESIM as Matlab functions.  Therefore, experimental systems and pulse sequences other than those considered here can be addressed by using the optimization and statistical assessment  features of OPTESIM.