EPR method development

As can be seen from the above list of applications of EPR spectroscopy to address fundamental questions of organic semiconductors, there are two main types of EPR spectroscopy currently employed by the author:

  • cw-EPR spectroscopy (mostly at room temperature) for characterising doped organic semiconductors and investigating doping mechanisms, including preferential orientation of molecules and relative orientation of host and dopant

  • TREPR spectroscopy of triplet-states at 80 K for investigating the electronic structure and morphology of organic semiconductors by means of their light-induced triplet states.

While both have been quite successfully applied to the questions at hand, there is much room for improvement in order to make EPR spectroscopy a routinely employed analysis tool, mainly by automating both, data acquisition and analysis. Automating not only saves time, but greatly enhances robustness, reliability and accuracy of the results.

An incomplete list of (possible) applications from own experience and out of own interest

Routine cw-EPR spectroscopy for characterising samples at room temperature

  • automatic power sweep and modulation-amplitude sweep

  • including adaptive SNR and number of scans

  • reliable quantitative EPR

Goniometer sweeps in cw-EPR spectroscopy at room temperature

  • including automatic sanity check of sample position (0/180°)

  • plotting results, including fits of shifting positions

  • including automatic fitting of orientation-dependent spectra globally

3D TREPR spectroscopy with wavelength scan

  • automatic correction for number of photons

  • automatic fitting of triplet spectra

In-situ electrochemistry

  • fully automated recording of spectra for crucial points in the voltammogram

  • plotting results as reports, including voltammograms and EPR spectra

Robust and reliable fitting of simulations to EPR spectra

  • advanced fitting concepts (global fitting, sampling of starting conditions)

  • advanced preprocessing (denoising via wavelets and others)

  • disentangling multiple overlapping species

  • accounting for preferential orientation

  • applying machine-learning concepts to extracting parameters from EPR spectra