Routine analysis of a sample using NMR or mass spectrometry doesn’t take more than a day at most. EPR spectroscopy rather counts weeks, and the problem is not so much the measurement time but rather the effort needed to analyse and interpret the results that cannot be left to the inexperienced user or collaboration partner.
Spectroscopy is intrinsically slower than other approaches, as it usually involves extensive processing and analysis of the obtained data. However, as usual two forms of complexity can be distinguished: essential and accidental complexity. While essential complexity is due to the method itself and the underlying physics, accidental complexity usually stems from lack of routine protocols and tools.
Recording EPR spectra can be a matter of hours or even a full day, but usually, if one cannot achieve a sensible signal-to-noise ratio within 24 hours, there is no point in trying to do even longer measurements, as the signal-to-noise ratio will only improve with the square root of the number of accumulations.
Data processing and analysis, on the other hand, usually requires long time, mostly again due to the lack of established protocols and automation. Therefore, highly skilled researchers have to perform the same routine tasks over and over again – manually. There is much potential in automating most of the routine processing and analysis tasks, including but not limited to fitting spectral simulations. Key aspects are a modular and user-friendly framework for data processing and analysis and a standardised output format for results that can be readily shared with collaboration partners, but provide an overview of the data and analyses for the EPR spectroscopist as well.