Why would you need an EPR spectroscopist?

Electron paramagnetic resonance (EPR) spectroscopy is a powerful method to help unravel reaction mechanisms involving paramagnetic states such as radicals or transition metal ions. Other applications involve (light-induced) electron transfer in biological systems and materials as well as insight into the structure–function relationship of organic semiconductors. Here, we demonstrate the power of conventional continuous-wave (cw) EPR spectroscopy at moderate magnetic fields and frequencies (X-band) [1], with a special focus on time-resolved (tr-EPR) investigation of short-lived, light-induced paramagnetic states. In a series of case studies, we show its application to biological electron transfer [2] and triplet states in organic semiconductors [3]. Particularly interesting are effects of excitation wavelength on the paramagnetic states created [4], in analogy to precision photochemistry in photocatalysis [5]. cw-EPR spectroscopy is particularly suited, but by far not limited, to investigate doping mechanisms in organic semiconductors, including orientation between dopant and host polymer [6]. Further plans for methods development include a 3D tr-EPR experiment with the wavelength as third variable (besides time and magnetic field) and largely automated in-situ spectro-electrochemistry within a cw-EPR spectrometer, allowing for routine measurements of larger amounts of samples. To make a genuine contribution to science, we not only require long-standing experience, but we need to develop and competently use the necessary (nowadays digital) tools as well. To this end, we have developed a format to collect all relevant metadata during data acquisition [7], an electronic lab notebook (ELN) [8], a framework for data analysis providing a gap-less record from the raw data to the final publication [9] together with dedicated packages for cw-EPR [10] and tr-EPR [11] data, and a larger digital laboratory infrastructure [12] including a repository for “warm” research data. The last step is to educate students and scientists how to develop software [13] and handle research data according to scientific standards [14]. “At stake is the future of scholarship.”[15] References[1] EPR spectroscopy for the 21st century, https://www.epr-21.de/[2] T. Biskup, Time-resolved EPR of radical pair intermediates in cryptochromes, Mol. Phys. 2013, 111:3698–3703[3] T. Biskup, Structure–function relationship of organic semiconductors: Detailed insights from time-resolved EPRspectroscopy, Front. Chem. 2019, 7:10[4] D. L. Meyer, F. Lombeck, S. Huettner, M. Sommer, T. Biskup, Direct S0→T excitation of a conjugated polymerrepeat unit: unusual spin-forbidden transitions probed by time-resolved electron paramagnetic resonancespectroscopy, J. Phys. Chem. Lett. 2017, 8:1677–1682[5] J. P. Menzel, B. B. Noble, J. P. Blinco, C. Barner-Kowollik, Predicting wavelength-dependent photochemicalreactivity and selectivity, Nat. Comm. 2021, 12:1691[6] T. Biskup, Doping of organic semiconductors: Insights from EPR spectroscopy, Appl. Phys. Lett. 2021, 119:010503[7] B. Paulus, T. Biskup, Towards more reproducible and FAIRer research data: documenting provenance during dataacquisition using the Infofile format, Digit. Discov. 2023, 2:234[8] M. Schröder, T. Biskup, LabInform ELN: A lightweight and flexible electronic laboratory notebook for academicresearch based on the open-source software DokuWiki, ChemRxiv 2023, doi:10.26434/chemrxiv-2023-2tvct[9] J. Popp, T. Biskup, ASpecD: A modular framework for the analysis of spectroscopic data focussing on reproducibilityand good scientific practice, Chem. Meth. 2022, 2:e202100097[10] M. Schröder, T. Biskup, cwepr – A Python package for analysing cw-EPR data focussing on reproducibility andsimple usage, J. Magn. Reson. 2022, 335:107140[11] J. Popp, M. Schröder, T. Biskup, trepr Python package, Zenodo 2021, doi:10.5281/zenodo.4897112[12] T. Biskup, LabInform: A modular laboratory information system built from open source components, ChemRxiv2022, doi:10.26434/chemrxiv-2022-vz360[13] Vorlesung “Wissenschaftliche Softwareentwicklung”, https://www.till-biskup.de/de/lehre/softwareentwicklung/[14] Vorlesung “Forschungsdatenmanagement”, https://www.till-biskup.de/de/lehre/forschungsdatenmanagement/[15] C. L. Borgman, Big Data, Little Data, No Data. MIT Press, Cambridge MA 2015.