Effects of Processing-Induced Contamination on Organic Electronic Devices

Dimitrios Simatos, Ian Jacobs, Illia Dobryden, Małgorzata Nguyen, Achilleas Savva, Deepak Venkateshvaran, Mark Nikolka, Jérôme Charmet, Leszek J. Spalek, Mindaugas Gicevičius, Youcheng Zhang, Guillaume Schweicher, Duncan J. Howe, Sarah Ursel, John Armitage, Ivan B. Dimov, Ulrike Kraft, Weimin Zhang, Maryam Alsufyani, Iain McCullochR. M. Owens, Per M. Claesson, Tuomas P. J. Knowles, Henning Sirringhaus

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Organic semiconductors are a family of pi-conjugated compounds used in many applications, such as displays, bioelectronics, and thermoelectrics. However, their susceptibility to processing-induced contamination is not well understood. Here, it is shown that many organic electronic devices reported so far may have been unintentionally contaminated, thus affecting their performance, water uptake, and thin film properties. Nuclear magnetic resonance spectroscopy is used to detect and quantify contaminants originating from the glovebox atmosphere and common laboratory consumables used during device fabrication. Importantly, this in-depth understanding of the sources of contamination allows the establishment of clean fabrication protocols, and the fabrication of organic field effect transistors (OFETs) with improved performance and stability. This study highlights the role of unintentional contaminants in organic electronic devices, and demonstrates that certain stringent processing conditions need to be met to avoid scientific misinterpretation, ensure device reproducibility, and facilitate performance stability. The experimental procedures and conditions used herein are typical of those used by many groups in the field of solution-processed organic semiconductors. Therefore, the insights gained into the effects of contamination are likely to be broadly applicable to studies, not just of OFETs, but also of other devices based on these materials.
Original languageEnglish (US)
JournalSmall Methods
StatePublished - Sep 3 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-07
Acknowledgements: The authors acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC) (EP/R031894/1, EP/R032025/1, EP/W017091/1). D.S. acknowledges support from the EPSRC Centre for Doctoral Training (CDT) in Sensor Technologies and Applications (EP/L015889/1). I.E.J. acknowledges funding from a Royal Society Newton International Fellowship. A.S. and R.M.O. acknowledge funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant, MultiStem (No. 895801). D.V. acknowledges the Royal Society for funding in the form of a Royal Society University Research Fellowship (Royal Society Reference No. URF\R1\201590). M.G. and H.S. acknowledge funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement, UHMob (No 811284). G.S. thanks the Belgian National Fund for Scientific Research (FNRS) for financial support through research project COHERENCE2 No. F.4536.23. G.S. is an FNRS Research Associate. G.S. acknowledges financial support from the Francqui Foundation (Francqui Start-Up Grant). I.B.D. acknowledges support from the EPSRC Cambridge NanoDTC (EP/L015978/1). H.S. acknowledges funding from a Royal Society Research Professorship (RP\R1\201082). The authors acknowledge support from the Henry Royce Institute facilities grant (EP/P024947/1), as well as the Sir Henry Royce Institute recurrent grant (EP/R00661X/1) for the use of the ambient cluster tool and the EQCM.


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