Abstract

Solution processed organic field effect transistors can become ubiquitous in flexible optoelectronics. While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications. Here, we introduce a strategy to identify the most probable and severe degradation pathways in organic transistors and then implement a method to eliminate the main sources of instabilities. Real time monitoring of the energetic distribution and transformation of electronic trap states during device operation, in conjunction with simulations, revealed the nature of traps responsible for performance degradation. With this information, we designed the most efficient encapsulation strategy for each device type, which resulted in fabrication of high performance, environmentally and operationally stable small molecule and polymeric transistors with consistent mobility and unparalleled threshold voltage shifts as low as 0.1 V under the application of high bias stress in air.

Document Type

Article

Publication Date

4-21-2021

Notes/Citation Information

Published in Nature Communications, v. 12, issue 1, article no. 2352.

© The Author(s) 2021

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

Digital Object Identifier (DOI)

https://doi.org/10.1038/s41467-021-22683-2

Funding Information

The work at Wake Forest University was supported by the National Science Foundation through Grant No. DMR-1627925, while the work at the University of Kentucky was supported through Grant No. DMR-1627428. Computing resources on the Lipscomb High Performance Computing Cluster were provided by the University of Kentucky Information Technology Department and the Center for Computational Sciences (CCS). I.M. acknowledges funding from KAUST Office of Sponsored Research (OSR) under awards no. OSR-2018-CARF/CCF-3079, no. OSR-2015-CRG4-2572 and OSR-4106 CPF2019, as well as EC FP7 Project SC2 (610115), EC H2020 (643791), and EPSRC EP/M005143/1.

Related Content

The data sets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable requests.

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