Search NASA⌕ Search

DOE OSTI · 1810776

Light extraction in tandem organic light emitting diodes

Abstract

Since the invention of organic light emitting diodes (OLEDs), great research efforts have been dedicated to improving their efficiency and lifetime. For high-brightness applications, tandem OLED structures have advantages because of the lower current densities required to achieve high brightness. With the successful development of highly efficient charge generation layers, high brightness tandem OLEDs are used in displays and lighting. However, the major challenge for tandem OLEDs is the low light extraction efficiency, because about 50% of the light is trapped inside the device as waveguide modes. In this Perspective, we first review the recent works done on light extraction, analyze different waveguide mode extraction structures, and then identify the key factors determining the extraction efficiencies in tandem OLEDs.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fu, Xiangyu, Yin, Shichen, Chen, Yi-An, Zhu, Liping, Dong, Qi, Chang, Chih-Hao, So, Franky. 2021-08-11. Light extraction in tandem organic light emitting diodes. https://doi.org/10.1063/5.0057325

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗