Search NASA⌕ Search

DOE OSTI · 1369052

Wrapped optoelectronic devices and methods for making same

Abstract

In various embodiments, optoelectronic devices are described herein. The optoelectronic device may include an optoelectronic cell arranged so as to wrap around a central axis wherein the cell includes a first conductive layer, a semi-conductive layer disposed over and in electrical communication with the first conductive layer, and a second conductive layer disposed over and in electrical communication with the semi-conductive layer. In various embodiments, methods for making optoelectronic devices are described herein. The methods may include forming an optoelectronic cell while flat and wrapping the optoelectronic cell around a central axis. The optoelectronic devices may be photovoltaic devices. Alternatively, the optoelectronic devices may be organic light emitting diodes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Curran, Seamus, Dias, Sampath, Alley, Nigel, Haldar, Amrita, Yambem, Soniya Devi, Kang-Shyang, Liao, Chaudhari, Prajakta. 2017-07-11. Wrapped optoelectronic devices and methods for making same. https://www.osti.gov/biblio/1369052

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↗