Search NASA⌕ Search

DOE OSTI · 1097258

Stretchable and foldable electronic devices

Abstract

Disclosed herein are stretchable, foldable and optionally printable, processes for making devices and devices such as semiconductors, electronic circuits and components thereof that are capable of providing good performance when stretched, compressed, flexed or otherwise deformed. Strain isolation layers provide good strain isolation to functional device layers. Multilayer devices are constructed to position a neutral mechanical surface coincident or proximate to a functional layer having a material that is susceptible to strain-induced failure. Neutral mechanical surfaces are positioned by one or more layers having a property that is spatially inhomogeneous, such as by patterning any of the layers of the multilayer device.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rogers, John A, Huang, Yonggang, Ko, Heung Cho, Stoykovich, Mark, Choi, Won Mook, Song, Jizhou, Ahn, Jong Hyun, Kim, Dae Hyeong. 2013-10-08. Stretchable and foldable electronic devices. https://www.osti.gov/biblio/1097258

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↗