Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1157076

Optoelectronic device

Bonekamp, Jeffrey E.·Boven, Michelle L.·Gaston, Ryan S.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The invention is an optoelectronic device comprising an active portion which converts light to electricity or converts electricity to light, the active portion having a front side for the transmittal of the light and a back side opposite from the front side, at least two electrical leads to the active portion to convey electricity to or from the active portion, an enclosure surrounding the active portion and through which the at least two electrical leads pass wherein the hermetically sealed enclosure comprises at the front side of the active portion a barrier material which allows for transmittal of light, one or more getter materials disposed so as to not impede the transmission of light to or from the active portion, and a contiguous gap pathway to the getter material which pathway is disposed between the active portion and the barrier material.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bonekamp, Jeffrey E., Boven, Michelle L., Gaston, Ryan S.. 2014-09-09. Optoelectronic device. https://www.osti.gov/biblio/1157076

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗