Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1331312

Gas sensor protection device and method

Boyd, David·Magera, Craig
View original source ↗Read source document ↗Export metadata ↓

Abstract

A gas sensor includes a sensor housing and a sensing element located within the sensor housing. The sensing element has a distal end and defines an axis. The gas sensor also includes a sensor protection device coupled to the sensor housing and at least partially surrounding the distal end of the sensing element. The sensor protection device includes a first member coupled to the housing, the first member having a generally rectangular cross-sectional shape in a plane perpendicular to the axis. The first member includes a gas inlet and a gas outlet. The sensor protection device also includes a second member coupled to the housing.

Explore related subjects

42 ENGINEERING47 OTHER INSTRUMENTATION

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Boyd, David, Magera, Craig. 2016-11-08. Gas sensor protection device and method. https://www.osti.gov/biblio/1331312

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↗