Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1495040

Separating components of mixed fluid using a functionally graded material

Pascall, Andrew J.·Farquar, George R.·Spadaccini, Christopher M.·Chinn, Sarah C.
View original source ↗Read source document ↗Export metadata ↓

Abstract

A system for separating components of a fluid containing at least a first component and a second component includes a device having an inlet for introducing the fluid into the device, a first outlet for directing the first component of the fluid from the device, and a second outlet for directing the second component of the fluid from the device. A material that has a gradient in properties is located in the device between the inlet and the first and second outlets. The material has a first portion with an affinity for the first fluid component and a second portion with an affinity for the second fluid component. The first portion is positioned with relation to the first outlet such that the first component is directed from said device through the first outlet. The second portion is positioned with relation to the second outlet such that the second component is directed from the device through the second outlet.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pascall, Andrew J., Farquar, George R., Spadaccini, Christopher M., Chinn, Sarah C.. 2018-11-20. Separating components of mixed fluid using a functionally graded material. https://www.osti.gov/biblio/1495040

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↗