Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1156820

Constant pressure high throughput membrane permeation testing system

Albenze, Erik J.·Hopkinson, David P.·Luebke, David R.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The disclosure relates to a membrane testing system for individual evaluation of a plurality of planar membranes subjected to a feed gas on one side and a sweep gas on a second side. The membrane testing system provides a pressurized flow of a feed and sweep gas to each membrane testing cell in a plurality of membrane testing cells while a stream of retentate gas from each membrane testing cell is ported by a retentate multiport valve for sampling or venting, and a stream of permeate gas from each membrane testing cell is ported by a permeate multiport valve for sampling or venting. Back pressure regulators and mass flow controllers act to maintain substantially equivalent gas pressures and flow rates on each side of the planar membrane throughout a sampling cycle. A digital controller may be utilized to position the retentate and permeate multiport valves cyclically, allowing for gas sampling of different membrane cells over an extended period of time.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Albenze, Erik J., Hopkinson, David P., Luebke, David R.. 2014-09-02. Constant pressure high throughput membrane permeation testing system. https://www.osti.gov/biblio/1156820

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↗