Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1078270

Staged membrane oxidation reactor system

Repasky, John Michael·Carolan, Michael Francis·Stein, VanEric Edward·Chen, Christopher Ming-Poh
View original source ↗Read source document ↗Export metadata ↓

Abstract

Ion transport membrane oxidation system comprising (a) two or more membrane oxidation stages, each stage comprising a reactant zone, an oxidant zone, one or more ion transport membranes separating the reactant zone from the oxidant zone, a reactant gas inlet region, a reactant gas outlet region, an oxidant gas inlet region, and an oxidant gas outlet region; (b) an interstage reactant gas flow path disposed between each pair of membrane oxidation stages and adapted to place the reactant gas outlet region of a first stage of the pair in flow communication with the reactant gas inlet region of a second stage of the pair; and (c) one or more reactant interstage feed gas lines, each line being in flow communication with any interstage reactant gas flow path or with the reactant zone of any membrane oxidation stage receiving interstage reactant gas.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Repasky, John Michael, Carolan, Michael Francis, Stein, VanEric Edward, Chen, Christopher Ming-Poh. 2012-09-11. Staged membrane oxidation reactor system. https://www.osti.gov/biblio/1078270

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↗