Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1082864

Efficient reversible electrodes for solid oxide electrolyzer cells

Elangovan, S.·Hartvigsen, Joseph J.·Zhao, Feng
View original source ↗Read source document ↗Export metadata ↓

Abstract

An electrolyzer cell is disclosed which includes a cathode to reduce an oxygen-containing molecule, such as H2O, CO.sub.2, or a combination thereof, to produce an oxygen ion and a fuel molecule, such as H.sub.2, CO, or a combination thereof. An electrolyte is coupled to the cathode to transport the oxygen ion to an anode. The anode is coupled to the electrolyte to receive the oxygen ion and produce oxygen gas therewith. In one embodiment, the anode may be fabricated to include an electron-conducting phase having a perovskite crystalline structure or structure similar thereto. This perovskite may have a chemical formula of substantially (Pr(.sub.1-x)La.sub.x)(z-y)A&#x27;.sub.yBO(3-.differential.), wherein 0<x<1, 0.ltoreq.y.ltoreq.0.5, and 0.8.ltoreq.z.ltoreq.1.1. In another embodiment, the cathode includes an electron-conducting phase that contains nickel oxide intermixed with magnesium oxide.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Elangovan, S., Hartvigsen, Joseph J., Zhao, Feng. 2013-01-15. Efficient reversible electrodes for solid oxide electrolyzer cells. https://www.osti.gov/biblio/1082864

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↗