Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1089405

Nanoengineered membrane electrode assembly interface

Song, Yujiang·Shelnutt, John A
View original source ↗Read source document ↗Export metadata ↓

Abstract

A membrane electrode structure suitable for use in a membrane electrode assembly (MEA) that comprises membrane-affixed metal nanoparticles whose formation is controlled by a photochemical process that controls deposition of the metal nanoparticles using a photocatalyst integrated with a polymer electrolyte membrane, such as an ionomer membrane. Impregnation of the polymer membrane with the photocatalyst prior to metal deposition greatly reduces the required amount of metal precursor in the deposition reaction solution by restricting metal reduction substantially to the formation of metal nanoparticles affixed on or near the surface of the polymer membrane with minimal formation of metallic particles not directly associated with the membrane.

Explore related subjects

42 ENGINEERING77 NANOSCIENCE AND NANOTECHNOLOGY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Song, Yujiang, Shelnutt, John A. 2013-08-06. Nanoengineered membrane electrode assembly interface. https://www.osti.gov/biblio/1089405

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↗