Search NASA⌕ Search

DOE OSTI · 1771509

Broad band tunable energy electron beam pulser

Abstract

An electromagnetic mechanical pulser implements a transverse wave metallic comb stripline TWMCS kicker having inwardly opposing teeth that retards a phase velocity of an RF traveling wave to match the kinetic velocity of a continuous electron beam, causing the beam to oscillate before being chopped into pulses by an aperture. The RF phase velocity is substantially independent of RF frequency and amplitude, thereby enabling independent tuning of the electron pulse widths and repetition rate. The TWMCS further comprises an electron pulse picker (EPP) that applies a pulsed transverse electric field across the TWMCS to deflect electrons out of the beam, allowing only selected electrons and/or groups of electrons to pass through. The EPP pulses can be synchronized with the RF traveling wave and/or with a pumping trigger of a transverse electron microscope (TEM), for example to obtain dynamic TEM images in real time.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jing, Chunguang, Qiu, Jiaqi, Liu, Ao, Montgomery, Eric John, Zhao, Yubin, Rush, Wade, Kostin, Roman, Kanareykin, Alexei. 2020-10-13. Broad band tunable energy electron beam pulser. https://www.osti.gov/biblio/1771509

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

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↗