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DOE OSTI · 1531753

Device containing non-blinking quantum dots

Kahen, Keith B.·Ren, Xiaofan
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Abstract

An optoelectronic device including two spaced apart electrodes; and at least one layer containing ternary core/shell nanocrystals disposed between the spaced electrodes and having ternary semiconductor cores containing a gradient in alloy composition and wherein the ternary core/shell nanocrystals exhibit single molecule non-blinking behavior characterized by on times greater than one minute or radiative lifetimes less than 10 ns.

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42 ENGINEERING75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY77 NANOSCIENCE AND NANOTECHNOLOGY

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BibTeXRIS

Kahen, Keith B., Ren, Xiaofan. 2012-08-14. Device containing non-blinking quantum dots. https://www.osti.gov/biblio/1531753

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DOE OSTI2026

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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.

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