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Progress and Perspectives: Zirconium Electrodeposition from Different Electrolytes

Watkins, Nicholas Bret [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000172519387)·De Silva, Umanga Handi Umanga Iroshini [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000300295868)·Janusz, Jordyn Nevada [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000177884754)·McBride, Michael Anthony [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000252747275)·Mirabal, Alex James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245017602)·Jones, Benjamin Jesse [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000181850311)·Stull, Jamie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000204647456)
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Abstract

Zirconium (Zr) possesses outstanding properties, including exceptional chemical resistance and a high melting point, and is therefore desirable for use in a wide variety of challenging environments, such as in nuclear reactors and the chemical processing industry. To minimize the amount of Zr required for any given application, developing methods for generating metallic Zr coatings is highly advantageous. Owing to Zr’s highly negative reduction potential, electrodeposition in traditional solvents at near ambient temperatures remains challenging and thus not well understood. Due to the extreme conditions required to deposit this metal, extensive work has been conducted in molten salt electrolytes, however the broad applicability of this methodology is limited due to its corrosivity. The primary focus of this article is to present an overview of Zr’s electrochemical behavior and to consolidate the efforts of researchers in exploring electrodeposition techniques for Zr involving aqueous, organic, ionic liquid, deep eutectic, and molten salt solvents. With this information, we highlight trends across solvent systems and opportunities for future research.

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42 ENGINEERINGZirconiumelectrodepositionelectroplatingreview

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Watkins, Nicholas Bret [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000172519387), De Silva, Umanga Handi Umanga Iroshini [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000300295868), Janusz, Jordyn Nevada [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000177884754), McBride, Michael Anthony [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000252747275), Mirabal, Alex James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245017602), Jones, Benjamin Jesse [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000181850311), Stull, Jamie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000204647456). 2026-06-01. Progress and Perspectives: Zirconium Electrodeposition from Different Electrolytes. https://doi.org/10.1149/1945-7111%2Fae6d79

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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.

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