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DOE OSTI · 2585507

Optimizing Adhesion: Evaluating B-Stage Epoxy for Bonding [Poster]

Abstract

The application of increased pressure during the curing process of B-stage epoxy has been hypothesized to enhance the adhesive properties and bond strength in printed wiring board (PWB) assembly. This study aimed to investigate the correlation between pressure application and the performance of B-stage epoxy.

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BibTeXRIS

Perez, Joseph Evan [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0009000137855470), Nash, Jordan Paige [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0009000947421017), Hill, Preston J. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)], Hagen, Deborah Ann [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000168663327), Ha, Quang (Alvin) Xuan [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)]. 2025-07-01. Optimizing Adhesion: Evaluating B-Stage Epoxy for Bonding [Poster]. https://doi.org/10.2172/2585507

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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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