Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1797902

High performance Nb/TiNi nanocomposites produced by packaged accumulative roll bonding

Jiang, Daqiang·Song, Jian·Yang, Hong·Liu, Yinong·Jiang, Xiaohua·Ren, Yang·Yu, Kaiyuan·Cui, Lishan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Taking full advantage of intrinsic high strength of nano-reinforcements has been proven difficult in a composite until the concept of strain matching was experimental verified in a nanowire Nb/TiNi in-situ composite, which was obtained via solidifying eutectic Nb/TiNi and subsequent severe wire drawing. However, the volume fraction of the nano-reinforcement was dictated by eutectic reaction, which severely limited the development of high-performance composites. Here, the martensitic transformation nanocomposites were successfully acquired with increased volume fraction and changeable morphology of the nano-reinforcement through hot packaged ARB (PARB) and subsequent wire drawing. The nano-reinforcement with lamellar configuration in the composite could exhibit a large lattice strain of similar to 3.8%, which demonstrated the applicability and effectiveness of the strain matching principle in nano-lamellar composite. The obtained composites show a yield strength over 2.0 GPa and a quasi-linear elastic strain as larger as similar to 6.5% with adjustable apparent elastic modulus from 30 to 50 GPa.

Explore related subjects

42 ENGINEERINGMartensitic transformationNanocompositePackageD accumulative roll bondingShape memory alloyStrain matching

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiang, Daqiang, Song, Jian, Yang, Hong, Liu, Yinong, Jiang, Xiaohua, Ren, Yang, Yu, Kaiyuan, Cui, Lishan. 2020-09-05. High performance Nb/TiNi nanocomposites produced by packaged accumulative roll bonding. https://doi.org/10.1016/j.compositesb.2020.108403

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↗