Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2563604

Colloidal phenomena reflect the interplay between interfacial solution structure, interparticle forces, and dynamical response

Heo, Jaeyoung [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Butreddy, Pravalika [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Schenter, Gregory K. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Mundy, Christopher J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Washington, Seattle, WA (United States)]·De Yoreo, James J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Washington, Seattle, WA (United States)]·Nakouzi, Elias [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Lee, Jaewon [University of Missouri, Columbia, MO (United States)]·Chun, Jaehun [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); City University of New York (CUNY), NY (United States)] (ORCID:0000000222916496)
View original source ↗Read source document ↗Export metadata ↓

Abstract

The physical and chemical properties of colloidal systems are central to both natural and synthetic processes that enable a wide range of energy and environmental applications. These properties are an emergent outcome of the structure that colloidal systems adopt in response to the interplay of electrical, van der Waals, hydration, and steric forces coupled to the inherent fluctuations of the system. Here, in this study, we review the nature of those forces and show how variations in their relative importance lead to distinct free energy landscapes and consequent dynamic behaviors, illustrated for colloidal dispersions, nanocrystal superlattices, and mesocrystlline solids formed through oriented attachment. We end by discussing the challenges to future progress arising from the need to seamless couple from molecular to particle to ensemble scales.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Heo, Jaeyoung [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Butreddy, Pravalika [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Schenter, Gregory K. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Mundy, Christopher J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Washington, Seattle, WA (United States)], De Yoreo, James J. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); University of Washington, Seattle, WA (United States)], Nakouzi, Elias [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Lee, Jaewon [University of Missouri, Columbia, MO (United States)], Chun, Jaehun [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); City University of New York (CUNY), NY (United States)] (ORCID:0000000222916496). 2024-12-06. Colloidal phenomena reflect the interplay between interfacial solution structure, interparticle forces, and dynamical response. https://doi.org/10.1016/j.cocis.2024.101887

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↗