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Molecular dynamics simulations have unveiled the atomic-scale mechanisms underlying the formation of core-shell nanostructures involving nanowires (NWs) and graphene. Our study identifies two pivotal steps in this process: the self-scrolling of graphene around NWs and the subsequent edge connection to form carbon nanotubes (CNTs). The tendency of graphene to scroll is governed by the complex interplay of van der Waals forces. Stronger interactions between the NW and graphene act as a driving force, significantly promoting the scrolling process. We find that the initial velocity of NWs significantly influences CNT formation, with optimal velocities enabling the creation of defect-free CNTs. Increasing NW velocity enhances the graphene self-scrolling and wrapping dynamics. NW diameter plays a critical role; larger diameters minimize bending deformation, favoring defect-free CNT formation, but also augment transverse wave amplitudes, increasing relative sliding resistance between graphene and NWs and decelerating van der Waals interactions, thereby reducing scrolling speed. We determine that graphene length must meet the condition L = π(+2r) and the width-to-length ratio (W/L) must surpass a threshold for effective NW/CNT core-shell structure formation. Multilayer graphene, experiencing reduced van der Waals adsorption from the substrate, is more prone to self-scrolling. Lower temperatures enhance graphene wrapping around NWs, facilitating edge connection and system stabilization, whereas higher temperatures disrupt core-shell integrity by increasing atomic thermal motion, preventing edge connection. These findings establish a solid theoretical foundation for the design and fabrication of advanced NW/CNT core-shell nanostructures.
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http://dx.doi.org/10.1021/acs.langmuir.4c05237 | DOI Listing |
PLoS One
September 2025
Department of Economics, Cornell University, Ithaca, United States of America.
In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology.
View Article and Find Full Text PDFAppl Biochem Biotechnol
September 2025
School of Biological Sciences, University of the Punjab, Quaid-E-Azam Campus, P.O. 54590, Lahore, Pakistan.
Recombinant DNA technology is widely used to produce industrially and pharmaceutically important proteins. In silico analysis, performed before executing wet lab experiments has been greatly helpful in this connection. A shift in protein analysis has been observed over the past decade, driven by advancements in bioinformatics databases, tools, software, and web servers.
View Article and Find Full Text PDFJ Fish Biol
September 2025
National Oceanic and Atmospheric Administration/NOS/NCCOS/MSE/Biogeography Branch, Silver Spring, Maryland, USA.
Despite snappers' (family Lutjanidae) commercial and ecological significance, knowledge gaps remain regarding life history, ontogeny and ecology across their range in the Caribbean and south Atlantic. There is also a need to explore the efficacy of marine protected areas (MPAs) as a tool for enhancing nursery and spawning habitat conservation for multiple snapper species. Additionally, even as hurricanes and sargassum inundation have become rising issues for coastal communities, there is a scarcity of data on how commercially important species respond to these environmental disturbances.
View Article and Find Full Text PDFJ Comput Soc Sci
September 2025
Chair of Research Methods in Developmental and Educational Sciences, Institute of Education, University of Zurich, Zurich, Switzerland.
School curricula guide the daily learning activities of millions of students. They embody the understanding of the education experts who designed them of how to organize the knowledge that students should acquire in a way that is optimal for learning. This can be viewed as a learning 'theory' which is, nevertheless, rarely put to the test.
View Article and Find Full Text PDFACS Omega
September 2025
School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Identifying side effects is crucial for drug development and postmarket surveillance. Several computational methods based on graph neural networks (GNNs) have been developed, leveraging the topological structure and node attributes in graphs with promising results. However, existing heterogeneous-network-based approaches often fail to fully capture the complex structure and rich semantic information within these networks.
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