The Large Particle 3D Concrete Printing (LP3DCP) process presented in this paper is based on the particle bed 3D printing method; here, the integration of significantly larger particles (up to 36 mm) for selective binding using the shotcrete technique is presented. In the LP3DCP process, the integration of large particles, i.e., naturally coarse, crushed or recycled aggregates, reduces the cement volume fraction by more than 50% compared to structures conventionally printed with mortar. Hence, with LP3DCP, the global warming potential, the acidification potential and the total non-renewable primary energy of 3D printed structures can be reduced by approximately 30%. Additionally, the increased proportion of aggregates enables higher compressive strengths than without the coarse aggregates, ranging up to 65 MPa. This article presents fundamental material investigations on particle packing and matrix penetration as well as compressive strength tests and geometry studies. The results of this systematic investigation are presented, and the best set is applied to produce a large-scale demonstrator of one cubic meter of size and complex geometry. Moreover, the demonstrator features reinforcement and subtractive surface processing strategies. Further improvements of the LP3DCP technology as well as construction applications and architectural design potentials are discussed thereafter.
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http://dx.doi.org/10.3390/ma14206125 | DOI Listing |
Adv Sci (Weinh)
January 2025
School of Science and Engineering, University of Dundee, Nethergate, Dundee, DD1 4HN, UK.
Ferrites are an essential material in modern industry due to their exceptional magnetic properties and high resistivity. Many applications of ferrites necessitate exposure to high energy electrons, particularly space science and particle accelerators, where charging, multipacting, and electron clouds (ECs) are major issues. ECs are of particular concern around the Ni/Zn soft ferrite kicker magnets as the large hadron collider (LHC) undergoes its high luminosity upgrade.
View Article and Find Full Text PDFSimulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFMikrochim Acta
January 2025
School of Chemical Engineering and Technology, Hebei Key Laboratory of Functional Polymers, Hebei University of Technology, Beichen District, Xiping Road No. 5340, Tianjin, 300401, China.
A kind of sulfur-doped carbon dots was prepared which were encapsulated with polydopamine (S-CDs@PDA) that has fluorescence response on polyethylene (PE) microplastics (MPs). Modified membranes were constructed using S-CDs@PDA for MP detection. Through heating and vacuum filtration process, yellow emission from the modified membrane appeared because of the combination between S-CDs@PDA and PE MPs.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States.
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI.
View Article and Find Full Text PDFIUCrJ
March 2025
RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, USA.
2D template matching (2DTM) can be used to detect molecules and their assemblies in cellular cryo-EM images with high positional and orientational accuracy. While 2DTM successfully detects spherical targets such as large ribosomal subunits, challenges remain in detecting smaller and more aspherical targets in various environments. In this work, a novel 2DTM metric, referred to as the 2DTM p-value, is developed to extend the 2DTM framework to more complex applications.
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