Many materials have a network of fibers as their main structural component and are referred to as network materials. Their strength and toughness are important in both engineering and biology. In this work we consider stochastic model fiber networks without pre-existing cracks and study their rupture mechanism. These materials soften as the crosslinks or fibers fail and exhibit either brittle failure immediately after the peak stress, or a more gradual, ductile rupture in the post peak regime. We observe that ductile failure takes place at constant energy release rate defined in the absence of pre-existing cracks as the strain derivative of the specific energy released. The network parameters controlling the energy release rate are identified and discussed in relation to the Lake-Thomas theory which applies to crack growth situations. We also observe a ductile to brittle failure transition as the network becomes more affine and relate the embrittlement to the reduction of mechanical heterogeneity of the network. Further, we confirm previous reports that the network strength scales linearly with the bond strength and with the crosslink density. The present results extend the Lake-Thomas theory to networks without pre-existing cracks which fail by the gradual accumulation of distributed damage and contribute to the development of a physical picture of failure in stochastic network materials.
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http://dx.doi.org/10.1016/j.jmps.2022.105176 | DOI Listing |
J Am Chem Soc
January 2025
Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
Li-rich cation-disordered rocksalt (DRX) materials introduce new paradigms in the design of high-capacity Li-ion battery cathode materials. However, DRX materials show strikingly sluggish kinetics due to random Li percolation with poor rate performance. Here, we demonstrate that Li stuffing into the tetrahedral sites of the Mn-based rocksalt skeleton injects a novel tetrahedron-octahedron-tetrahedron diffusion path, which acts as a low-energy-barrier hub to facilitate high-speed Li transport.
View Article and Find Full Text PDFAcc Chem Res
January 2025
Department of Chemistry, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel.
ConspectusA key challenge in modern chemistry research is to mimic life-like functions using simple molecular networks and the integration of such networks into the first functional artificial cell. Central to this endeavor is the development of signaling elements that can regulate the cell function in time and space by producing entities of code with specific information to induce downstream activity. Such artificial signaling motifs can emerge in nonequilibrium systems, exhibiting complex dynamic behavior like bistability, multistability, oscillations, and chaos.
View Article and Find Full Text PDFPurpose: Predicting long-term anatomical responses in neovascular age-related macular degeneration (nAMD) patients is critical for patient-specific management. This study validates a generative deep learning (DL) model to predict 12-month posttreatment optical coherence tomography (OCT) images and evaluates the impact of incorporating clinical data on predictive performance.
Methods: A total of 533 eyes from 513 treatment-naïve nAMD patients were analyzed.
Sci Adv
January 2025
Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.
Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in and mutants, where lysosomes accumulate cholesterol.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Key Laboratory of Low Carbon Energy and Chemical Engineering of Gansu Province, Lanzhou 730050, China.
Na superionic conductor (NASICON)-structure NaMnV(PO) (NVMP) electrode materials reveal highly attractive application prospects due to ultrahigh energy density originating from two-electron reactions. Nevertheless, NVMP also encounters challenges with its poor electronic conductivity, Mn dissolution, and Jahn-Teller distortion. To address this issue, utilizing N-doped carbon layers and carbon nanotubes (CNTs) for dual encapsulation enhances the material's electronic conductivity, creating an effective electron transport network that promotes the rapid diffusion and storage of Na.
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