This work presents a comprehensive model for nanobeams, incorporating beam curvature and surface energy. Gurtin-Murdoch surface stress theory is used, in conjunction with Euler-Bernoulli beam theory, to model the beams and take surface energy effects into consideration. The model was validated by contrasting its outcomes with experimental data published in the literature on the static bending of fixed-fixed and fixed-free nanobeams. The outcomes demonstrated that surface stress alters the stiffness of both fixed-fixed and fixed-free nanobeams with different behaviors in each case.
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http://dx.doi.org/10.3390/mi14030663 | DOI Listing |
ACS Nano
January 2025
Battery and Electrochemistry Laboratory (BELLA), Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe 76131, Germany.
Improving interfacial stability between cathode active material (CAM) and solid electrolyte (SE) is vital for developing high-performance all-solid-state batteries (ASSBs), with compatibility issues among the cell components representing a major challenge. CAM surface coating with a chemically inert ion conductor is a promising approach to suppress side reactions occurring at the cathode interfaces. Another strategy to mitigate mechanical degradation involves utilizing single-crystalline particle morphologies.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemical and Biomolecular Engineering, Lehigh University, 124 E. Morton Street, Bethlehem, Pennsylvania 18015, United States.
Quantum dot (QD) light-emitting diodes (QLEDs) are promising candidates for next-generation displays because of their high efficiency, brightness, broad color gamut, and solution-processability. Large-scale solution-processing of electroluminescent QLEDs poses significant challenges, particularly concerning the precise control of the active layer's thickness and uniformity. These obstacles directly impact charge transport, leading to current leakage and reduced overall efficiency.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States.
The kinetics of electronically inelastic quenching of O(Δ) and O(Σ) by collisions with O(P) have been investigated using mixed quantum-classical trajectories governed by adiabatic potential energy surfaces and state couplings generated from a recently developed diabatic potential energy matrix (DPEM) for the 14 lowest-energy A' states of O. Using the coherent switching with decay of mixing (CSDM) method, dynamics calculations were performed both with 14 coupled electronic states and with 8 coupled electronical states, and similar results were obtained. The calculated thermal quenching rate coefficients are generally small, but they increase with temperature.
View Article and Find Full Text PDFInorg Chem
January 2025
Beijing Spacecrafts Manufacturing Factory, Beijing 100094, P. R. China.
The rapid upsurge of metal-organic frameworks (MOFs) has sparked profound interest in their potential as proton conductors for proton exchange membrane fuel cells. However, proton-conducting behaviors of hydrophobic MOFs remain poorly understood compared with their hydrophilic counterparts, largely due to the absence of a microscopic phase separation structure akin to that found in Nafion membranes. Herein, we demonstrate a strategy for regulating the structures and proton conductivities of MOFs by separately incorporating hydrophobic -C(CF)- group alongside hydrophilic -O- and -SO- groups into organic ligands as linkers.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Xi'an Jiaotong University, School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials, Xi'an 710049, China.
The bismuth monolayer has recently been experimentally identified as a novel platform for the investigation of two-dimensional single-element ferroelectric system. Here, we model the potential energy surface of a bismuth monolayer by employing a message-passing neural network and achieve an error smaller than 1.2 meV per atom.
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