The threshold structural transformation of the DUT-4 metal-organic framework (MOF) from an ordered to distorted phase during exposure to ambient conditions has been revealed. The X-ray diffraction analysis, Raman and FTIR spectroscopy, scanning electron microscopy and synchronous thermal analysis have been used for investigation. The reversible effect of exposure time and humidity on such a phase transition has been confirmed. We also demonstrated that the observed phase transition correlated well with changes in the optical and electronic properties of DUT-4, paving the way to a new family of MOF-based phase change materials for optoelectronic applications.
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http://dx.doi.org/10.1039/d4dt00038b | DOI Listing |
Nano Lett
January 2025
Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027 Zhejiang, China.
Chiral vortices and their phase transition in ferroelectric/dielectric heterostructures have drawn significant attention in the field of condensed matter. However, the dynamical origin of the chiral phase transition from achiral to chiral polar vortices has remained elusive. Here, we develop a phase-field perturbation model and discover the softening of out-of-plane vibration mode of polar vortices in [(PbTiO)/(SrTiO)] superlattices at a critical epitaxial strain or temperature.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Center for High Pressure Science & Technology Advanced Research (HPSTAR), Shanghai, 201203, P.R. China.
Under extreme conditions, condensed matters are subject to undergo a phase transition and there have been many attempts to find another form of hydroxide stabilized over HO. Here, using Density Functional Theory (DFT)-based crystal structure prediction including zero-point energy, it is that proton superoxide (HO), the lightest superoxide, can be stabilized energetically at high pressure and temperature conditions. HO is metallic at high pressure, which originates from the 𝜋 orbitals overlap between adjacent superoxide anions (O ).
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Physical-Chemistry, Complutense University of Madrid, Madrid, Spain.
Intracellular liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)-a protein implicated in the pathological liquid-to-solid transition of stress granules.
View Article and Find Full Text PDFCell Rep
January 2025
Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan; Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan; Department of Applied Chemistry, National Chiayi University, Chiayi City 600, Taiwan; Neuroscience Program of Academia Sinica, Academia Sinica, Taipei 115, Taiwan. Electronic address:
The toxicity of C9ORF72-encoded polyproline-arginine (poly-PR) dipeptide is associated with its ability to disrupt the liquid-liquid phase separation of intrinsically disordered proteins participating in the formation of membraneless organelles, such as the nucleolus and paraspeckles. Amyotrophic lateral sclerosis (ALS)-related TAR DNA-binding protein 43 (TDP-43) also undergoes phase separation to form nuclear condensates (NCs) in response to stress. However, whether poly-PR alters the nuclear condensation of TDP-43 in ALS remains unclear.
View Article and Find Full Text PDFMicrosc Microanal
January 2025
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
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