The properties that distinguish topological crystalline insulator (TCI) and topological insulator (TI) rely on crystalline symmetry and time-reversal symmetry, respectively, which encodes different bulk and surface/edge properties. Here, we predict theoretically that electron-doped TlM (M = S and Se) (110) monolayers realize a family of two-dimensional (2D) TCIs characterized by mirror Chern number CM = -2. Remarkably, under uniaxial strain (≈ 1%), a topological phase transition between 2D TCI and 2D TI is revealed with the calculated spin Chern number CS = -1 for the 2D TI. Using spin-resolved edge states analysis, we show different edge-state behaviors, especially at the time reversal invariant points. Finally, a TlBiSe2/NaCl quantum well is proposed to realize an undoped 2D TCI with inverted gap as large as 0.37 eV, indicating the high possibility for room-temperature observation.
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http://dx.doi.org/10.1021/acs.nanolett.5b02299 | DOI Listing |
Inorg Chem
January 2025
School of Chemistry and Chemical Engineering, Jiangxi Provincial Key Laboratory of Functional Crystalline Materials Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi Province, P. R. China.
Amino acids and dipicolinic acid (DPA) are important biomarkers for identifying human health. Establishing rapid, accurate, sensitive, and simple assays is essential for disease prevention and early diagnosis. In this work, a novel Zn(II) metal-organic framework (MOF) with the formula {[Zn(μ-OH)(BTDI)(dpp)]·dpp·4HO·2DMF} (, where denotes Jiangxi University of Science and Technology, HBTDI = 5,5'-(benzo[][1,2,5]thiadiazole-4,7-diyl)diisophthalic acid; dpp = 1,3-di(4-pyridyl)propane) was successfully synthesized via a mixed-ligands strategy.
View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Kemerovo State University, Krasnaya 6, Kemerovo, 650000, Russia.
The compressibility of crystalline tetrabromophthalic anhydride (TBPA) and 1-ethyl-3-methylimidazolium nitrate (EMN) was studied based on density functional theory including dispersion interactions at pressures below 1 GPa. It is found for the first time that EMN demonstrates negative linear compressibility (NLC) up to ∼0.15 GPa, whereas TBPA shows significant NLC at pressures higher than ∼0.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
January 2025
University of Science and Technology Beijing, School of Chemistry and Biological Engineering, CHINA.
Designing and realizing new topologies represent one of the most important ways toward developing new structures and functionalities for molecule-based frameworks including SOFs, MOFs, and COFs. Herein, Aldol condensation between 5,10,15,20-tetrayl(tetrakis(([1,1':3',1''-terphenyl]-4,4''-dicarbaldehyde)))-porphyrin (TTEP) and 2,4,6-trimethyl-1,3,5-triazine (TMT) affords the vinylene-linked 3D covalent organic framework Por-COF-cya. Powder X-ray diffraction (PXRD) in combination with structural simulation reveals its high crystalline structure with an unprecedented cya topology in the molecule-based frameworks reported thus far.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305.
A central paradigm of nonequilibrium physics concerns the dynamics of heterogeneity and disorder, impacting processes ranging from the behavior of glasses to the emergent functionality of active matter. Understanding these complex mesoscopic systems requires probing the microscopic trajectories associated with irreversible processes, the role of fluctuations and entropy growth, and the timescales on which nonequilibrium responses are ultimately maintained. Approaches that illuminate these processes in model systems may enable a more general understanding of other heterogeneous nonequilibrium phenomena, and potentially define ultimate speed and energy cost limits for information processing technologies.
View Article and Find Full Text PDFMetal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction.
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