Recent discoveries of exotic physical phenomena, such as unconventional superconductivity in magic‐angle twisted bilayer graphene, dissipationless Dirac fermions in topological insulators, and quantum spin liquids, have triggered tremendous interest in quantum materials. The macroscopic revelation of quantum mechanical effects in quantum materials is associated with strong electron–electron correlations in the lattice, particularly where materials have reduced dimensionality. Owing to the strong correlations and confined geometry, altering atomic spacing and crystal symmetry via strain has emerged as an effective and versatile pathway for perturbing the subtle equilibrium of quantum states. This review highlights recent advances in strain‐tunable quantum phenomena and functionalities, with particular focus on low‐dimensional quantum materials. Experimental strategies for strain engineering are first discussed in terms of heterogeneity and elastic reconfigurability of strain distribution. The nontrivial quantum properties of several strain‐quantum coupled platforms, including 2D van der Waals materials and heterostructures, topological insulators, superconducting oxides, and metal halide perovskites, are next outlined, with current challenges and future opportunities in quantum straintronics followed. Overall, strain engineering of quantum phenomena and functionalities is a rich field for fundamental research of many‐body interactions and holds substantial promise for next‐generation electronics capable of ultrafast, dissipationless, and secure information processing and communications.
Understanding modulation of liquid molecule slippage along graphene surfaces is crucial for many promising applications of two-dimensional materials, such as in sensors, nanofluidic devices, and biological systems. Here, we use force measurements by atomic force microscopy (AFM) to directly measure hydrodynamic, solvation, and frictional forces along the graphene plane in seven liquids. The results show that the greater slip lengths correlate with the interfacial ordering of the liquid molecules, which suggests that the ordering of the liquid forming multiple layers promotes slip. This phenomenon appears to be more relevant than solely the wetting behavior of graphene or the solid−liquid interaction energy, as traditionally assumed. Furthermore, the slip boundary condition of the liquids along the graphene plane is sensitive to the substrate underneath graphene, indicating that the underlying substrate affects graphene's interaction with the liquid molecules. Because interfacial slip can have prominent consequences on the pressure drop, on electrical and diffusive transport through nanochannels, and on lubrication, this work can inspire innovation in many applications through the modulation of the substrate underneath graphene and of the interfacial ordering of the liquid.
Inclusion of auxiliary cracks increases the fracture stress of silicene nanosheets with a pre existing crack.
Understanding of the material properties of layered transition-metal dichalcogenides (TMDs) is critical for their applications in flexible electronics. Data-driven machine learning (ML)-based approaches are being developed in contrast to the traditional experimental or computational methods to predict and understand material properties under varied operating conditions. In this study, we used two ML algorithms, namely, long short-term memory (LSTM) and feed forward neural network (FFNN), combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX 2 (M = Mo, W and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress−strain response, whereas the FFNN is used to predict material properties such as fracture stress, fracture strain, and Young's modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of the LSTM model to predict the entire stress−strain curve is advantageous for assessing material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs.
The low bending stiffness of atomic membranes from van der Waals ferroelectrics such as α-In2Se3 allow access to a regime of strong coupling between electrical polarization and mechanical deformation at extremely high strain gradients and nanoscale curvatures. Here, we investigate the atomic structure and polarization at bends in multilayer α-In2Se3 at high curvatures down to 0.3 nm utilizing atomic-resolution scanning transmission electron microscopy, density functional theory, and piezoelectric force microscopy. We find that bent α-In2Se3 produces two classes of structures: arcs, which form at bending angles below ∼33°, and kinks, which form above ∼33°. While arcs preserve the original polarization of the material, kinks contain ferroelectric domain walls that reverse the out-of-plane polarization. We show that these kinks stabilize ferroelectric domains that can be extremely small, down to 2 atoms or ∼4 Å wide at their narrowest point. Using DFT modeling and the theory of geometrically necessary disclinations, we derive conditions for the formation of kink-induced ferroelectric domain boundaries. Finally, we demonstrate direct control over the ferroelectric polarization using templated substrates to induce patterned micro- and nanoscale ferroelectric domains with alternating polarization. Our results describe the electromechanical coupling of α-In2Se3 at the highest limits of curvature and demonstrate a strategy for nanoscale ferroelectric domain patterning.
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