A facile approach to synthesize porous disordered carbon layers as energy storage units coating on graphene sheets to form interconnected frameworks by one‐step pyrolysis of the mixture of graphene oxide/polyaniline and KOH is presented. As effective energy storage units, these porous carbon layers play an important role in enhancing the electrochemical performances. The obtained porous carbon material exhibits a high specific surface area (2927 m2 g−1), hierarchical interconnected pores, moderate pore volume (1.78 cm3 g−1), short ion diffusion paths, and a high nitrogen level (6 at%). It displays both unparalleled gravimetric (481 F g−1) and outstanding volumetric capacitance (212 F cm−3) in an aqueous electrolyte. More importantly, the assembled symmetrical supercapacitor delivers not only high gravimetric (25.7 Wh kg−1 based on total mass of electroactive materials) but also high volumetric energy densities (11.3 Wh L−1) in an aqueous electrolyte. Furthermore, the assembled asymmetric supercapacitor yields a maximum energy density up to 88 Wh kg−1, which is, to the best of our knowledge, the highest value so far reported for carbon//MnO2 asymmetric supercapacitors in aqueous electrolytes. Therefore, this novel carbon material holds great promise for potential applications in energy‐related technological fields.

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Although dislocations and grain boundaries (GBs) are ubiquitous in large-scale MoS2 samples, their interaction with phonons, which plays an important role in determining the lattice thermal conductivity of polycrystalline MoS2, remains elusive. Here, we perform a systematic study of the heat transport in two-dimensional polycrystalline MoS2 by both molecular dynamics simulation and atomic Green’s function method. Our results indicate that the thermal boundary conductance of GBs of MoS2 is in the range from 6.4 × 108 to 35.3 × 108 W m–2 K–1, which is closely correlated with the overlap between the vibrational density of states of GBs and those of the pristine lattice, as well as the GB energy. It is found that the GBs strongly scatter the phonons with frequency larger than 2 THz, accompanied by a pronounced phonon localization effect and significantly reduced phonon group velocities. Furthermore, by comparing the results from realistic polycrystalline MoS2 to those from different theoretical models, we observe that the Casimir model is broken down and detailed phonon dynamics at a specific GB should be taken into account to accurately describe the phonon transport in polycrystalline materials. Our findings will provide useful guidelines for designing efficient thermoelectric and thermal management materials based on phonon–GB interaction.

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The long-wavelength behavior of vibrational modes plays a central role in carrier transport, phonon-assisted optical properties, superconductivity, and thermomechanical and thermoelectric properties of materials. Here, we present general invariance and equilibrium conditions of the lattice potential; these allow to recover the quadratic dispersions of flexural phonons in low-dimensional materials, in agreement with the phenomenological model for long-wavelength bending modes. We also prove that for any low-dimensional material the bending modes can have a purely out-of-plane polarization in the vacuum direction and a quadratic dispersion in the long-wavelength limit. In addition, we propose an effective approach to treat invariance conditions in crystals with non-vanishing Born effective charges where the long-range dipole-dipole interactions induce a contribution to the lattice potential and stress tensor. Our approach is successfully applied to the phonon dispersions of 158 two-dimensional materials, highlighting its critical relevance in the study of phonon-mediated properties of low-dimensional materials.

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm−1 K−1) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.

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