We conduct molecular dynamics (MD) simulations to study the thermal conductivity of freestanding silicene and silicene supported on an amorphous silicon dioxide (SiO2) substrate in the temperature range from 300 to 900 K. The results show that the thermal conductivity decreases with increasing temperature and that the presence of the SiO2 substrate results in a great reduction, up to 78% at 300 K, to the thermal conductivity of silicene. With atomic trajectories from equilibrium MD simulations, we perform spectral energy density analysis to compute the thermal conductivities, spectral phonon relaxation times, and spectral phonon mean free paths (MFPs) of freestanding and supported silicene at 300 K. When silicene is put on a SiO2 substrate, the phonon relaxation times are decreased from 1–13 ps to less than 1 ps, and the phonon MFPs are reduced from 10–120 nm to 0–20 nm. We also calculate the thermal conductivity contributions from all phonon branches and find that the thermal conductivities of freestanding and supported silicene are mainly (>85%) contributed by the longitudinal and transverse acoustic phonons, while the out-of-plane acoustic phonons have a contribution less than 3%. Our study predicts the reduction of the thermal conductivity of silicene due to substrate effects and provides a fundamental understanding of the reduction in terms of the spectral phonon relaxation times and MFPs.

Controlling thermomechanical anisotropy is important for emerging heat management applications such as thermal interface and electronic packaging materials. Whereas many studies report on thermal transport in anisotropic nanocomposite materials, a fundamental understanding of the interplay between mechanical and thermal properties is missing, due to the lack of measurements of direction‐dependent mechanical properties. In this work, exceptionally coherent and transparent hybrid Bragg stacks made of strictly alternating mica‐type nanosheets (synthetic hectorite) and polymer layers (polyvinylpyrrolidone) were fabricated at large scale. Distinct from ordinary nanocomposites, these stacks display long‐range periodicity, which is tunable down to angstrom precision. A large thermal transport anisotropy (up to 38) is consequently observed, with the high in‐plane thermal conductivity (up to 5.7 W m−1 K−1) exhibiting an effective medium behavior. The unique hybrid material combined with advanced characterization techniques allows correlating the full elastic tensors to the direction‐dependent thermal conductivities. We, therefore, provide a first analysis on how the direction‐dependent Young's and shear moduli influence the flow of heat.

Abstract:Forest mapping is an important source of information for assessing woodland resources and a key issue for any National Forest Inventory (NFI). In the present study, a detailed wall-to-wall forest cover map was generated for all of Switzerland, which meets the requirement of the Swiss NFI forest definition. The workflow is highly automated and based on digital surface models from image-based point clouds of airborne digital sensor data. It fully takes into account the four key criteria of minimum tree height, crown coverage, width, and land use. The forest cover map was validated using almost 10,000 terrestrial and stereo-interpreted NFI plots, which verified 97% agreement overall. This validation implies different categories such as five production regions, altitude, tree type, and distance to the forest border. Overall accuracy was lower at forest borders but increased with increasing distance from the forest border. Commission errors remained stable at around 10%, but increased to 17.6% at the upper tree line. Omission errors were low at 1%-10%, but also increased with altitude and mainly occurred at the upper tree line (19.7%). The main reasons for this are the lower image quality and the NFI height definition for forest which apparently excludes shrub forest from the mask. The presented forest mapping approach is superior to existing products due to its national coverage, high level of detail, regular updating, and implementation of the land use criteria.

Although extensive experimental and theoretical works have been conducted to understand the ballistic and diffusive phonon transport in nanomaterials recently, direct observation of temperature and thermal nonequilibrium of different phonon modes has not been realized. Herein, we have developed a method within the framework of molecular dynamics to calculate the temperatures of phonon in both real and phase spaces. Taking silicon thin film and graphene as examples, we directly obtained the spectral phonon temperature (SPT) and observed the local thermal nonequilibrium between the ballistic and diffusive phonons. Such nonequilibrium also generally exists across interfaces and is surprisingly large, and it provides an additional thermal interfacial resistance mechanism. Our SPT results directly show that the vertical thermal transport across the dimensionally mismatched graphene/substrate interface is through the coupling between flexural acoustic phonons of graphene and the longitudinal phonons in the substrate with mode conversion. In the dimensionally matched interfaces, e.g. graphene/graphene junction and graphene/boron nitride planar interfaces, strong coupling occurs between the acoustic phonon modes on both sides, and the coupling decreases with interfacial mixing. The SPT method together with the spectral heat flux can eliminate the size effect of the thermal conductivity prediction induced from ballistic transport. Our work shows that in thin films and across interfaces, phonons are in local thermal nonequilibrium. * λQλ ,whereQ λ (t) is the time derivative of normal mode amplitude, which is given by the Fourier transform of atomic arXiv:1703.10957v1 [cond-mat.mes-hall]

Small molecule organic semiconductors (OSCs) suffer from their uncontrolled nucleation and growth during solution processing limiting their functionality in electronic devices. In this work, we present a new method based on dip-coating a blend consisting of OSC and insulating polymer to control the crystallization of the active film for organic field-effect transistors. A small fraction of amorphous poly(methyl methacrylate) (PMMA) efficiently improves the crystallization of dip-coated small molecule OSCs, α,ω-dihexylquaterthiophene (DH4T) and diketopyrrolopyrrole-sexithiophene (DPP6T). The maximum charge carrier mobilities of dip-coated OSC:PMMA films are significantly higher than drop-cast blend ones and comparable with OSC single crystals. The high charge carrier mobility originates from a continuous alignment of the crystalline films and stratified OSC and PMMA layers. The improved crystallization is attributed to two mechanisms: firstly, the polymer binder leads to a viscosity gradient at the meniscus during dip-coating, facilitating the draw of solute and thus mass transport. Secondly, the polymer binder solidifies at the bottom layer, reducing the nucleation barrier height of small molecule OSC. Our findings demonstrate that a small fraction of a polymer binder during dip-coating efficiently balances the mass transport during Max Planck Institute for Polymer Research -Author's Manuscript 2 solution processing and improves the crystallization as well as the electronic properties of small molecule OSC films. Crystallization of small molecular organic semiconductors (OSCs) during meniscusguided coating was enhanced by blending with a minor amount of an insulating polymer binder. The polymer binder increased mass transport and formed a bottom layer reducing the nucleation barrier height for OSC crystallization. The aligned crystalline films with stratified OSC/polymer layers contribute to a high charge transport in field effect transistors.

The crystallization and film formation of organic semiconductors are controlled by the meniscus shape during meniscus guided coating for field-effect transistors.

Strain and defects in graphene have critical impact on morphology and properties of graphene. Here we report equi-biaxial compressive strain in monolayer graphene on SiO2 and Si3N4 substrates induced by thermal cycling in vacuum. The equi-biaxial strain is attributed to the mismatch in coefficient of thermal expansion between graphene and the substrate and sliding of graphene on the substrate. The sliding occurs during heating at the temperatures of 390 and 360 K for graphene on SiO2 and Si3N4 substrates, respectively. The biaxial Grüneisen parameter is determined to be 1.95 and 3.15 for G and 2D Raman bands of graphene, respectively. As the heating temperature exceeds a threshold temperature (1040 K for graphene/SiO2 and 640 K for graphene/Si3N4), buckling ridges are observed in graphene after the thermal cycle, from which the biaxial buckling strain of graphene on SiO2 and Si3N4 substrates are obtained as 0.21% and 0.22%, respectively. Importantly, the induced buckling ridges in graphene exhibit a pattern representing the symmetry of graphene crystal structure, which indicates that graphene relieves the compressive stress mainly along its lattice symmetry directions. These thermally induced graphene ridges are also found reminiscent of those in the synthesized graphene, suggesting the same origin of formation of the buckling ridges under biaxial compression.

Remote sensing scene classification (RSSC) is a hotspot and play very important role in the field of remote sensing image interpretation in recent years. With the recent development of the convolutional neural networks, a significant breakthrough has been made in the classification of remote sensing scenes. Many objects form complex and diverse scenes through spatial combination and association, which makes it difficult to classify remote sensing image scenes. The problem of insufficient differentiation of feature representations extracted by Convolutional Neural Networks (CNNs) still exists, which is mainly due to the characteristics of similarity for inter-class images and diversity for intra-class images. In this paper, we propose a remote sensing image scene classification method via Multi-Branch Local Attention Network (MBLANet), where Convolutional Local Attention Module (CLAM) is embedded into all down-sampling blocks and residual blocks of ResNet backbone. CLAM contains two submodules, Convolutional Channel Attention Module (CCAM) and Local Spatial Attention Module (LSAM).The two submodules are placed in parallel to obtain both channel and spatial attentions, which helps to emphasize the main target in the complex background and improve the ability of feature representation. Extensive experiments on three benchmark datasets show that our method is better than stateof-the-art methods.