Molybdenum disulfide (MoS 2) is one of the most important two-dimensional materials after graphene. Monolayer MoS 2 has a direct bandgap (1.9 eV) and is potentially suitable for post-silicon electronics. Among all atomically thin semiconductors, MoS 2 's synthesis techniques are more developed. Here, we review the recent developments in the synthesis of hexagonal MoS 2 , where they are categorized into top-down and bottom-up approaches. Micromechanical exfoliation is convenient for beginners and basic research. Liquid phase exfoliation and solutions for chemical processes are cheap and suitable for large-scale production; yielding materials mostly in powders with different shapes, sizes and layer numbers. MoS 2 films on a substrate targeting high-end nanoelectronic applications can be produced by chemical vapor deposition, compatible with the semiconductor industry. Usually, metal catalysts are unnecessary. Unlike graphene, the transfer of atomic layers is omitted. We especially emphasize the recent advances in metalorganic chemical vapor deposition and atomic layer deposition, where gaseous precursors are used. These processes grow MoS 2 with the smallest building-blocks, naturally promising higher quality and controllability. Most likely, this will be an important direction in the field. Nevertheless, today none of those methods reproducibly produces MoS 2 with competitive quality. There is a long way to go for MoS 2 in real-life electronic device applications.
The asymmetric condensation/amine
addition cascade sequence of
2-aminobenzamides and aldehydes catalyzed by chiral spirocyclic SPINOL-phosphoric
acids was realized. SPINOL-phosphoric acid 1j was found
to be a general, highly enantioselective organocatalyst for such cascade
reactions at room temperature, affording 2,3-dihydroquinazolinones
in excellent yields (up to 99%) with good to excellent ee’s
(up to 98%). The best level of stereocontrol was obtained for aromatic
aldehydes with an ortho substituent.
Recently, convolutional neural networks (CNNs) showed excellent performance in many tasks, such as computer vision and remote sensing semantic segmentation. Especially, the ability to learn high-representation features of CNN draws much attention. And random forest (RF) algorithm, on the other hand, is widely applied for variables selection, classification, and regression. Based on the previous fusion models that fused CNN with the other models, such as conditional random fields (CRFs), support vector machine (SVM), and RF, this article tested a method based on the fusion of an RF classifier and the CNN for a very high resolution remote sensing (VHRRS) based forests mapping. The study area is located in the south of China and the main purpose was to precisely distinguish Lei bamboo forests from the other subtropical forests. The main novelties of this article are as follows. First, a test was conducted to confirm if a fusion of CNN and RF make an improvement in the VHRRS information extraction. Second, based on RF, variables with high importance were selected. Then, a test was again conducted to confirm if the learning from the selected variables will further give better results.
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