This study demonstrates
quick and efficient removal of different
dyes from wastewater by using MoS2/CuS nanosheet composites
(NCs) as adsorbent. The MoS2/CuS NCs are prepared by a
facile hydrothermal route, and the composites exhibit high adsorption
capacity with 273.23, 432.68, 98.78, and 211.18 mg/g for rhodamine
B (RhB), methylene blue (MB), methyl orange (MO), and rhodamine 6G
dyes (RhB 6G), respectively. This is ascribed to its high specific
surface area (106.27 m2/g) and small mesopores (2.299 nm)
which provide numerous adsorption sites and uniform coverage for dye
molecules. High adsorption efficiency is obtained for RhB (93.8%),
MB (100%), and RhB 6G (84.73%), except for MO (48.9%) at the adsorption
equilibrium time at the solution concentration of 80 mg/L. The adsorption
of MoS2/CuS NCs can be well described by the pseudo-second-order
kinetic model, and the adsorption isotherm at the equilibrium fits
well with the Langmuir model. The rapid and efficient adsorption ensures
MoS2/CuS NCs to be a broad-spectrum adsorbent for different
dye contaminants in water.
Lithium–sulfur (Li–S)
batteries featuring high-energy
densities are identified as a hopeful energy storage system but are
strongly impeded by shuttle effect and sluggish redox chemistry of
sulfur cathodes. Herein, annealed melamine foam loaded 2H/1T MoS2 (CF@2H/1T MoS2) is prepared as a multifunctional
interlayer to inhibit the shuttle effect, improve redox kinetics,
and reduce the charge–discharge polarization of Li–S
batteries. The CF@2H/1T MoS2 becomes fragmented structures
after assembling the cell, which not only benefits to adsorb and catalyze
LiPSs but also to significantly buffer the volume expansion due to
a large number of gaps between fragmented structures. Meanwhile, the
batteries based on CF@2H/1T MoS2 interlayer delivers high
areal capacity of 5.1 mAh cm–2 under high sulfur
mass loading of 7.6 mg cm–2 at 0.2 C. Importantly,
the experiments of in situ Raman spectra demonstrate
that the CF@2H/1T MoS2 can obviously inhibit the shuttle
effect by effectively adsorbing and catalyzing LiPSs. This novel design
idea and low-cost melamine foam raw material open up a new way for
the application of high-energy density Li–S batteries.
Hand pose estimation is more challenging than body pose estimation due to severe articulation, self-occlusion and high dexterity of the hand. Current approaches often rely on a popular body pose algorithm, such as the Convolutional Pose Machine (CPM), to learn 2D keypoint features. These algorithms cannot adequately address the unique challenges of hand pose estimation, because they are trained solely based on keypoint positions without seeking to explicitly model structural relationship between them. We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly. The structure learning is guided by synthetic hand mask representations, which are directly computed from keypoint positions, and is further strengthened by a novel probabilistic representation of hand limbs and an anatomically inspired composition strategy of mask synthesis. We conduct extensive studies on two public datasets -OneHand 10k and CMU Panoptic Hand. Experimental results demonstrate that explicitly enforcing structure learning consistently improves pose estimation accuracy of CPM baseline models, by 1.17% on the first dataset and 4.01% on the second one. The implementation and experiment code is freely available online 1 . Our proposal of incorporating structural learning to hand pose estimation requires no additional training information, and can be a generic add-on module to other pose estimation models.
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