Graphene humidity sensor with gold nanoparticles

Humidity sensors can be used to monitor body sweat. Here, we studied a humidity sensor that comprised of a graphene layer between two electrodes. The operating principle is that the humidity sensor will respond when vapor reaches the graphene layer from the top. Based on the humidity diffusion, the sensor measures the relative humidity (RH) with different response times. Graphene is a material with high diffusivity and small thickness that can increase the sensitivity of a sensor. Based on the Micro Electro Mechanical Systems (MEMS) method, we modeled the humidity sensor using COMSOL Multiphysics transport of diluted species software. Finally, we used the concentration values from the simulations to determine the relationship between capacitance and relative humidity. The sensitivity was found to be 3.379 ×10^−11 pF/%RH for the 4-layer graphene, 1.210 ×10^−14 pF/%RH for the 8-layer graphene, and 3.597 ×10^−11 pF/%RH for the 16-layer graphene sensor. The sensitivity of 4-layer graphene with gold sensor is 3.872 ×10^−13 pF/%RH which is smaller than 4-layer graphene one, and graphene with gold should have better response time than 4-layer graphene

Fast Preprocessing for Robust Face Sketch Synthesis

Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos. The critical step causing the failure is the search of similar patch candidates for an input photo patch. Conventional illumination invariant patch distances are adopted rather than directly relying on pixel intensity difference, but they will fail when local contrast within a patch changes. In this paper, we propose a fast preprocessing method named Bidirectional Luminance Remapping (BLR), which interactively adjust the lighting of training and input photos. Our method can be directly integrated into state-of-the-art exemplar-based methods to improve their robustness with ignorable computational cost.Comment: IJCAI 2017. Project page: http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sketch/index.htm

Learning to Hallucinate Face Images via Component Generation and Enhancement

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methodsComment: IJCAI 2017. Project page: http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm

The Interactions Between Oxide Film Inclusions and Inoculation Particles TiB2 in Aluminum Melt

In this work, a systematic study on the interactions between aluminum oxide films and TiB2 grain refiner particles and their effect on grain refinement behavior have been conducted. Oxide f ilms were introduced into a commercial purity aluminum melt by adding AA 6061 alloy chips while the grain refiner particles were introduced by adding Al-3T-1B master alloy. Strong sedimentation of TiB2 grain refiner particles was observed in aluminum melt without chip addition during long-time settling. Most of the TiB2 particles were settled and accumulated at the bottom of crucible. In contrast, the sedimentation of TiB2 particles is much less in the melt with the addition of oxide films. A large fraction of TiB2 particles were found to be adhered to the oxide films located at the top part of the crucible, which inhibited the sedimentation of grain refiner particles. TP-1 type tests were also done to study the grain refinement efficiency of Al-3Ti-1B master alloy under different melt cleanliness and settling time. It is found that sedimentation of TiB2 particles greatly reduces the grain refinement efficiency. The introduction of oxide films seems to slightly alleviate the fading effect. This is owing to the strong adherence between the oxide films and TiB2 particles, which leads to a retardation of particle sedimentation.publishedVersio

Influence of grain refiners on the wettability of Al2O3 substrate by aluminum melt

It is well known that grain refiner additions in aluminum melts significantly reduce the filtration efficiency of ceramic foam filters (CFF). However, the mechanism remains unclear. In this work, the influence of grain refiners on the wettability of alumina substrate by aluminum melt was studied by both conventional sessile drop and improved sessile drop methods at different temperatures and vacuums. Commercial purity aluminum (CP-Al) and grain refiner master alloys Al-3Ti-1B, Al-5Ti-1B, Al-3Ti-0.15C were used. It is found that master alloy melts wet alumina substrate better than CP-Al. Generally, a lower temperature or lower vacuum results in a higher contact angle. The roles of grain refiner particles in improving the wettability were studied by analyzing the solidification structure of post wetting-test droplets using SEM. Strong sedimentation of grain refiner particles at the metal-substrate interface was observed, which is attributed to the higher density of grain refiner particles compared to the Al melt. Meanwhile, a large fraction of grain refiner particles agglomerates at the oxide skin of the aluminum droplets, showing a strong adhesion between the particles and oxide skin. Such adhering of grain refiner particles is proposed to enhance the rupture of the original oxide skin of the droplets and slow down the reoxidation process at the surface layer. Both adherence of grain refiner particles to surface oxide skin and sedimentation of particles at the metal-substrate interface are responsible for the wetting improvement.publishedVersio

The Influences of Grain Refiner, Inclusion Level, and Filter Grade on the Filtration Performance of Aluminum Melt

The addition of grain refiner particles in the aluminum melt is known to reduce the filtration efficiency of ceramic foam filter (CFF). In the present work, a systematic study on the influence of the addition level of Al-Ti-B master alloys and the inclusion level on the filtration performance of aluminum melt has been investigated by pilot-scale filtration tests using 50 PPi and 80 PPi filters. The inclusion level of the melt has been measured using both LiMCA and PoDFA. For 80 PPi CFF, the N20 inclusion (diameter larger than 20 μm) value in the post-filtrated melt does not increase when an ultra-high level of inclusions is introduced in the form of chips. For the melts with a low level of grain refiners (~ 0.5 kg/ton), the filtration performance of CFF is not affected by grain refiners, regardless of inclusion load. An addition of 2.0 kg/ton grain refiners reduces the filtration performance for melts with a high inclusion level, where post-filtration inclusions with the size of 15-20 µm were significantly increased. It is found, however, for the melts with an ultra-high inclusion load, the filtration performance of 80 PPi CFF is not affected by the grain refiner addition up to 2.0 kg/ton. The interactions between inclusions and grain refiner particles and the filtration mechanism have been studied by characterizing the spent filter and measuring the pressure drop during the filtration process. It is revealed that the strong adherence between oxide film with grain refiner particles dominates the grain refiner influence on the filtration performance of CFF. During the filtration process, oxide films have strong influences on the capturing of other inclusions such as oxide particles and TiB2 particles by the filter. A mechanism based on the interactions between oxide films and grain refiner particles is proposed to explain the CFF performance under the influence of grain refiner.publishedVersio

RESEARCH ON DAMAGE CHARACTERISTICS AND PROTECTIVE STRUCTURE DESIGN OF STEEL PONTOONS UNDER NEAR-FIELD EXPLOSION LOAD

The focus of this paper is to investigate the damage characteristics and protective structure design of pontoons as an important barrier for the protection of ports. Two types of protective measures of pontoons are investigated：filling tanks with water and installing springs in tanks. In this paper, the damage characteristics of two types of pontoon side structures under the action of near-field explosion loads are simulated by using LS-DYNA explicit dynamic analysis software and the ALE algorithm. According to the numerical experiment results for filling different volumes of water in the side tanks, the volume of water for the minimum deformation of the shell plate is 100%, and for the first longitudinal bulkhead, it is 30-40%. Moreover, by applying weights to their deformations based on the actual explosion-proof performance requirements of the shell plate and the first longitudinal bulkhead, the pontoon side structure with the best explosion-proof performance can be obtained. The plastic deformation of the pontoon structure equipped with different types of springs is an order of magnitude smaller than that of the ordinary structure and of the pontoon structure filled with a water medium in the positive tanks. The explosive shock wave energy absorbed by the pontoon is effectively reduced by the addition of water or springs to the protective tanks. The minimum energy absorbed by the pontoon structure with water added in the protective tanks is 18.31% of the energy absorbed by the ordinary structure, and the corresponding volume ratio of water added in the protective tanks is 100%. The pontoon structure with springs in the side protection tanks absorbs only 7.2% of the energy absorbed by the ordinary structure. Both new side protection structures have demonstrated excellent explosion-proof performance

Particle image velocimetry experiments and direct numerical simulations of solids suspension in transitional stirred tank flow

The financial supports from the National Key R&D Program of China (2017YFB0306703) and the National Natural Science Foundation of China (No.21676007) are gratefully acknowledged.Peer reviewedPostprin

New insight into the material parameter B to understand the enhanced thermoelectric performance of Mg2Sn1−x−yGexSby

Historically, a material parameter B incorporating weighted mobility and lattice thermal conductivity has guided the exploration of novel thermoelectric materials. However, the conventional definition of B neglects the bipolar effect which can dramatically change the thermoelectric energy conversion efficiency at high temperatures. In this paper, a generalized material parameter B* is derived, which connects weighted mobility, lattice thermal conductivity, and the band gap. Based on the new parameter B*, we explain the successful tuning of the electron and phonon transport in Mg[subscript 2]S[subscript n1−x−y]Ge[subscript x]Sb[subscript y], with an improved ZT value from 0.6 in Mg[subscript 2]Sn[subscript 0.99]Sb[subscript 0.01] to 1.4 in Mg[subscript 2]Sn[subscript 0.73]Ge[subscript 0.25]Sb[subscript 0.02]. We uncover that the Ge alloying approach simultaneously improves all the key variables in the material parameter B*, with an ∼25% enhancement in the weighted mobility, ∼27% band gap widening, and ∼50% reduction in the lattice thermal conductivity. We show that a higher generalized parameter B* leads to a higher optimized ZT in Mg[subscript 2]Sn[subscript 0.73]Ge[subscript 0.25]Sb[subscript 0.02], and some common thermoelectric materials. The new parameter B* provides a better characterization of material's thermoelectric transport, particularly at high temperatures, and therefore can facilitate the search for good thermoelectric materials.United States. Department of Energy. Office of Science. Solid-State Solar Thermal Energy Conversion Center (Award DE-SC0001299/DE-FG02-09ER46577

Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.Comment: In IJCV 201