Chinese Academy of Sciences

About: Chinese Academy of Sciences is a government organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 421602 authors who have published 634849 publications receiving 14894293 citations. The organization is also known as: CAS.

Special wettable materials for oil/water separation

Abstract: Oil/water separation is an important field, not only for scientific research but also for practical applications aiming to resolve industrial oily wastewater and oil-spill pollution, as well as environmental protection. Recently, research into the role of special wettability for oil/water separation has attracted much attention. In this review we summarize the design, fabrication, applications and recent developments of special wettable materials for oil/water separation. Based on the different types of separation, we organize this review into three parts: “oil-removing” type materials with superhydrophobicity and superoleophilicity (that selectively filter or absorb oil from oil/water mixtures), “water-removing” type materials with superhydrophilicity and superoleophobicity (that selectively separate water from oil/water mixtures), and smart controllable separation materials. In each section, we present in detail the representative work, introduce the design idea, outline their fabrication methods, and discuss the role of special wettability on the separation. Finally, the challenges and outlook for the future of this subject are discussed.

WLD: A Robust Local Image Descriptor

Abstract: Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.

ShiDianNao: shifting vision processing closer to the sensor

Abstract: In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are Convolutional Neural Networks (CNN), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is 60× more energy efficient than the previous state-of-the-art neural network accelerator. We present a full design down to the layout at 65 nm, with a modest footprint of 4.86mm2 and consuming only 320mW, but still about 30× faster than high-end GPUs.

Directional adhesion of superhydrophobic butterfly wings.

Abstract: We showed directional adhesion on the superhydrophobic wings of the butterfly Morpho aega. A droplet easily rolls off the surface of the wings along the radial outward (RO) direction of the central axis of the body, but is pinned tightly against the RO direction. Interestingly, these two distinct states can be tuned by controlling the posture of the wings (downward or upward) and the direction of airflow across the surface (along or against the RO direction), respectively. Research indicated that these special abilities resulted from the direction-dependent arrangement of flexible nano-tips on ridging nano-stripes and micro-scales overlapped on the wings at the one-dimensional level, where two distinct contact modes of a droplet with orientation-tuneable microstructures occur and thus produce different adhesive forces. We believe that this finding will help the design of smart, fluid-controllable interfaces that may be applied in novel microfluidic devices and directional, easy-cleaning coatings.

Single-Junction Polymer Solar Cells Exceeding 10% Power Conversion Efficiency

Abstract: A single-junction polymer solar cell with an efficiency of 10.1% is demonstrated by using deterministic aperiodic nanostructures for broadband light harvesting with optimum charge extraction. The performance enhancement is ascribed to the self-enhanced absorption due to collective effects, including pattern-induced anti-reflection and light scattering, as well as surface plasmonic resonance, together with a minimized recombination probability.