scholarly journals Novel 3D lightweight carbon foam as an effective adsorbent for arsenic(v) removal from contaminated water

RSC Advances ◽  
2016 ◽  
Vol 6 (36) ◽  
pp. 29899-29908 ◽  
Author(s):  
Pinki Rani Agrawal ◽  
Rajeev Kumar ◽  
Himani Uppal ◽  
Nahar Singh ◽  
Saroj Kumari ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Carbon Foam ◽  
Contaminated Water ◽  
Light Weight ◽  
Efficient Removal ◽  
Pentavalent Arsenic

An efficient removal of pentavalent arsenic (As(v)) from water has been developed using novel three-dimensional (3D) light weight carbon foam which exhibit adoption capacity of 38.4 μg g−1.

THREE DIMENSIONAL THERMO-MECHANICAL MODELLING OF CARBON FOAM

Equipment ◽  
2006 ◽  
Author(s):  
M. K. Alam ◽  
C. Druma ◽  
M. Anghelescu ◽  
B. Maruyama
Keyword(s):  
Three Dimensional ◽  
Carbon Foam ◽  
Mechanical Modelling

Three-dimensional light-weight piezoresistive sensors based on conductive polyurethane sponges coated with hybrid CNT/CB nanoparticles

2021 ◽  
Vol 548 ◽  
pp. 149268
Author(s):  
Litao Huang ◽  
Jianwen Chen ◽  
Youquan Xu ◽  
Dengwen Hu ◽  
Xihua Cui ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Light Weight ◽  
Piezoresistive Sensors

scholarly journals Agricultural wastes from wheat, barley, flax and grape for the efficient removal of Cd from contaminated water

RSC Advances ◽  
2018 ◽  
Vol 8 (70) ◽  
pp. 40378-40386 ◽  
Author(s):  
Patrick M. Melia ◽  
Rosa Busquets ◽  
Santanu Ray ◽  
Andrew B. Cundy
Keyword(s):  
Agricultural Production ◽  
Agricultural Wastes ◽  
Contaminated Water ◽  
Efficient Removal

Agricultural production results in wastes that can be re-used to improve the quality of the environment.

A Fast View Synthesis Implementation Method for Light Field Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Wei Gao ◽  
Linjie Zhou ◽  
Lvfang Tao
Keyword(s):  
Neural Networks ◽  
Light Field ◽  
Three Dimensional ◽  
Super Resolution ◽  
View Synthesis ◽  
Compact Representation ◽  
Light Weight ◽  
Computationally Efficient ◽  
Full Resolution ◽  
Time Systems

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.

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