Small-Scale Energy Harvesting and Storage with Thermogalvanic Cells

2019 ◽  
Vol 16 (26) ◽  
pp. 27-36 ◽  
Author(s):  
Nicholas S. Hudak ◽  
G. Amatucci
2021 ◽  
Vol 288 ◽  
pp. 116617
Author(s):  
Mickaël Lallart ◽  
Linjuan Yan ◽  
Hiroyuki Miki ◽  
Gaël Sebald ◽  
Gildas Diguet ◽  
...  

2012 ◽  
Vol 476-478 ◽  
pp. 1336-1340
Author(s):  
Kai Feng Li ◽  
Rong Liu ◽  
Lin Xiang Wang

The concept of energy harvesting works towards developing self-powered devices that do not require replaceable power supplies. Energy scavenging devices are designed to capture the ambient energy surrounding the electronics and convert it into usable electrical energy. A number of sources of harvestable ambient energy exist, including waste heat, vibration, electromagnetic waves, wind, flowing water, and solar energy. While each of these sources of energy can be effectively used to power remote sensors, the structural and biological communities have placed an emphasis on scavenging vibrational energy with ferroelectric materials. Ferroelectric materials have a crystalline structure that provide a unique ability to convert an applied electrical potential into a mechanical strain or vice versa. Based on the properties of the material, this paper investigates the technique of power harvesting and storage.


2012 ◽  
Vol 216 ◽  
pp. 84-88 ◽  
Author(s):  
Vidhya Chakrapani ◽  
Florencia Rusli ◽  
Michael A. Filler ◽  
Paul A. Kohl

Solar Energy ◽  
2021 ◽  
Vol 226 ◽  
pp. 147-153
Author(s):  
Dongli Fan ◽  
Yaqing Lu ◽  
Yufeng Cao ◽  
Jie Liu ◽  
Shaohui Lin ◽  
...  

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.


2021 ◽  
Vol 33 (19) ◽  
pp. 2170151
Author(s):  
Veenasri Vallem ◽  
Yasaman Sargolzaeiaval ◽  
Mehmet Ozturk ◽  
Ying‐Chih Lai ◽  
Michael D. Dickey

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