Emerging Devices Based on Two-Dimensional Monolayer Materials for Energy Harvesting

Two-dimensional (2-D) materials of atomic thickness have attracted considerable interest due to their excellent electrical, optoelectronic, mechanical, and thermal properties, which make them attractive for electronic devices, sensors, and energy systems. Scavenging the otherwise wasted energy from the ambient environment into electrical power holds promise to address the emerging energy needs, in particular for the portable and wearable devices. The versatile properties of 2-D materials together with their atomically thin body create diverse possibilities for the conversion of ambient energy. The present review focuses on the recent key advances in emerging energy-harvesting devices based on monolayer 2-D materials through various mechanisms such as photovoltaic, thermoelectric, piezoelectric, triboelectric, and hydrovoltaic devices, as well as progress for harvesting the osmotic pressure and Wi-Fi wireless energy. The representative achievements regarding the monolayer heterostructures and hybrid devices are also discussed. Finally, we provide a discussion of the challenges and opportunities for 2-D monolayer material-based energy-harvesting devices in the development of self-powered electronics and wearable technologies.

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Piezoelectric property comparison of two-dimensional ZnO nanostructures for energy harvesting devices

RSC Advances ◽

10.1039/d0ra10371c ◽

2021 ◽

Vol 11 (6) ◽

pp. 3363-3370

Author(s):

Ang Yang ◽

Yu Qiu ◽

Dechao Yang ◽

Kehong Lin ◽

Shiying Guo

Keyword(s):

Energy Harvesting ◽

Piezoelectric Property ◽

Piezoelectric Effect ◽

Theoretical Studies ◽

Two Dimensional ◽

Zno Nanostructures ◽

Energy Harvesting Devices ◽

Experimental And Theoretical Studies

In this paper, experimental and theoretical studies of the piezoelectric effect of two-dimensional ZnO nanostructures, including straight nanosheets (SNSs) and curved nanosheets (CNSs) are conducted.

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Paper-based triboelectric nanogenerators and their applications: a review

Beilstein Journal of Nanotechnology ◽

10.3762/bjnano.12.12 ◽

2021 ◽

Vol 12 ◽

pp. 151-171

Author(s):

Jing Han ◽

Nuo Xu ◽

Yuchen Liang ◽

Mei Ding ◽

Junyi Zhai ◽

...

Keyword(s):

Energy Harvesting ◽

Three Dimensional ◽

Human Machine Interaction ◽

Practical Applications ◽

Energy Devices ◽

Paper Electronics ◽

Facile Fabrication ◽

Self Powered ◽

Triboelectric Nanogenerators ◽

Energy Harvesting Devices

The development of industry and of the Internet of Things (IoTs) have brought energy issues and huge challenges to the environment. The emergence of triboelectric nanogenerators (TENGs) has attracted wide attention due to their advantages, such as self-powering, lightweight, and facile fabrication. Similarly to paper and other fiber-based materials, which are biocompatible, biodegradable, environmentally friendly, and are everywhere in daily life, paper-based TENGs (P-TENGs) have shown great potential for various energy harvesting and interactive applications. Here, a detailed summary of P-TENGs with two-dimensional patterns and three-dimensional structures is reported. P-TENGs have the potential to be used in many practical applications, including self-powered sensing devices, human–machine interaction, electrochemistry, and highly efficient energy harvesting devices. This leads to a simple yet effective way for the next generation of energy devices and paper electronics.

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Footstep Energy Harvesting with the Magnetostrictive Fiber Integrated Shoes

Materials ◽

10.3390/ma12132055 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2055 ◽

Cited By ~ 4

Author(s):

Hiroki Kurita ◽

Kenichi Katabira ◽

Yu Yoshida ◽

Fumio Narita

Keyword(s):

Energy Harvesting ◽

Piezoelectric Materials ◽

Electrical Power ◽

Point Of View ◽

Output Energy ◽

User Mobility ◽

Magnetostrictive Materials ◽

Long Time ◽

Functional Point ◽

Energy Harvesting Devices

Wearable energy harvesting devices attract attention as the devices provide electrical power without inhibiting user mobility and independence. While the piezoelectric materials integrated shoes have been considered as wearable energy harvesting devices for a long time, they can lose their energy harvesting performance after being used several times due to their brittleness. In this study, we focused on Fe–Co magnetostrictive materials and fabricated Fe–Co magnetostrictive fiber integrated shoes. We revealed that Fe–Co magnetostrictive fiber integrated shoes are capable of generating 1.2 µJ from 1000 steps of usual walking by the Villari (inverse magnetostrictive) effect. It seems that the output energy is dependent on user habit on ambulation, not on their weight. From both a mechanical and functional point of view, Fe–Co magnetostrictive fiber integrated shoes demonstrated stable energy harvesting performance after being used many times. It is likely that Fe–Co magnetostrictive fiber integrated shoes are available as sustainable and wearable energy harvesting devices.

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Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices

Sensors ◽

10.3390/s20030764 ◽

2020 ◽

Vol 20 (3) ◽

pp. 764 ◽

Cited By ~ 2

Author(s):

Jaehyun Park ◽

Ganapati Bhat ◽

Anish NK ◽

Cemil S. Geyik ◽

Umit Y. Ogras ◽

...

Keyword(s):

Energy Harvesting ◽

Gesture Recognition ◽

Energy Budget ◽

Optimization Technique ◽

Optimization Approach ◽

Activity Tracking ◽

Battery Capacity ◽

Self Powered ◽

Iot Devices ◽

Energy Harvesting Devices

Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.

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Battery-less biosensing platform

Impact ◽

10.21820/23987073.2019.10.87 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 87-89

Author(s):

Haruichi Kanaya

Keyword(s):

Energy Harvesting ◽

Large Scale ◽

New Technology ◽

Electrical Power ◽

First Century ◽

Hydroelectric Power ◽

Energy Needs ◽

External Power Source ◽

External Power ◽

Low Levels

As fossil fuel levels are exhausted, building a more sustainable world is an issue that is coming to the fore as a crucial consideration in the development of new technology. The energy needs of the planet's population are immense, and an environmentally friendly source of energy is desperately needed. Energy harvesting from renewable sources is not a new concept - windmills have been around since the first century - but the desire to harness renewable energy has intensified. Energy harvesting technology is the term given to technology used for collecting unused energy from the surrounding environment and converting it into electrical power. Solar, wind and hydroelectric power are perhaps the best-known of these technologies. However, there are many other forms of energy that are under developed and hold much potential for powering the future. These include vibration, pressure, heat and temperature difference. While large-scale power generation cannot be realised using these sources due to their low levels, devices with low power demands may be able to harness such energy sources, potentially eliminating the need for an external power source. Dr Haruichi Kanaya at Kyushu University is leading a team investigating wireless technology.

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Optimum power of a nonlinear piezomagnetoelastic energy harvester with using multidisciplinary optimization algorithms

Journal of Intelligent Material Systems and Structures ◽

10.1177/1045389x20974439 ◽

2020 ◽

pp. 1045389X2097443

Author(s):

Mohammad Tahmasbi ◽

Asghar Jamshiddoust ◽

Amin Farrokhabadi

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Energy Harvesting ◽

Energy Harvester ◽

Random Search ◽

Electrical Power ◽

Multidisciplinary Optimization ◽

Physical Parameters ◽

Parallel Configuration ◽

Energy Harvesting Devices

Energy-harvesting devices have been widely used to generate electrical power. Through the use of energy harvesting techniques, ambient vibration energy can be captured and converted into usable electricity in order to create self-powering systems. In the present study, to further improve the efficiency of energy-harvesting devices, a nonlinear piezomagnetoelastic energy harvester is proposed in two different configurations that is parallel and series. In order to optimize the generated electrical power, the physical parameters of the harvester are chosen as the design variables. Classical and Metaheuristic algorithms, namely, random search, genetic algorithm, and simulated annealing are applied to optimize the output power regarding the stress and displacement constraints and feasible variable bounds. Finally, the results of the applied algorithms are compared together. The results demonstrate that most of the implemented algorithms converge to the similar objective function value. The constrained random search methods with SQP and active set algorithms converge faster with small iterations. However, the genetic algorithm and simulated annealing algorithm are more capable to find the global optimum. The obtained results revealed that, before the optimization, the average extracted power in specified time was 3.121 W in parallel configuration and 3.156 W in serial configuration. By using the optimization approaches, the power converged to 4.273 W in parallel configuration and 4.296 W in serial configuration that means the power is increased by 36.9% and 36.1% approximately.

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