Affiliation:
1. Department of Mechanical Engineering The University of Tokyo Hongo 7‐3‐1, Bunkyo‐ku Tokyo 113‐8656 Japan
2. Department of NanoEngineering University of California San Diego 9500 Gilman Drive #0448 La Jolla California CA 92093 USA
Abstract
AbstractElectret materials are promising dielectric materials with trapped charges for various applications such as vibration energy harvesters and acoustic transducers. In the present work, ionization potential is discovered as the descriptor to quantify the charging performance for amorphous fluorinated polymer electrets. Using this descriptor, high‐throughput computations, and graph neural network models, 1 176 591 functional groups are screened on the cyclic transparent optical polymers (CYTOP), and 3 promising electrets are identified. The electrets are synthesized experimentally as 15 µm‐thick films. The films are able to keep their both bipolar surface potentials above ±3.1 kV for over 1500 h and are estimated to have longevity of 146 years under 80 °C, achieving significant improvements on charging stability among CYTOP‐based polymer electrets. The excellent bipolar charging performance can greatly enhance power generation capacity of electret‐based vibration energy harvesters. This work also demonstrates the use of deep learning as a new paradigm for accelerating practical materials discovery.
Funder
Japan Science and Technology Corporation
Core Research for Evolutional Science and Technology
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Cited by
2 articles.
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