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Cited by 290

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Implantable Energy‐Harvesting Devices

Shi, Bojing, Li, Zhou, Fan, Yubo·2018·Advanced Materials

Abstract

The sustainable operation of implanted medical devices is essential for healthcare applications. However, limited battery capacity is a key challenge for most implantable medical electronics (IMEs). The human body abounds with mechanical and chemical energy, such as the heartbeat, breathing, blood circulation, and the oxidation–reduction of glucose. Harvesting energy from the human body is a possible approach for powering IMEs. Many new methods for developing in vivo energy harvesters (IVEHs) have been proposed for powering IMEs. In this context energy harvesters based on the piezoelectric effect, triboelectric effect, automatic wristwatch devices, biofuel cells, endocochlear potential, and light, with an emphasis on fabrication, energy output, power management, durability, animal experiments, evaluation criteria, and typical applications are discussed. Importantly, the IVEHs that are discussed, are actually implanted into living things. Future challenges and perspectives are also highlighted.

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Crossref · 10.1002/adma.201801511

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Metadata

Authors
Shi, B., Li, Z., & Fan, Y.
Venue
Advanced Materials
Publisher
Wiley
Published
2018-07-24
Volume
30
Issue
44
Pages
e1801511-e1801511
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2884796804
Cited by
290
References
10
FWCI
21.43
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Implantable Energy‐Harvesting Devices. https://app.cochat.ai/papers/10.1002/adma.201801511
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Shi, B., Li, Z., & Fan, Y. (2018). Implantable Energy‐Harvesting Devices. Advanced Materials. https://doi.org/10.1002/adma.201801511

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.