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Cited by 269

The DOI matches its Crossref record; 159 of the 159 references it cites were found in a registry or an index.

Energy Harvesting for Nanostructured Self‐Powered Photodetectors

Peng, Lin, Hu, Linfeng, Fang, Xiaosheng·2014·Advanced Functional Materials

Abstract

Harvesting the available forms of energies in the environment to create self‐powered nanosystems is now becoming a technological reality. Self‐powered nanodevices and nanosystems are expected to play a crucial role in the future development of nanotechnology because of their specific role in fundamental studies and nanotechnological applications, mainly due to their size‐dependent properties and independent, sustainable, maintainance‐free operation. As a new field in self‐powered nanotechnology‐related research, self‐powered photodetectors have been developed which exhibit a much faster photoresponse and higher photosensitivity than the conventional photoconductor‐based photodetectors. Herein, the energy‐havesting techniques are discussed and their prospects for application in self‐powered photodetectors are summarized. Moreover, potential future directions of this research area are highlighted.

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Crossref · 10.1002/adfm.201303367

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Metadata

Authors
Peng, L., Hu, L., & Fang, X.
Venue
Advanced Functional Materials
Publisher
Wiley
Published
2014-01-14
Volume
24
Issue
18
Pages
2591-2610
ISSN
  • 1616-301X (ISSN-L)
  • 1616-3028
OpenAlex
W2034456872
Cited by
269
References
159
FWCI
12.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Energy Harvesting for Nanostructured Self‐Powered Photodetectors. https://app.cochat.ai/papers/10.1002/adfm.201303367
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Peng, L., Hu, L., & Fang, X. (2014). Energy Harvesting for Nanostructured Self‐Powered Photodetectors. Advanced Functional Materials. https://doi.org/10.1002/adfm.201303367

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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.