Flexible composite material for self-powered applications via triboelectricity and mechanoluminescence: PDMS/ZnS:Cu composites

• Published in: Nano energy Vol. 114; p. 108668
• Main Authors: Hajra, Sugato, Panda, Swati, Song, Seongkyu, Panigrahi, Basanta Kumar, Pakawanit, Phakkhananan, Jeong, Soon Moon, Kim, Hoe Joon
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-09-2023
• Subjects: Finger joint; Mechanoluminescence; Triboelectric; Wind flow; Mechanoluminescence; Finger joint; Wind flow; Triboelectric
• ISSN: 2211-2855
• DOI: 10.1016/j.nanoen.2023.108668

Triboelectricity and mechanoluminescence (ML) arise from the same physical processes of charge separation and recombination within the material. By measuring both phenomena simultaneously, researchers can gain insights into the nature and extent of charge separation and recombination, as well as the correlation between mechanical stress and light emission. This work shows that a composite based on polydimethylsiloxane (PDMS)/ ZnS:Cu particles possess the ideal ML and can also generate electric output by contact electrification. Using mechanoluminescent materials in wearable devices offers a non-invasive and reliable way to measure mechanical deformation and stress, generating crucial data for a wide range of applications. The single-electrode mode-based triboelectric nanogenerator (TENG) was developed to realize the simultaneous ML and TENG output. During pressing motion, the PDMS/ ZnS: Cu-based TENG device delivered an electrical output of 210 V and 800 nA. Furthermore, the bending motion was then utilized to demonstrate the simultaneous ML and TENG output during various self-powered applications, such as the monitoring of bent body parts, finger joints, and harnessing wind flow.
[Display omitted]
•Single-electrode mode-based TENG demonstrates simultaneous mechanoluminescence and triboelectricity output.•Various mechanical stimuli to detect mechanoluminescence and triboelectricity output.•A self-powered sensor for monitoring wind flow and joint bending.•An overall accuracy of 96.7% indicates that the ANN effectively classifies wind flow.