EconPapers    
Economics at your fingertips  
 

Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing

Yaqian Liu, Di Liu, Changsong Gao, Xianghong Zhang, Rengjian Yu, Xiumei Wang, Enlong Li, Yuanyuan Hu, Tailiang Guo and Huipeng Chen ()
Additional contact information
Yaqian Liu: Fuzhou University
Di Liu: Fuzhou University
Changsong Gao: Fuzhou University
Xianghong Zhang: Fuzhou University
Rengjian Yu: Fuzhou University
Xiumei Wang: Fuzhou University
Enlong Li: Fuzhou University
Yuanyuan Hu: School of Physics and Electronics, Hunan University
Tailiang Guo: Fuzhou University
Huipeng Chen: Fuzhou University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Devices with sensing-memory-computing capability for the detection, recognition and memorization of real time sensory information could simplify data conversion, transmission, storage, and operations between different blocks in conventional chips, which are invaluable and sought-after to offer critical benefits of accomplishing diverse functions, simple design, and efficient computing simultaneously in the internet of things (IOT) era. Here, we develop a self-powered vertical tribo-transistor (VTT) based on MXenes for multi-sensing-memory-computing function and multi-task emotion recognition, which integrates triboelectric nanogenerator (TENG) and transistor in a single device with the simple configuration of vertical organic field effect transistor (VOFET). The tribo-potential is found to be able to tune ionic migration in insulating layer and Schottky barrier height at the MXene/semiconductor interface, and thus modulate the conductive channel between MXene and drain electrode. Meanwhile, the sensing sensitivity can be significantly improved by 711 times over the single TENG device, and the VTT exhibits excellent multi-sensing-memory-computing function. Importantly, based on this function, the multi-sensing integration and multi-model emotion recognition are constructed, which improves the emotion recognition accuracy up to 94.05% with reliability. This simple structure and self-powered VTT device exhibits high sensitivity, high efficiency and high accuracy, which provides application prospects in future human-mechanical interaction, IOT and high-level intelligence.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-35628-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35628-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-35628-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35628-0