Defect Engineered Few Layered MoS2 for Human-Machine Interface.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Raksha D Salian, Subhendu Mishra, Chinmayee Chowde Gowda, Ranjan Kumar Barik, Abhishek Kumar Singh, Chandra Sekhar Tiwary, Partha Kumbhakar
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引用次数: 0

Abstract

Ultrasensitive flexible devices have huge applications in many areas, like healthcare monitoring, human-machine interaction, and wearable technology. However, improving the sensitivity of these devices is still challenging. In the current study, a flexible non-contact sensing system is designed with a human-machine interface using defect-engineered, few-layered Molybdenum disulfide (MoS2). The fabricated sensors show high sensitivity when monitoring proximity, humidity, and in-plane applied strain. The output performance demonstrates the influence of surface defects, which greatly impact the average surface charge of the nanosheets. The experimental measurements and in-detail density functional theoretical (DFT) calculation further reveal surface charge variations on the basal planes that correlate with topographic defects and increase sensitivity. The electrical signals for different gestures of human hands are used to illustrate the identification of multidirectional bending and sliding events. These findings will contribute to understanding the effect of surface defects that play an important role in sensing applications with human-machine interface.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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