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A water destructible SnS2 QD/PVA film based transient multifunctional sensor and machine learning assisted stimulus identification for non-invasive personal care diagnostics

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Abstract

With the advent of the internet of things, where millions of sensors are connected, they come with two important problems: disposability of the fabricated sensors and the accurate classification of the sensor data. Even though there are reports on multifunctional sensors, research into tackling both the disposability and the accurate frontend processing of the sensor data remains limited. This report demonstrates for the first time the use of a water soluble SnS2 QD/PVA film as a multifunctional sensor for both physical (strain, pressure) and chemical (breath) stimuli and further incorporating machine learning algorithms for the accurate classification of the sensor data. The water-soluble nature of the fabricated sensor allows for its easy disposability wherein the sensor completely dissolves in ∼360 seconds when immersed in water. The fabricated sensor exhibited an excellent pressure sensitivity of ∼10.14 kPa−1 and a gauge factor of ∼3.08 and can distinguish various respiration patterns. The multifunctional sensor data were subjected to machine learning algorithms wherein the data were trained to classify the stimulus with the highest accuracy of 87.7%. Combining both, the sensor has been projected for real time applications such as human motion monitoring, touch sensors, and breath analysers.

Graphical abstract: A water destructible SnS2 QD/PVA film based transient multifunctional sensor and machine learning assisted stimulus identification for non-invasive personal care diagnostics

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Article information


Submitted
06 Aug 2020
Accepted
21 Sep 2020
First published
22 Sep 2020

This article is Open Access

Mater. Adv., 2020, Advance Article
Article type
Paper

A water destructible SnS2 QD/PVA film based transient multifunctional sensor and machine learning assisted stimulus identification for non-invasive personal care diagnostics

N. Bokka, V. Selamneni and P. Sahatiya, Mater. Adv., 2020, Advance Article , DOI: 10.1039/D0MA00573H

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