Wearable Eu@HOF luminescent fabric as a highly selective and sensitive optical synapse sensor for identification of six laboratory volatile compounds by neuromorphic computing

Abstract

As a product of the artificial intelligence (AI) age, the artificial optical synaptic sensor (AOSS) integrating an optical sensor and artificial synaptic system (ASS) shows a huge advantage for classification and quantitative detection of various harmful gases. Herein, a dual-emission Eu@IsoMe@Cu/Ni fabric (1) is fabricated for the first time by electrodeposition for harmful gas detection. 1 can emit a 1140.08 ms phosphorescence and a red emission of Eu3+ ions. By integrating an optical sensor and ASS, the 1-based AOSS is also created for the first time, which can classify and quantitatively detect six laboratory volatile compounds (LVCs), such as N-propylamine (Nme), ethylamine (Eme), methylamine (Mme), ethylenediamine (Ede), trifluoroacetic acid (TFc) and hydrochloric acid (HCl). For the sensing of these six LVCs, 1 shows high sensitivity, low detection limits and rapid response time. The sensing mechanisms of 1 toward these LVCs are clearly explored. The dual-emitting 1 can be fixed on a mask and lab coat to fabricate a 1-based wearable optical sensor, suggesting that 1 is expected to develop into the next generation of flexible wearable optical sensors. This work provides a facile method to fabricate wearable dual-emitting Eu@HOF fabric and opens the way to building an AOSS to simultaneously detect a variety of LVCs.

Graphical abstract: Wearable Eu@HOF luminescent fabric as a highly selective and sensitive optical synapse sensor for identification of six laboratory volatile compounds by neuromorphic computing

Supplementary files

Article information

Article type
Paper
Submitted
20 Apr 2022
Accepted
24 May 2022
First published
24 May 2022

J. Mater. Chem. A, 2022, Advance Article

Wearable Eu@HOF luminescent fabric as a highly selective and sensitive optical synapse sensor for identification of six laboratory volatile compounds by neuromorphic computing

X. Xu and B. Yan, J. Mater. Chem. A, 2022, Advance Article , DOI: 10.1039/D2TA03154J

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