Journal
SENSORS
Volume 22, Issue 20, Pages -Publisher
MDPI
DOI: 10.3390/s22208032
Keywords
carbon nanotubes; convolutional neural networks; pollutant detection; screen-printed electrodes; cyclic voltammetry
Funding
- Project 2DSENSE - NATO under the SPS Programme [G5777]
- Project TERASSE - EU under H2020MSCA-RISE Programme [823878]
- Academy of Finland [320166]
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This paper proposes a deep learning technique for accurate detection and reliable classification of organic pollutants in water. The study demonstrates the possibility of improving detection sensitivity with modified electrodes and achieving accurate classification using a convolutional neural network after Gramian transformation.
This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about x25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments.
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