A Pencil-Drawn Electronic Tongue for Environmental Applications

Kirsanov, Dmitry; Mukherjee, Subhankar; Pal, Souvik; Ghosh, Koustuv; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib; Jendrlin, Martin; Radu, Aleksandar; Zholobenko, Vladimir L.; Dehabadi, Monireh

doi:10.3390/s21134471

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…In this sense, the identification and quantification of microplastics in the marine environment have also been explored [131]. Other approaches include the development of systems for quantitative analysis of inorganic ions in aqueous media by using sensor arrays composed by graphite and zeolites to evaluate samples in rivers [132].…”

Section: Detection Of Substances In Watermentioning

confidence: 99%

Electronic Tongues and Noses: A General Overview

Tibaduiza,

Anaya,

Gómez

et al. 2024

Biosensors

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As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

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Section: Detection Of Substances In Watermentioning

confidence: 99%

Electronic Tongues and Noses: A General Overview

Tibaduiza,

Anaya,

Gómez

et al. 2024

Biosensors

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show abstract

“…This fabrication method is especially explored to produce physical, chemical, and electrochemical sensors. 30,32,33 One of the challenges presented by the pencil-drawing technique is to guarantee good reproducibility in the transfer of the graphite layers, which can impact the film's conductivity and the overall performance of the electrode. Also, the transfer of the graphite film in a manual way (hand drawing) is highly dependent on the pressure applied.…”

Section: Graphite Characteristicsmentioning

confidence: 99%

Review—A Pencil Drawing Overview: From Graphite to Electrochemical Sensors/Biosensors Applications

Ataide

Arantes

Mendes

et al. 2022

J. Electrochem. Soc.

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Much effort was made in the literature to put pencil-drawing as an attractive way to fabricate electrochemical sensors. The pencil drawing is one of the easiest, cost-effective, unique single-step methods for fabricating paper-based devices without necessitating any expensive instrumentation by merely transferring the carbon by exfoliation abrasion on paper. Additionally, much has to be done to improve the quality of this transfer carbon to achieve a suitable sensing platform for electrochemical measurements. Hence, this review article aims to bring the new approaches developed in the last five years to circumvent some fabrication problems and enhance this “carbonization” step for electrochemistry detection using paper-based sensors. We will start the discussion on the carbon materials moving to the substrate and finally discuss the strategies to produce the conductive tracks and enhance their properties for sensing applications.

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“…Regression prediction based on multi-sensor fusion technology has been widely used in other industries, such as the remaining life prediction of aviation engines (Li et al, 2022b), tool wear prediction (Meng et al, 2021), air pollution level prediction (Ari and Alagoz, 2022), and wheel odometry prediction (Zhu et al, 2021). Kirsanov et al (2021) used PLSR to relate the sensor signals to the values of different water quality parameters, which enabled the accurate detection of various water quality parameters. Li et al (2019) has applied SVM in multi-sensor fusion to assess green tea quality accurately.…”

Section: Introductionmentioning

confidence: 99%

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

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To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

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A Pencil-Drawn Electronic Tongue for Environmental Applications

Cited by 9 publications

References 49 publications

Electronic Tongues and Noses: A General Overview

Electronic Tongues and Noses: A General Overview

Review—A Pencil Drawing Overview: From Graphite to Electrochemical Sensors/Biosensors Applications

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

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