Voltammetric Electronic Tongue Based on Carbon Paste Electrodes Modified with Biochar for Phenolic Compounds Stripping Detection

Kalinke, Cristiane; Oliveira, Paulo Roberto de; Emeterio, Marta Bonet San; González‐Calabuig, Andreu; Valle, Manel del; Mangrich, Antônio S.; Marcolino‐Júnior, Luiz H.; Bergamini, Márcio F.

doi:10.1002/elan.201900072

Cited by 31 publications

(8 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, the accuracy of this prediction was slightly lower than that observed using random forest (R 2 = 0.996, MSE = 0.0012). Previous studies [28,29] showed that the use of FNN as a data regression method in the development of sensors based on electrochemical measurements provided prediction results with high precision. However, to the best of the authors' knowledge, this is the first study to predict the concentration of a compound using key features from multimodal data (ECL imaging and amperograms) into a single FNN.…”

Section: Feedforward Neural Network (Fnn) Prediction Resultsmentioning

confidence: 99%

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Rivera

Swerdlow

Summerscales

et al. 2020

Sensors

View full text Add to dashboard Cite

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

show abstract

Section: Feedforward Neural Network (Fnn) Prediction Resultsmentioning

confidence: 99%

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Rivera

Swerdlow

Summerscales

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For both electrochemical techniques a typical three electrode circuit was used. In the used assembly it can be found: the working electrode based on a graphite epoxy composite (electrode already optimized in our laboratory [ 39 , 40 , 41 ]), an Ag/AgCl (0.1 mol·L −1 KCl) electrode as a reference and a Pt electrode as a counter. All measurements were carried out at room temperature without stirring using as an electrolyte solution phosphate buffer 50 mmol·L −1 + KCl 100 mmol·L −1 at pH 7.4.…”

Section: Methodsmentioning

confidence: 99%

Graphene for the Building of Electroanalytical Enzyme-Based Biosensors. Application to the Inhibitory Detection of Emerging Pollutants

2021

Self Cite

View full text Add to dashboard Cite

Graphene and its derivates offer a wide range of possibilities in the electroanalysis field, mainly owing to their biocompatibility, low-cost, and easy tuning. This work reports the development of an enzymatic biosensor using reduced graphene oxide (RGO) as a key nanomaterial for the detection of contaminants of emerging concern (CECs). RGO was obtained from the electrochemical reduction of graphene oxide (GO), an intermediate previously synthesized in the laboratory by a wet chemistry top-down approach. The extensive characterization of this material was carried out to evaluate its proper inclusion in the biosensor arrangement. The results demonstrated the presence of GO or RGO and their correct integration on the sensor surface. The detection of CECs was carried out by modifying the graphene platform with a laccase enzyme, turning the sensor into a more selective and sensitive device. Laccase was linked covalently to RGO using the remaining carboxylic groups of the reduction step and the carbodiimide reaction. After the calibration and characterization of the biosensor versus catechol, a standard laccase substrate, EDTA and benzoic acid were detected satisfactorily as inhibiting agents of the enzyme catalysis obtaining inhibition constants for EDTA and benzoic acid of 25 and 17 mmol·L−1, respectively, and a maximum inhibition percentage of the 25% for the EDTA and 60% for the benzoic acid.

show abstract

“…The reason is based not only on the procedure of the preparation, but also on the fact that the demand of the mixed modifier in the preparation of carbon-paste electrodes is easier and lower than that of screen-printed electrodes. Kalinke et al, have carried out several works regarding the preparation of carbon-paste electrodes by mixing biochar as the modifier [ 33 , 34 ]. For example, their recent work involved the preparation of biochar by pyrolysis of a castor cake waste biomass at 400 °C [ 34 ].…”

Section: The Role Of Biochar In the Fabrication Of Electrochemical Se...mentioning

confidence: 99%

Recent Advances of Biochar-Based Electrochemical Sensors and Biosensors

Wang

et al. 2022

Biosensors

View full text Add to dashboard Cite

In the context of accelerating the global realization of carbon peaking and carbon neutralization, biochar produced from biomass feedstock via a pyrolysis process has been more and more focused on by people from various fields. Biochar is a carbon-rich material with good properties that could be used as a carrier, a catalyst, and an absorbent. Such properties have made biochar a good candidate as a base material in the fabrication of electrochemical sensors or biosensors, like carbon nanotube and graphene. However, the study of the applications of biochar in electrochemical sensing technology is just beginning; there are still many challenges to be conquered. In order to better carry out this research, we reviewed almost all of the recent papers published in the past 5 years on biochar-based electrochemical sensors and biosensors. This review is different from the previously published review papers, in which the types of biomass feedstock, the preparation methods, and the characteristics of biochar were mainly discussed. First, the role of biochar in the fabrication of electrochemical sensors and biosensors is summarized. Then, the analytes determined by means of biochar-based electrochemical sensors and biosensors are discussed. Finally, the perspectives and challenges in applying biochar in electrochemical sensors and biosensors are provided.

show abstract

Voltammetric Electronic Tongue Based on Carbon Paste Electrodes Modified with Biochar for Phenolic Compounds Stripping Detection

Cited by 31 publications

References 62 publications

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Graphene for the Building of Electroanalytical Enzyme-Based Biosensors. Application to the Inhibitory Detection of Emerging Pollutants

Recent Advances of Biochar-Based Electrochemical Sensors and Biosensors

Contact Info

Product

Resources

About