Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

Citation Author(s):
Simone
Aiassa
Politecnico di Torino
Sandro
Carrara
École Polytechnique Fédérale de Lausanne (EPFL)
Danilo
Demarchi
Politecnico di Torino
Submitted by:
Simone Aiassa
Last updated:
Mon, 05/27/2019 - 05:11
DOI:
10.21227/g9bn-sn96
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Abstract: 

The Voltammetry-Based Sensing (VBS) methods are extremely interesting due to high specificity in several biochemical applications. Several considerations can be applied to use this method to measure different analytes, and implement efficient and optimized electronic measurement platform for point-of-care diagnostic, in wearable, portable, or IoT systems. The dataset contains the data presented in [1], which proves on real experimental data a method to define the optimized setup to develop efficient and electronic bio-sensing platforms. Namely, the dataset contains Scan Cyclic Voltammetry (SCV) and Differential Pulse Voltammetry (DPV) waveform, obtained from APAP (Paracetamol) samples, considering different sampling rate.

[1] S. Aiassa, S. Carrara and D. Demarchi, "Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications," in IEEE Sensors Letters. doi: 10.1109/LSENS.2019.2918575.

Instructions: 

The dataset contains Scan Cyclic Voltammetry (SCV) and Differential Pulse Voltammetry (DPV) waveform, obtained from APAP (Paracetamol) samples, considering different sampling rate. The data are provided to the reader "as is", for more detail on the set-up, the chemicals and materials refer to [1].

The DownSampling.zip folder contains the following file:

  • SCV.mat contains seven cells, one per every sampling rate in the range [33,66:0,58] Hz named CV[sampling_rate]. Any of this cell is composed of seven calibration point, acquired respectively sensing sample of [0,50,100,150,200,250,300] µM of APAP. Every cell contains a matrix composed of a couple of coordinates, the first column, x-axis, is the voltage (V), and the second column, y-axis, is the current (µA). The matrix describes the full Scav Cyclic Voltammogram acquired at the given condition, every voltammogram is repeated three times to consider inter-electrode variability and avoid artefact. 

  • DPV.mat contains seven cells, one per every sampling rate in the range [16,83:0,29] Hz named DPV[sampling_rate]. Any of this cell is composed of seven calibration point, acquired respectively sensing sample of [0,50,100,150,200,250,300] µM of APAP. Every cell contains a matrix composed of a couple of coordinates, the first column, x-axis, is the voltage (V), and the second column, y-axis, is the current (µA). The matrix describes the full Differential Pulse Voltammogram acquired at the given condition, every voltammogram is repeated three times to consider inter-electrode variability and avoid artefact.

  • openfile.m helps the user to import and plot data in Matlab.

  • readme.txt contains this information.

[1] S. Aiassa, S. Carrara and D. Demarchi, "Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications," in IEEE Sensors Letters. doi: 10.1109/LSENS.2019.2918575.

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[1] Simone Aiassa, Sandro Carrara, Danilo Demarchi, "Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/g9bn-sn96. Accessed: Aug. 23, 2019.
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doi = {10.21227/g9bn-sn96},
url = {http://dx.doi.org/10.21227/g9bn-sn96},
author = {Simone Aiassa; Sandro Carrara; Danilo Demarchi },
publisher = {IEEE Dataport},
title = {Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications},
year = {2019} }
TY - DATA
T1 - Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications
AU - Simone Aiassa; Sandro Carrara; Danilo Demarchi
PY - 2019
PB - IEEE Dataport
UR - 10.21227/g9bn-sn96
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Simone Aiassa, Sandro Carrara, Danilo Demarchi. (2019). Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications. IEEE Dataport. http://dx.doi.org/10.21227/g9bn-sn96
Simone Aiassa, Sandro Carrara, Danilo Demarchi, 2019. Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications. Available at: http://dx.doi.org/10.21227/g9bn-sn96.
Simone Aiassa, Sandro Carrara, Danilo Demarchi. (2019). "Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications." Web.
1. Simone Aiassa, Sandro Carrara, Danilo Demarchi. Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/g9bn-sn96
Simone Aiassa, Sandro Carrara, Danilo Demarchi. "Supplementary Material for Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications." doi: 10.21227/g9bn-sn96