Rigorous Benchmarking of Continuous Dry-Printed CNT Networks as High-Performance Electrochemical Sensors
- 1CANATU Finland Oy , Tiilenlyöjänkuja 9 A , ,
Abstract
In this study, we present a systematic evaluation of the electrochemical performance of floating catalyst chemical vapor deposited carbon nanotubes (FCCVD-CNTs) and compare their performance against conventional carbon, gold, and platinum electrodes. Differential pulse voltammetry and cyclic voltammetry were employed to detect well-established benchmark redox species, such as methylene blue, 3,3′,5,5′-tetramethylbenzidine (TMB), and acetaminophen (commonly known as paracetamol), in both buffer and human plasma, serving as proof-of-concept for the proposed approach. The FCCVD-CNTs achieved superior electrochemical sensor performance metrics, such as their signal-to-noise ratio, signal-to-background ratio, and detection limit, highlighting their potential as a novel material to redefine sensing standards in complex biological environments. Recognizing the challenges associated with processing an electrode material into a scalable, robust, high-performance, ready-to-use platform, particularly in the form of a miniaturized electrochemical cell such as a screen-printed electrode (SPE), we benchmarked our FCCVD-CNT-based dry-printed electrode (DPE) against commercially available, ink processed SPEs to assess its practical suitability for biosensing applications. The obtained results corroborate the capability of the CNT DPEs as a robust alternative for biosensing applications in a clinically representative matrix.
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BibTeX
@article{gustafsson2026rigorous,
title = {{Rigorous Benchmarking of Continuous Dry-Printed CNT Networks as High-Performance Electrochemical Sensors}},
author = {Eero Gustafsson and Laura Ferrer Pascual and Olli Kokko and Narender Kumar Joon and Marta Maria Pereira Silva Neves and Mohamed Elfadil and Ahmed Soliman and Ilkka Varjos},
journal = {ACS Omega},
year = {2026},
month = nov,
doi = {10.1021/acsomega.6c00484},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c00484/5466080/Rigorous-Benchmarking-of-Continuous-Dry-Printed},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Rigorous Benchmarking of Continuous Dry-Printed CNT Networks as High-Performance Electrochemical Sensors
AU - Eero Gustafsson
AU - Laura Ferrer Pascual
AU - Olli Kokko
AU - Narender Kumar Joon
AU - Marta Maria Pereira Silva Neves
AU - Mohamed Elfadil
AU - Ahmed Soliman
AU - Ilkka Varjos
PY - 2026
DA - 2026/11/06
JO - ACS Omega
DO - 10.1021/acsomega.6c00484
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c00484/5466080/Rigorous-Benchmarking-of-Continuous-Dry-Printed
AB - In this study, we present a systematic evaluation of the electrochemical performance of floating catalyst chemical vapor deposited carbon nanotubes (FCCVD-CNTs) and compare their performance against conventional carbon, gold, and platinum electrodes. Differential pulse voltammetry and cyclic voltammetry were employed to detect well-established benchmark redox species, such as methylene blue, 3,3′,5,5′-tetramethylbenzidine (TMB), and acetaminophen (commonly known as paracetamol), in both buffer and human plasma, serving as proof-of-concept for the proposed approach. The FCCVD-CNTs achieved superior electrochemical sensor performance metrics, such as their signal-to-noise ratio, signal-to-background ratio, and detection limit, highlighting their potential as a novel material to redefine sensing standards in complex biological environments. Recognizing the challenges associated with processing an electrode material into a scalable, robust, high-performance, ready-to-use platform, particularly in the form of a miniaturized electrochemical cell such as a screen-printed electrode (SPE), we benchmarked our FCCVD-CNT-based dry-printed electrode (DPE) against commercially available, ink processed SPEs to assess its practical suitability for biosensing applications. The obtained results corroborate the capability of the CNT DPEs as a robust alternative for biosensing applications in a clinically representative matrix.
SN - 2470-1343
ER -