Kinetic Insights Into Carbon Nanotube-Based Neurotransmitter Sensors by Single Molecule Experiments

Single-walled carbon nanotubes (SWCNTs) are powerful building blocks for near-infrared (NIR) fluorescent biosensors. They can be chemically tailored to detect specific biomolecules. Their performance in (bio)imaging applications depends on how fast analytes bind orunbind and change their fluorescence, but measuring fast kinetics remains a challenge. Here, we demonstrate single-molecule detection by single SWCNT-based nanosensors and measure their rate constants. We use fluorescence microscopy (>900 nm) to image DNA functionalized (6,5)-SWCNTs, which detect the neurotransmitters dopamine and epinephrine as well as the interfering molecule ascorbic acid. By analyzing fluorescence traces of these sensors, we directly observe binding and unbinding events of individual analyte molecules. Hidden Markov modeling allows us to obtain dwell times and consequently rate constants (<i>k</i><sub>off</sub>). Debinding is best described by two exponential first order kinetics with <i>k</i><sub>off,fast</sub> = 0.40 to 0.71 s<sup>-1</sup> and <i>k</i><sub>off,slow</sub> = 0.01 to 0.10 s<sup>-1</sup> depending on DNA sequence and analyte. Additionally, we find that sensitivity is correlated with sensor brightness and darker sensors have faster off-rates. We also show that differences in rate constants can be exploited for kinetically improved sensor selectivity (KISS) beyond the thermodynamic limit. Overall, we provide fundamental insights into the kinetics and mechanism of SWCNT-based sensors and propose a concept for kinetic selectivity.

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Publication Details

Journal
ACS Nano
Published
2025-12-25
DOI
https://doi.org/10.1021/acsnano.5c15290
Primary Topic
Carbon Nanotubes in Composites
Type
article
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article

Kinetic Insights Into Carbon Nanotube-Based Neurotransmitter Sensors by Single Molecule Experiments

Juliana Gretz, B. Hill, Sebastian Kruss, Chen Ma et al.
ACS Nano
Carbon Nanotubes in Composites
article

Kinetic Insights Into Carbon Nanotube-Based Neurotransmitter Sensors by Single Molecule Experiments

Juliana Gretz, B. Hill, Sebastian Kruss, Chen Ma, C. Alexander Schrage
article en

Abstract

Single-walled carbon nanotubes (SWCNTs) are powerful building blocks for near-infrared (NIR) fluorescent biosensors. They can be chemically tailored to detect specific biomolecules. Their performance in (bio)imaging applications depends on how fast analytes bind orunbind and change their fluorescence, but measuring fast kinetics remains a challenge. Here, we demonstrate single-molecule detection by single SWCNT-based nanosensors and measure their rate constants. We use fluorescence microscopy (>900 nm) to image DNA functionalized (6,5)-SWCNTs, which detect the neurotransmitters dopamine and epinephrine as well as the interfering molecule ascorbic acid. By analyzing fluorescence traces of these sensors, we directly observe binding and unbinding events of individual analyte molecules. Hidden Markov modeling allows us to obtain dwell times and consequently rate constants (<i>k</i><sub>off</sub>). Debinding is best described by two exponential first order kinetics with <i>k</i><sub>off,fast</sub> = 0.40 to 0.71 s<sup>-1</sup> and <i>k</i><sub>off,slow</sub> = 0.01 to 0.10 s<sup>-1</sup> depending on DNA sequence and analyte. Additionally, we find that sensitivity is correlated with sensor brightness and darker sensors have faster off-rates. We also show that differences in rate constants can be exploited for kinetically improved sensor selectivity (KISS) beyond the thermodynamic limit. Overall, we provide fundamental insights into the kinetics and mechanism of SWCNT-based sensors and propose a concept for kinetic selectivity.

ACS NanoVol. 20(1)
Fraunhofer Institute for Microelectronic Circuits and Systems (DE), Ruhr University Bochum (DE)
Openalex Percentile: Top 48%
Carbon Nanotubes in Composites
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Kinetic Insights Into Carbon Nanotube-Based Neurotransmitter Sensors by Single Molecule Experiments — Juliana Gretz, B. Hill, et al. · ACS Nano (2025) | TGRS Research Map | TGRS