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Cited by 280

The DOI matches its Crossref record; 223 of the 223 references it cites were found in a registry or an index.

Biosensing with Fluorescent Carbon Nanotubes

Open access
Ackermann, Julia, Metternich, Justus T., Herbertz, Svenja, Kruss, Sebastian·2022·Angewandte Chemie International Edition

Abstract

Biosensors are powerful tools for modern basic research and biomedical diagnostics. Their development requires substantial input from the chemical sciences. Sensors or probes with an optical readout, such as fluorescence, offer rapid, minimally invasive sensing of analytes with high spatial and temporal resolution. The near‐infrared (NIR) region is beneficial because of the reduced background and scattering of biological samples (tissue transparency window) in this range. In this context, single‐walled carbon nanotubes (SWCNTs) have emerged as versatile NIR fluorescent building blocks for biosensors. Here, we provide an overview of advances in SWCNT‐based NIR fluorescent molecular sensors. We focus on chemical design strategies for diverse analytes and summarize insights into the photophysics and molecular recognition. Furthermore, different application areas are discussed—from chemical imaging of cellular systems and diagnostics to in vivo applications and perspectives for the future.

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Metadata

Authors
Ackermann, J., Metternich, J. T., Herbertz, S., & Kruss, S.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2022-03
Volume
61
Issue
18
Pages
e202112372-e202112372
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W4205901195
Cited by
280
References
223
FWCI
14.74
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Biosensing with Fluorescent Carbon Nanotubes. https://app.cochat.ai/papers/10.1002/anie.202112372
The paper
Ackermann, J., Metternich, J. T., Herbertz, S., & Kruss, S. (2022). Biosensing with Fluorescent Carbon Nanotubes. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.202112372

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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