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Cited by 375

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Single-molecule recognition imaging microscopy

Open access
Stroh, C., Wang, H., Bash, R., Ashcroft, B., Nelson, J., Gruber, H., Lohr, D., Lindsay, S. M., Hinterdorfer, P.·2004·Proceedings of the National Academy of Sciences

Abstract

Atomic force microscopy is a powerful and widely used imaging technique that can visualize single molecules and follow processes at the single-molecule level both in air and in solution. For maximum usefulness in biological applications, atomic force microscopy needs to be able to identify specific types of molecules in an image, much as fluorescent tags do for optical microscopy. The results presented here demonstrate that the highly specific antibody–antigen interaction can be used to generate single-molecule maps of specific types of molecules in a compositionally complex sample while simultaneously carrying out high-resolution topographic imaging. Because it can identify specific components, the technique can be used to map composition over an image and to detect compositional changes occurring during a process.

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Crossref · 10.1073/pnas.0403538101

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Metadata

Authors
Stroh, C., Wang, H., Bash, R., Ashcroft, B., Nelson, J., Gruber, H., Lohr, D., Lindsay, S. M., & Hinterdorfer, P.
Venue
Proceedings of the National Academy of Sciences
Published
2004-08-16
Volume
101
Issue
34
Pages
12503-12507
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2078861590
Cited by
375
References
9
FWCI
15.36
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Single-molecule recognition imaging microscopy. https://app.cochat.ai/papers/10.1073/pnas.0403538101
The paper
Stroh, C., Wang, H., Bash, R., Ashcroft, B., Nelson, J., Gruber, H., Lohr, D., Lindsay, S. M., & Hinterdorfer, P. (2004). Single-molecule recognition imaging microscopy. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0403538101

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

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.