The DOI matches its Crossref record.
Nanoscale magnetic resonance imaging
Open accessAbstract
We have combined ultrasensitive magnetic resonance force microscopy (MRFM) with 3D image reconstruction to achieve magnetic resonance imaging (MRI) with resolution <10 nm. The image reconstruction converts measured magnetic force data into a 3D map of nuclear spin density, taking advantage of the unique characteristics of the “resonant slice” that is projected outward from a nanoscale magnetic tip. The basic principles are demonstrated by imaging the 1 H spin density within individual tobacco mosaic virus particles sitting on a nanometer-thick layer of adsorbed hydrocarbons. This result, which represents a 100 million-fold improvement in volume resolution over conventional MRI, demonstrates the potential of MRFM as a tool for 3D, elementally selective imaging on the nanometer scale.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Degen, C. L., Reinhard, F., & Cappellaro, P. (2017) · Reviews of Modern Physics · doi:10.1103/revmodphys.89.035002 · Cited by 4,045
Self-citationImage fusion meets deep learning: A survey and perspective
Zhang, H., Xu, H., Tian, X., Jiang, J., & Ma, J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.008 · Cited by 834
Optical magnetic imaging of living cells
Le Sage, D., Arai, K., Glenn, D. R., DeVience, S. J., Pham, L. M., Rahn-Lee, L., Lukin, M. D., Yacoby, A., Komeili, A., & Walsworth, R. L. (2013) · Nature · doi:10.1038/nature12072 · Cited by 730
Zeptonewton force sensing with nanospheres in an optical lattice
Ranjit, G., Cunningham, M., Casey, K., & Geraci, A. A. (2016) · Physical Review A · doi:10.1103/physreva.93.053801 · Cited by 334
High sensitivity magnetic imaging using an array of spins in diamond
Steinert, S., Dolde, F., Neumann, P., Aird, A., Naydenov, B., Balasubramanian, G., Jelezko, F., & Wrachtrup, J. (2010) · Review of Scientific Instruments · doi:10.1063/1.3385689 · Cited by 297
Metadata
- Authors
- Degen, C. L., Poggio, M., Mamin, H. J., Rettner, C. T., & Rugar, D.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-02-03
- Volume
- 106
- Issue
- 5
- Pages
- 1313-1317
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2008964631
- Cited by
- 499
- References
- 0
- FWCI
- 23.79
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Nanoscale magnetic resonance imaging. https://app.cochat.ai/papers/10.1073/pnas.0812068106
- The paper
- Degen, C. L., Poggio, M., Mamin, H. J., Rettner, C. T., & Rugar, D. (2009). Nanoscale magnetic resonance imaging. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0812068106
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.