The DOI matches its Crossref record.
Chemical Sensing Based on Catalytic Nanomotors: Motion-Based Detection of Trace Silver
Open accessAbstract
A motion-based chemical sensing involving fuel-driven nanomotors is demonstrated. The new protocol relies on the use of an optical microscope for tracking changes in the speed of nanowire motors in the presence of the target analyte. Selective and sensitive measurements of trace silver ions are illustrated based on the dramatic and specific acceleration of bimetal nanowire motors in the presence of silver. Such nanomotor-based measurements would lead to a wide range of novel and powerful chemical and biological sensing protocols.
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
The Environmental Impact of Micro/Nanomachines: A Review
Gao, W., & Wang, J. (2014) · ACS Nano · doi:10.1021/nn500077a · Cited by 594
Self-citationMoran, J. L., & Posner, J. D. (2017) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-122414-034456 · Cited by 426
Motion-based DNA detection using catalytic nanomotors
Wu, J., Balasubramanian, S., Kagan, D., Manesh, K. M., Campuzano, S., & Wang, J. (2010) · Nature Communications · doi:10.1038/ncomms1035 · Cited by 317
Self-citationPhotonic Nanosensor for Colorimetric Detection of Metal Ions
Yetisen, A. K., Montelongo, Y., Qasim, M. M., Butt, H., Wilkinson, T. D., Monteiro, M. J., & Yun, S. H. (2015) · Analytical Chemistry · doi:10.1021/ac504274q · Cited by 94
Metadata
- Authors
- Kagan, D., Calvo-Marzal, P., Balasubramanian, S., Sattayasamitsathit, S., Manesh, K. M., Flechsig, G.-U., & Wang, J.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-08-11
- Volume
- 131
- Issue
- 34
- Pages
- 12082-12083
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2039610537
- Cited by
- 300
- References
- 0
- FWCI
- 9.46
- Type
- journal article
- Language
- en
- Open access
- green
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: Chemical Sensing Based on Catalytic Nanomotors: Motion-Based Detection of Trace Silver. https://app.cochat.ai/papers/10.1021/ja905142q
- The paper
- Kagan, D., Calvo-Marzal, P., Balasubramanian, S., Sattayasamitsathit, S., Manesh, K. M., Flechsig, G.-U., & Wang, J. (2009). Chemical Sensing Based on Catalytic Nanomotors: Motion-Based Detection of Trace Silver. Journal of the American Chemical Society. https://doi.org/10.1021/ja905142q
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.