The DOI matches its Crossref record.
Ultrasensitive and highly selective gas sensors using three-dimensional tungsten oxide nanowire networks
Abstract
Three-dimensional (3D) tungsten oxide nanowire networks have been demonstrated as a high-surface area material for building ultrasensitive and highly selective gas sensors. Utilizing the 3D hierarchical structure of the networks, high sensitivity has been obtained towards NO2, revealing the capability of the material to detect concentration as low as 50ppb (parts per billion). The distinctive selectivity at different working temperatures is observed for various gases. The results highlight that the nanobelts (nanowires) technology can be adopted for the development of gas sensors with performances suitable for practical applications.
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
Hierarchical WO3 Hollow Shells: Dendrite, Sphere, Dumbbell, and Their Photocatalytic Properties
Chen, D., & Ye, J. (2008) · Advanced Functional Materials · doi:10.1002/adfm.200701468 · Cited by 560
Devices and chemical sensing applications of metal oxide nanowires
Shen, G., Chen, P.-C., Ryu, K., & Zhou, C. (2009) · J. Mater. Chem. · doi:10.1039/b816543b · Cited by 313
A Gradient Microarray Electronic Nose Based on Percolating SnO2 Nanowire Sensing Elements
Sysoev, V. V., Goschnick, J., Schneider, T., Strelcov, E., & Kolmakov, A. (2007) · Nano Letters · doi:10.1021/nl071815+ · Cited by 272
High aspect ratio β-MnO2 nanowires and sensor performance for explosive gases
Xiao, J., Liu, P., Liang, Y., Li, H. B., & Yang, G. W. (2013) · Journal of Applied Physics · doi:10.1063/1.4819218 · Cited by 45
Metadata
- Authors
- Ponzoni, A., Comini, E., Sberveglieri, G., Zhou, J., Deng, S. Z., Xu, N. S., Ding, Y., & Wang, Z. L.
- Venue
- Applied Physics Letters
- Publisher
- AIP Publishing
- Published
- 2006-05-15
- Volume
- 88
- Issue
- 20
- ISSN
- 0003-6951 (ISSN-L)
- 1077-3118
- OpenAlex
- W2029593330
- Cited by
- 427
- References
- 0
- FWCI
- 29.69
- Type
- journal article
- Language
- en
- Open access
- closed
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: Ultrasensitive and highly selective gas sensors using three-dimensional tungsten oxide nanowire networks. https://app.cochat.ai/papers/10.1063/1.2203932
- The paper
- Ponzoni, A., Comini, E., Sberveglieri, G., Zhou, J., Deng, S. Z., Xu, N. S., Ding, Y., & Wang, Z. L. (2006). Ultrasensitive and highly selective gas sensors using three-dimensional tungsten oxide nanowire networks. Applied Physics Letters. https://doi.org/10.1063/1.2203932
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.
