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Mesoporous materials as gas sensors
Abstract
Ordered mesoporous materials have great potential in the field of gas sensing. Today various template-assisted synthesis methods facilitate the preparation of silica (SiO2) as well as numerous metal oxides with well-defined, uniform and regular pore systems. The unique nanostructural properties of such materials are particularly useful for their application as active layers in gas sensors based on various operating principles, such as capacitive, resistive, or optical sensing. This review summarizes the basic aspects of materials synthesis, discusses some structural properties relevant in gas sensing, and gives an overview of the literature on ordered mesoporous gas sensors.
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Metadata
- Authors
- Wagner, T., Haffer, S., Weinberger, C., Klaus, D., & Tiemann, M.
- Venue
- Chem. Soc. Rev.
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 42
- Issue
- 9
- Pages
- 4036-4053
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- OpenAlex
- W2027542125
- Cited by
- 629
- References
- 112
- FWCI
- 1.41
- 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 ›
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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.