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More Sustainable Chemical Activation Strategies for the Production of Porous Carbons
Open accessAbstract
The preparation of porous carbons attracts a great deal of attention given the importance of these materials in many emerging applications, such as hydrogen storage, CO 2 capture, and energy storage in supercapacitors and batteries. In particular, porous carbons produced by applying chemical activation methods are preferred because of the high pore development achieved. However, given the environmental risks associated with conventional activating agents such as KOH, the development of greener chemical activation methodologies is an important objective. This Review summarizes recent progress in the production of porous carbons by using more sustainable strategies based on chemical activation. The use of less‐corrosive chemical agents as an alternative to KOH is thoroughly reviewed. In addition, progress achieved to date by using emerging self‐activation methodologies applied to organic salts and biomass products is also discussed.
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Metadata
- Authors
- Sevilla, M., Díez, N., & Fuertes, A. B.
- Venue
- ChemSusChem
- Publisher
- Wiley
- Published
- 2020-10-23
- Volume
- 14
- Issue
- 1
- Pages
- 94-117
- ISSN
- 1864-5631 (ISSN-L)
- 1864-564X
- OpenAlex
- W3092115245
- Cited by
- 371
- References
- 145
- FWCI
- 20.57
- 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.
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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.