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Methane Production and Conductive Materials: A Critical Review
Open accessAbstract
Conductive materials (CM) have been extensively reported to enhance methane production in anaerobic digestion processes. The occurrence of direct interspecies electron transfer (DIET) in microbial communities, as an alternative or complementary to indirect electron transfer (via hydrogen or formate), is the main explanation given to justify the improvement of methane production. Not disregarding that DIET can be promoted in the presence of certain CM, it surely does not explain all the reported observations. In fact, in methanogenic environments DIET was only unequivocally demonstrated in cocultures of Geobacter metallireducens with Methanosaeta harundinacea or Methanosarcina barkeri and frequently Geobacter sp. are not detected in improved methane production driven systems. Furthermore, conductive carbon nanotubes were shown to accelerate the activity of methanogens growing in pure cultures, where DIET is not expected to occur, and hydrogenotrophic activity is ubiquitous in full-scale anaerobic digesters treating for example brewery wastewaters, indicating that interspecies hydrogen transfer is an important electron transfer mechanism in those systems. This paper presents an overview of the effect of several iron-based and carbon-based CM in bioengineered systems, focusing on the improvement in methane production and in microbial communities' changes. Control assays, as fundamental elements to support major conclusions in reported experiments, are critically revised and discussed.
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Metadata
- Authors
- Martins, G., Salvador, A. F., Pereira, L., & Alves, M. M.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-08-17
- Volume
- 52
- Issue
- 18
- Pages
- 10241-10253
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2887786160
- Cited by
- 432
- References
- 134
- FWCI
- 32.08
- Type
- journal article
- Language
- en
- Open access
- green
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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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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.