Evidence map›Paper›PMID 36278556›Full record

ReviewBiotech (Basel (Switzerland))2022

A Review of Recent Advances in Microbial Fuel Cells: Preparation, Operation, and Application.

Jianfei Wang, Kexin Ren, Yan Zhu, Jiaqi Huang, Shijie Liu

Abstract readReview
In one paragraph

Review in Biotech (Basel (Switzerland)), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jianfei WangDepartment of Chemical Engineering, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA.ORCID 0000-0001-9259-1642
Kexin RenDepartment of Chemical Engineering, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA.
Yan ZhuDepartment of Chemical Engineering, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA.
Jiaqi HuangDepartment of Chemical Engineering, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA.
Shijie LiuDepartment of Chemical Engineering, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The microbial fuel cell has been considered a promising alternative to traditional fossil energy. It has great potential in energy production, waste management, and biomass valorization. However, it has several technical issues, such as low power generation efficiency and operational stability. These issues limit the scale-up and commercialization of MFC systems. This review presents the latest progress in microbial community selection and genetic engineering techniques for enhancing microbial electricity production. The summary of substrate selection covers defined substrates and some inexpensive complex substrates, such as wastewater and lignocellulosic biomass materials. In addition, it also includes electrode modification, electron transfer mediator selection, and optimization of operating conditions. The applications of MFC systems introduced in this review involve wastewater treatment, production of value-added products, and biosensors. This review focuses on the crucial process of microbial fuel cells from preparation to application and provides an outlook for their future development.

Indexed as

electrode modificationgenetic engineeringlignocellulosic biomassmicrobial fuel cellsvalue-added productswastewater treatment

Identifiers

PMID36278556
PMCPMC9589990

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.