Evidence map›Paper›PMID 34471855›Full record

ReviewiScience2021

Electrosynthesis, modulation, and self-driven electroseparation in microbial fuel cells.

Iwona Gajda, Jiseon You, Buddhi Arjuna Mendis, John Greenman, Ioannis A Ieropoulos

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. RSC advances · 2025
    Article
  2. Article
  3. Article
  4. Reconstruction of a Genome-Scale Metabolic Network forFrontiers in bioengineering and biotechnology · 2022
    Article
  5. Article
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.

Iwona GajdaBristol BioEnergy Centre, Bristol Robotics Laboratory, T-Block, Frenchay Campus, University of the West of England (UWE Bristol), Bristol BS16 1QY, UK.
Jiseon YouBristol BioEnergy Centre, Bristol Robotics Laboratory, T-Block, Frenchay Campus, University of the West of England (UWE Bristol), Bristol BS16 1QY, UK.
Buddhi Arjuna MendisBristol BioEnergy Centre, Bristol Robotics Laboratory, T-Block, Frenchay Campus, University of the West of England (UWE Bristol), Bristol BS16 1QY, UK.
John GreenmanBristol BioEnergy Centre, Bristol Robotics Laboratory, T-Block, Frenchay Campus, University of the West of England (UWE Bristol), Bristol BS16 1QY, UK.
Ioannis A IeropoulosBristol BioEnergy Centre, Bristol Robotics Laboratory, T-Block, Frenchay Campus, University of the West of England (UWE Bristol), Bristol BS16 1QY, UK.

Funding

Gates Foundation INV-006499
6 · The paper itself

Abstract

Microbial electrosynthesis (MES) represents a sustainable platform that converts waste into resources, using microorganisms within an electrochemical cell. Traditionally, MES refers to the oxidation/reduction of a reactant at the electrode surface with externally applied potential bias. However, microbial fuel cells (MFCs) generate electrons that can drive electrochemical reactions at otherwise unbiased electrodes. Electrosynthesis in MFCs is driven by microbial oxidation of organic matter releasing electrons that force the migration of cationic species to the cathode. Here, we explore how electrosynthesis can coexist within electricity-producing MFCs thanks to electro-separation of cations, electroosmotic drag, and oxygen reduction within appropriately designed systems. More importantly, we report on a novel method of

Indexed as

ChemistryElectrochemical energy conversionElectrochemical energy productionElectrochemistryEngineering

Identifiers

PMID34471855
PMCPMC8390849

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.