Evidence map›Paper›PMID 37763851›Full record

ReviewMicromachines2023

Biomass-Derived Carbon-Based Electrodes for Electrochemical Sensing: A Review.

Christian Onfray, Abdoulaye Thiam

Abstract readReview
In one paragraph

Review in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
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

2 authors.

Christian OnfrayPrograma Institucional de Fomento a la Investigación, Desarrollo e Innovación, Universidad Tecnológica Metropolitana, Santiago 8940577, Chile.ORCID 0000-0001-5485-9169
Abdoulaye ThiamPrograma Institucional de Fomento a la Investigación, Desarrollo e Innovación, Universidad Tecnológica Metropolitana, Santiago 8940577, Chile.ORCID 0000-0001-5024-1855

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diverse composition of biomass waste, with its varied chemical compounds of origin, holds substantial potential in developing low-cost carbon-based materials for electrochemical sensing applications across a wide range of compounds, including pharmaceuticals, dyes, and heavy metals. This review highlights the latest developments and explores the potential of these sustainable electrodes in electrochemical sensing. Using biomass sources, these electrodes offer a renewable and cost-effective route to fabricate carbon-based sensors. The carbonization process yields highly porous materials with large surface areas, providing a wide variety of functional groups and abundant active sites for analyte adsorption, thereby enhancing sensor sensitivity. The review classifies, summarizes, and analyses different treatments and synthesis of biomass-derived carbon materials from different sources, such as herbaceous, wood, animal and human wastes, and aquatic and industrial waste, used for the construction of electrochemical sensors over the last five years. Moreover, this review highlights various aspects including the source, synthesis parameters, strategies for improving their sensing activity, morphology, structure, and functional group contributions. Overall, this comprehensive review sheds light on the immense potential of biomass-derived carbon-based electrodes, encouraging further research to optimize their properties and advance their integration into practical electrochemical sensing devices.

Indexed as

biomass wastecarbon-based electrodecircular economygreen synthesislow-cost catalyst

Identifiers

PMID37763851
PMCPMC10538108

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.