Evidence map›Paper›PMID 33673142›Full record

ReviewMicromachines2021

Recent Advances in Ammonia Gas Sensors Based on Carbon Nanomaterials.

Alexander G Bannov, Maxim V Popov, Andrei E Brester, Pavel B Kurmashov

Abstract readReview
In one paragraph

Review in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. MXene-Based Gas Sensors for NHMicromachines · 2025
    Review
  5. High-Performance NHACS omega · 2025
    Article
  6. Review
  7. Advanced Materials for NHNano-micro letters · 2024
    Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Exploitation of SnOSensors (Basel, Switzerland) · 2022
    Article
  14. NOMicromachines · 2022
    Article
  15. Article
  16. Review
  17. Article
  18. 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

4 authors.

Alexander G BannovDepartment of Chemistry and Chemical Engineering, Novosibirsk State Technical University, 630073 Novosibirsk, Russia.ORCID 0000-0001-5868-9013
Maxim V PopovDepartment of Chemistry and Chemical Engineering, Novosibirsk State Technical University, 630073 Novosibirsk, Russia.
Andrei E BresterDepartment of Chemistry and Chemical Engineering, Novosibirsk State Technical University, 630073 Novosibirsk, Russia.
Pavel B KurmashovDepartment of Chemistry and Chemical Engineering, Novosibirsk State Technical University, 630073 Novosibirsk, Russia.

Funding

Ministry of Science and Higher Education of Russia FSUN-2020-0008
6 · The paper itself

Abstract

This review paper is devoted to an extended analysis of ammonia gas sensors based on carbon nanomaterials. It provides a detailed comparison of various types of active materials used for the detection of ammonia, e.g., carbon nanotubes, carbon nanofibers, graphene, graphene oxide, and related materials. Different parameters that can affect the performance of chemiresistive gas sensors are discussed. The paper also gives a comparison of the sensing characteristics (response, response time, recovery time, operating temperature) of gas sensors based on carbon nanomaterials. The results of our tests on ammonia gas sensors using various techniques are analyzed. The problems related to the recovery of sensors using various approaches are also considered. Finally, the impact of relative humidity on the sensing behavior of carbon nanomaterials of various different natures was estimated.

Indexed as

ammoniacarbon nanotubeschemiresistive sensorgas sensorgraphenegraphene oxideresponsesensitivity

Identifiers

PMID33673142
PMCPMC7918724

What OpenQuestion holds

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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.