Evidence map›Paper›PMID 36772491›Full record

ArticleSensors (Basel, Switzerland)2023

Gas Sensor Based on Lossy Mode Resonances by Means of Thin Graphene Oxide Films Fabricated onto Planar Coverslips.

Ignacio Vitoria, Elieser E Gallego, Sonia Melendi-Espina, Miguel Hernaez, Carlos Ruiz Zamarreño, Ignacio R Matías

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Ignacio VitoriaElectrical, Electronic and Communications Engineering Department, Public University of Navarre, 31006 Pamplona, Spain.
Elieser E GallegoElectrical, Electronic and Communications Engineering Department, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0002-4226-080X
Sonia Melendi-EspinaSchool of Engineering, University of East Anglia (UEA), Norwich Research Park, Norwich NR4 7TJ, UK.ORCID 0000-0002-1083-3896
Miguel HernaezSchool of Engineering, University of East Anglia (UEA), Norwich Research Park, Norwich NR4 7TJ, UK.ORCID 0000-0001-7878-4704
Carlos Ruiz ZamarreñoElectrical, Electronic and Communications Engineering Department, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0001-6601-5449
Ignacio R MatíasElectrical, Electronic and Communications Engineering Department, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0002-2229-6178

Funding

Agencia Estatal de Investigación Spain PID2019-106231RB-I00European Union Horizon 2020 Research and Innovation Programme 774094Institute of Smart Cities and Public University of Navarra PhD Student grantsLeverhulme Trust Research Fellowship LTRF2021\17130
6 · The paper itself

Abstract

The use of planar waveguides has recently shown great success in the field of optical sensors based on the Lossy Mode Resonance (LMR) phenomenon. The properties of Graphene Oxide (GO) have been widely exploited in various sectors of science and technology, with promising results for gas sensing applications. This work combines both, the LMR-based sensing technology on planar waveguides and the use of a GO thin film as a sensitive coating, to monitor ethanol, water, and acetone. Experimental results on the fabrication and performance of the sensor are presented. The obtained results showed a sensitivity of 3.1, 2.0, and 0.6 pm/ppm for ethanol, water, and acetone respectively, with a linearity factor R

Indexed as

gas sensorgraphene oxideLossy Mode Resonanceoptical fiber sensor

Identifiers

PMID36772491
PMCPMC9920069

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.