Evidence map›Paper›PMID 36772247›Full record

ArticleSensors (Basel, Switzerland)2023

Investigating the Potential of Thin Silicon Nitride Membranes in Fiber-Based Photoacoustic Sensing.

Yorick Konijn, Edcel Salumbides, B Imran Akca

Open access · goldAbstract 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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 100% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Yorick KonijnLaserLab, Department of Physics and Astronomy, VU University, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
Edcel SalumbidesLaserLab, Department of Physics and Astronomy, VU University, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
B Imran AkcaLaserLab, Department of Physics and Astronomy, VU University, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.ORCID 0000-0002-6983-3460
Vrije Universiteit Amsterdam · NL

Funding

Dutch Research Council 17743
6 · The paper itself

Abstract

The detection of methane, a strong greenhouse gas, has increased in importance due to rising emissions, which partly originate from unreported and undetected leaks in oil and gas fields. The gas emitted by these leaks could be detected using an optical fiber-based photoacoustic sensor called PAS-WRAP. Here, we investigate the potential of silicon-based membranes as more sensitive microphones in the PAS-WRAP concept. Toward this goal, we built a setup with which the frequency response of the membranes was interrogated by an optical fiber. Multiple mounting mechanisms were tested by adapting commercial interferometry systems (OP1550, ZonaSens, Optics11 B.V.) to our case. Finally, methane detection was attempted using a silicon nitride membrane as a sensor. Our findings show a quality factor of 2.4 at 46 kHz and 33.6 at 168 kHz for a thin silicon nitride membrane. This membrane had a frequency response with a signal-to-background ratio of 1 ± 0.7 at 44 kHz when tested in a vacuum chamber with 4% methane at 0.94 bar. The signal-to-background ratio was not significant for methane detection; however, we believe that the methods and experimental procedures that we used in this work can provide a useful reference for future research into gas trace detection with optical fiber-based photoacoustic spectroscopy.

Indexed as

fiber opticsphotoacoustic sensingsilicon nitride membrane

Identifiers

PMID36772247
PMCPMC9921159
OpenAlexW4317538450

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.