Evidence map›Paper›PMID 38732845›Full record

ArticleSensors (Basel, Switzerland)2024

Measuring Sedimentation Profiles for Nanoparticle Characterization through a Square Spiral Resonator Sensor.

Miguel Monteagudo Honrubia, Gianluca Caposciutti, Francisco Javier Herraiz-Martínez, Javier Matanza Domingo, Bernardo Tellini, Romano Giannetti

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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
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.

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

6 authors.

Miguel Monteagudo HonrubiaInstitute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain.ORCID 0000-0001-9712-7486
Gianluca CaposciuttiDepartment of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0002-8357-0548
Francisco Javier Herraiz-MartínezInstitute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain.ORCID 0000-0001-5774-659X
Javier Matanza DomingoInstitute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain.ORCID 0000-0002-0391-1331
Bernardo TelliniDepartment of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0001-7681-8026
Romano GiannettiInstitute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain.ORCID 0000-0003-4671-6242

Funding

IIT, Comillas Pontifical University, Spain IIT Strategic PhD Research Grants and Proyectos de Investigacion Propia
6 · The paper itself

Abstract

Metallic nanoscale particles attract a growing interest in several fields, thanks to their unique bonding characteristics; applications are appearing in the literature in the fields of, for example, sensor coatings and biochemical compound detection. However, the controlled fabrication of such nanopowders is often cumbersome, especially because their characterization is normally slow, involving procedures such as electron microscopy. On the other hand, microwave sensors based on near-field effects on materials are being developed with high sensitivity and show promising characteristics. In this paper, the authors show how a microwave sensor based on a Square Spiral Resonator can be used to characterize paraffin dispersions of nanoparticles conveniently and cost-effectively.

Indexed as

metallic nanoparticlesmicrowave sensorspermittivitysedimentation

Identifiers

PMID38732845
PMCPMC11086076

What OpenQuestion holds

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Registered trials

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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.