Evidence map›Paper›PMID 34640644›Full record

ArticleSensors (Basel, Switzerland)2021

Thermal Characterization of New 3D-Printed Bendable, Coplanar Capacitive Sensors.

Mattia Alessandro Ragolia, Anna M L Lanzolla, Gianluca Percoco, Gianni Stano, Attilio Di Nisio

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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. 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

5 authors.

Mattia Alessandro RagoliaDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.ORCID 0000-0003-0990-8384
Anna M L LanzollaDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Gianluca PercocoDepartment of Mechanics, Mathematics and Management, Polytechnic University of Bari, 70126 Bari, Italy.
Gianni StanoDepartment of Mechanics, Mathematics and Management, Polytechnic University of Bari, 70126 Bari, Italy.ORCID 0000-0002-6676-8472
Attilio Di NisioDepartment of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.ORCID 0000-0002-4166-7755

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper a new low-cost stretchable coplanar capacitive sensor for liquid level sensing is presented. It has been 3D-printed by employing commercial thermoplastic polyurethane (TPU) and conductive materials and using a fused filament fabrication (FFF) process for monolithic fabrication. The sensor presents high linearity and good repeatability when measuring sunflower oil level. Experiments were performed to analyse the behaviour of the developed sensor when applying bending stimuli, in order to verify its flexibility, and a thermal characterization was performed in the temperature range from 10 °C to 40 °C to evaluate its effect on sunflower oil level measurement. The experimental results showed negligible sensitivity of the sensor to bending stimuli, whereas the thermal characterization produced a model describing the relationship between capacitance, temperature, and oil level, allowing temperature compensation in oil level measurement. The different temperature cycles allowed to quantify the main sources of uncertainty, and their effect on level measurement was evaluated.

Indexed as

PolyurethanesPrinting, Three-DimensionalElectric CapacitanceElectric ConductivityTemperaturePolyurethanesadditive manufacturingcapacitive level sensorsconductive filamentsflexible sensorsfused filament fabricationthermal characterization

Identifiers

PMID34640644
PMCPMC8512386

What OpenQuestion holds

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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.