Evidence map›Paper›PMID 42451442›Full record

ArticleSensors (Basel, Switzerland)2026

A Composite Layered Piezoelectric Pressure Sensor for Dynamic Monitoring with Enhanced Sensitivity and Temperature Adaptability.

Suyue Liu, Dazhao Zhou, Jinghua Lin, Jifang Tao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

4 authors.

Suyue LiuSchool of Information Science and Engineering, Shandong University, Qingdao 266237, China.ORCID 0009-0007-4186-1359
Dazhao ZhouKey Laboratory of Laser and Infrared System of Ministry of Education, Shandong University, Qingdao 266237, China.
Jinghua LinSchool of Information Science and Engineering, Shandong University, Qingdao 266237, China.ORCID 0009-0004-5857-758X
Jifang TaoSchool of Information Science and Engineering, Shandong University, Qingdao 266237, China.

Funding

Key R&D Program of Shandong Province 2023CXPT097
6 · The paper itself

Abstract

Piezoelectric pressure sensors for dynamic monitoring face a trade-off between charge output and measurement range, and existing high-sensitivity designs are largely confined to narrow ranges. This study presents a composite layered piezoelectric pressure sensor in which a 316L stainless-steel diaphragm drives a centrally suspended PZT-5H wafer supported by a perforated alumina gasket, with the wafer thickness and cavity radius optimized under a 10 MPa full-scale stress constraint. Over 0-10 MPa, quasi-static calibration gave a highly repeatable quadratic pressure-charge relationship (R2=0.99995) with a maximum residual below 1% FS. The sensitivity is pressure-dependent: the secant sensitivity increased monotonically from 3.16 pC/kPa at 1 MPa to 5.36 pC/kPa at 10 MPa, reflecting a stress-stiffening response rather than a measurement tolerance band. The output deviation remained within 3% from 25 °C to 150 °C. Shock-tube testing yielded a resonance of ∼50 kHz and a mutually consistent 10-90% leading-edge interval of 10.12 μs. Combining high charge sensitivity over a wide 0-10 MPa range with a fast transient response and stable operation up to 150 °C, the proposed sensor is suited to dynamic pressure-pulsation monitoring in fluid-power and thermal and power-plant fluid systems.

Indexed as

composite layered structuredynamic pressure monitoringpiezoelectric pressure sensorstructural optimizationtemperature stability

Identifiers

PMID42451442
PMCPMC13364113

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.