Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 528

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Flexible Ferroelectric Sensors with Ultrahigh Pressure Sensitivity and Linear Response over Exceptionally Broad Pressure Range

Lee, Youngoh, Park, Jonghwa, Cho, Soowon, Shin, Young-Eun, Lee, Hochan, Kim, Jinyoung, Myoung, Jinyoung, Cho, Seungse, Kang, Saewon, Baig, Chunggi, Ko, Hyunhyub·2018·ACS Nano

Abstract

Flexible pressure sensors with a high sensitivity over a broad linear range can simplify wearable sensing systems without additional signal processing for the linear output, enabling device miniaturization and low power consumption. Here, we demonstrate a flexible ferroelectric sensor with ultrahigh pressure sensitivity and linear response over an exceptionally broad pressure range based on the material and structural design of ferroelectric composites with a multilayer interlocked microdome geometry. Due to the stress concentration between interlocked microdome arrays and increased contact area in the multilayer design, the flexible ferroelectric sensors could perceive static/dynamic pressure with high sensitivity (47.7 kPa –1, 1.3 Pa minimum detection). In addition, efficient stress distribution between stacked multilayers enables linear sensing over exceptionally broad pressure range (0.0013–353 kPa) with fast response time (20 ms) and high reliability over 5000 repetitive cycles even at an extremely high pressure of 272 kPa. Our sensor can be used to monitor diverse stimuli from a low to a high pressure range including weak gas flow, acoustic sound, wrist pulse pressure, respiration, and foot pressure with a single device.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acsnano.8b01805

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 528

Metadata

Authors
Lee, Y., Park, J., Cho, S., Shin, Y.-E., Lee, H., Kim, J., Myoung, J., Cho, S., Kang, S., Baig, C., & Ko, H.
Venue
ACS Nano
Published
2018-04-12
Volume
12
Issue
4
Pages
4045-4054
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2797836587
Cited by
528
References
47
FWCI
17.08
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.