Evidence map›Paper›PMID 35062605›Full record

ArticleSensors (Basel, Switzerland)2022

A Novel Two-Axis Differential Resonant Accelerometer Based on Graphene with Transmission Beams.

Yang Xiao, Feng Hu, Yuchen Zhang, Jiaxing Zheng, Shiqiao Qin

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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 99% 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

5 authors at 1 institution in 1 country.

Yang XiaoCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Feng HuCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Yuchen ZhangCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Jiaxing ZhengCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Shiqiao QinCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
National University of Defense Technology · CN

Funding

National Natural Science Foundation of China 51705528
6 · The paper itself

Abstract

In this paper, a novel two-axis differential resonant accelerometer based on graphene with transmission beams is presented. This accelerometer can not only reduce the cross sensitivity, but also overcome the influence of gravity, realizing fast and accurate measurement of the direction and magnitude of acceleration on the horizontal plane. The simulation results show that the critical buckling acceleration is 460 g, the linear range is 0-89 g, while the differential sensitivity is 50,919 Hz/g, which is generally higher than that of the resonant accelerometer reported previously. Thus, the accelerometer belongs to the ultra-high sensitivity accelerometer. In addition, increasing the length and tension of graphene can obviously increase the critical linear acceleration and critical buckling acceleration with the decreasing sensitivity of the accelerometer. Additionally, the size change of the force transfer structure can significantly affect the detection performance. As the etching accuracy reaches the order of 100 nm, the critical buckling acceleration can reach up to 5 × 10

Indexed as

biaxial accelerometerdifferential modefinite element simulationgraphene resonant

Identifiers

PMID35062605
PMCPMC8777584
OpenAlexW4205201013

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.