Evidence map›Paper›PMID 31324044›Full record

ArticleSensors (Basel, Switzerland)2019

Stress-Insensitive Resonant Graphene Mass Sensing via Frequency Ratio.

Xing Xiao, Shang-Chun Fan, Cheng Li, Wei-Wei Xing

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Monolayer MXene Nanoelectromechanical Piezo-Resonators with 0.2 Zeptogram Mass Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. An Ultrahigh-Sensitivity Graphene Resonant Gyroscope.Nanomaterials (Basel, Switzerland) · 2021
    Article
  7. Article
  8. Applications of Graphene-Based Materials in Sensors.Sensors (Basel, Switzerland) · 2020
    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

4 authors.

Xing XiaoSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China. xiaoxing@buaa.edu.cn.
Shang-Chun FanSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China. fsc@buaa.edu.cn.
Cheng LiSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China. licheng@buaa.edu.cn.ORCID 0000-0002-7201-9005
Wei-Wei XingSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Herein, a peripherally clamped stretched square monolayer graphene sheet with a side length of 10 nm was demonstrated as a resonator for atomic-scale mass sensing via molecular dynamics (MD) simulation. Then, a novel method of mass determination using the first three resonant modes (mode11, mode21 and mode22) was developed to avoid the disturbance of stress fluctuation in graphene. MD simulation results indicate that improving the prestress in stretched graphene increases the sensitivity significantly. Unfortunately, it is difficult to determine the mass accurately by the stress-reliant fundamental frequency shift. However, the absorbed mass in the middle of graphene sheets decreases the resonant frequency of mode11 dramatically while having negligible effect on that of mode21 and mode22, which implies that the latter two frequency modes are appropriate for compensating the stress-induced frequency shift of mode11. Hence, the absorbed mass, with a resolution of 3.3 × 10

Indexed as

frequency ratiomass sensormolecular dynamics simulationresonant modestretched graphene resonator

Identifiers

PMID31324044
PMCPMC6651828

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.