Evidence map›Paper›PMID 41194419›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Ultra-Sensitive Dual-Resonator Graphene Pressure Sensor with Temperature Self-Compensation.

Zhen Wan, Cheng Li, Zhengwei Wu, Pengcheng Zhao, Yang Liu, Wanting Li, Shangchun Fan, Wei Jin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. 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. Ultra-Sensitive Dual-Resonator Graphene Pressure Sensor with Temperature Self-Compensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    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

8 authors.

Zhen WanSchool of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
Cheng LiSchool of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.ORCID https://orcid.org/0000-0002-7201-9005
Zhengwei WuAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China.
Pengcheng ZhaoLeibniz Institute of Photonic Technology, 07745, Jena, Germany.
Yang LiuSchool of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
Wanting LiSchool of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
Shangchun FanSchool of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100191, China.
Wei JinDepartment of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, Kowloon, 999077, China.ORCID https://orcid.org/0000-0003-4552-5890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Silicon resonator sensors have limitations in detecting small pressure changes due to their structural dimensions. Graphene nanomechanical resonators, with their ultra-small thickness and excellent mechanical properties, offer the opportunity to break this limitation. Here, a highly sensitive graphene nanomechanical pressure sensor with integrated temperature self-compensation is reported. It consists of two vacuum anode-bonded graphene resonators: one is sensitive to pressure and temperature while the other to temperature only, allowing for the cancellation of thermal effects via detecting the difference in the resonant frequencies. A sensitivity of 24.1 kHz kPa

Indexed as

dual‐resonatorgraphene pressure sensortemperature self‐compensationultra‐sensitive

Identifiers

PMID41194419
PMCPMC12850379

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.