Evidence map›Paper›PMID 42446152›Full record

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

Strategy of Triple-Gradient in Binary Pixels for Flexible Pressure Sensing with High Sensitivity and Wide-Range Linearity.

Yifan Liu, Xiao Xu, Fengming Hu, Yuanzhe Liang, Zidong Yin, Biao Qi, Ruolin Liu, Ziyi Dai, Jianyi Luo, Bingpu Zhou

Abstract read
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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. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yifan LiuJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Xiao XuSchool of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Fengming HuJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Yuanzhe LiangJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Zidong YinJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Biao QiJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.
Ruolin LiuShanghai Key Lab. of D&A for Metal-Functional Materials, School of Materials Science & Engineering, Tongji University, Shanghai, China.
Ziyi DaiSchool of Integrated Circuits, Shandong University, Jinan, China.
Jianyi LuoResearch Center of Flexible Sensing Materials and Devices, School of Applied Physics and Materials, Wuyi University, Jiangmen, China.
Bingpu ZhouJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, China.ORCID https://orcid.org/0000-0003-4866-3105

Funding

Macao Centre for Research and Development in Advanced Materials 0002/2024/TFPScience and Technology Development Fund, Macau SAR 0057/2023/RIB2University of Macau MYRG-GRG2024-00090-IAPMEUniversity of Macau MYRG-GRG2025-00142-IAPME
6 · The paper itself

Abstract

Flexible pressure sensors have recently aroused increasing interests for fields of electronic skin, intelligent robotics, and human-machine interactions (HMI). Preserving high sensitivity of flexible pressure sensors across a broad range is essential prerequisite to ensure signal-to-noise reliability for multiple applications. However, this remains challenging due to the inherent sensing saturation when flexible matrix is exposed to varying pressure. Herein, a flexible pressure sensor based on design of binary micro-dome pixels is proposed. Triple-gradient of conductivity, modulus, and dimension is precisely designed in binary pixels, ensuring mechanical and electrical compensation during the entire deformation process. With well-developed CNT/PDMS matrix, significant sensing enhancement is realized, showcasing a linear sensitivity of 974.1 kPa

Indexed as

binary pixelgradient designhuman‐machine interactionsmachine learningwearable sensors

Identifiers

PMID42446152
PMCPMC13367112

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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.