Evidence map›Paper›PMID 42390292›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

Bioinspired Electrostatic-Field Perturbated Sensing for General Material Noncontact Perception.

Weiqiang Zhang, Mingxin Liu, Xiaozhou Lü, Rusen Yang, Pengfei Wang, Linfeng Deng, Yangyang Cui, Xiangwang Hou, Zhong Lin Wang

Abstract read
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

9 authors.

Weiqiang ZhangSchool of Aerospace Science and Technology, Xidian University, Xi'an, China.
Mingxin LiuSchool of Aerospace Science and Technology, Xidian University, Xi'an, China.
Xiaozhou LüSchool of Aerospace Science and Technology, Xidian University, Xi'an, China.
Rusen YangSchool of Physics, Xidian University, Xi'an, China.ORCID https://orcid.org/0000-0002-4019-4642
Pengfei WangInstitute of Nanosurface Science and Engineering, State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, China.ORCID https://orcid.org/0000-0002-6747-1574
Linfeng DengSchool of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou, China.
Yangyang CuiSchool of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
Xiangwang HouDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Zhong Lin WangBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-5530-0380

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515240050Beijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems GS2025MS030National Natural Science Foundation of China 52575226
6 · The paper itself

Abstract

Noncontact sensing is essential for smart manufacturing and safe human-machine interaction, yet existing proximity and inspection sensors are often limited by material selectivity, ambient light, and continuous power consumption. Here, we report a bioinspired electrostatic-field sensor that implements a solid-state analogue of electrolocation using a corona-polarized fluoropolymer electret. The pre-charged electret establishes a quasi-static electric field, and nearby objects reshape this field to induce measurable potential modulation on a grounded electrode, enabling friction-free sensing without active emission or mechanical contact. The sensor detects both conductive and dielectric targets (metals, polymers, glass), while waveform features encode material-dependent electrical signatures. The device achieves a near-field change in separation sensitivity of 1.05 V per 50 µm and maintains stable responses over 10,000 approach-withdraw cycles. Combined with machine-learning models, the platform enables proximity warning, touch-free interaction, material discrimination, coating-defect recognition, gesture decoding, and gesture-to-robot control. This work establishes electrostatic-field perturbation as a robust strategy for low-power, multifunctional noncontact perception.

Indexed as

defect inspectionelectrostatic‐field sensingfield perturbationgeneral material noncontact sensinghuman‐machine interaction

Identifiers

PMID42390292
PMCPMC13449125

What OpenQuestion holds

Textmetadata
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.