Evidence map›Paper›PMID 40859909›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Thinned and Welded Silver Nanowires for Intelligent Pressure and Humidity Sensing Enabled by Machine Learning.

Jiajun Fan, Tao Wan, Tao Yin, Ziheng Feng, Peiyuan Guan, Tianxu Huang, Chao Liu, Mengyao Li, Shuying Wu, Shuhua Peng and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. 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. 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

15 authors.

Jiajun FanSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Tao WanSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Tao YinSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Ziheng FengSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Peiyuan GuanSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Tianxu HuangSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Chao LiuSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Mengyao LiSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Shuying WuSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, 2006, Australia.
Shuhua PengSchool of Mechanical Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Shihao HuangSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.
Zhaojun HanEastern Institute of Technology, Ningbo, Zhejiang, 315200, China.
Dongchen QiSchool of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD, 4000, Australia.
Wenlong ChengSchool of Biomedical Engineering, The University of Sydney, Darlington, New South Wales, 2008, Australia.
Dewei ChuSchool of Materials Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia.ORCID https://orcid.org/0000-0003-4581-0560

Funding

Australian Research Council DP210100879Australian Research Council DP230101040Australian Research Council IH210100040
6 · The paper itself

Abstract

The rapid development of flexible and wearable technologies urgently requires high-performance and compatible flexible electrodes. Silver nanowires (AgNWs) are considered a promising conductive material, but their inherent structural drawbacks significantly hinder their widespread application. Here, an innovative and facile strategy is employed to simultaneously enhance the electrical and optical properties of AgNWs by precisely tuning their microstructure. Thinner AgNWs are achieved through the selective etching, alongside enhanced heating and mechanical properties, stemming from the welded structure and preserved conductive network integrity. A pressure sensor constructed with modified AgNWs demonstrates improved sensitivity compared to one using pristine AgNWs. By leveraging machine learning, the sensor can identify pressing behaviors with different fingers, achieving a high accuracy of 94.5%. The letters of the alphabet are also accurately recognized through analysis of unique resistance patterns in their Morse code. With the incorporation of a moisture-sensitive graphene oxide layer, the device is capable of recognizing human respiration behaviors and detecting voices based on their distinctive respiration and pronunciation patterns. The strategy employed to tailor the functionality of AgNWs, combined with further integration of machine learning, presents a promising avenue for advancing flexible and wearable electronics.

Indexed as

conductive networkmachine learningmorse codeselective etchingsensing

Identifiers

PMID40859909
PMCPMC12631923

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.