Evidence map›Paper›PMID 42446087›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2026

Ultra-Tough and Stable Leather-Derived Gel Wearable Sensor: Enabling Multimodal Monitoring of Human Motion Signals and Distress Signal Transmission.

Jialu Shen, Pengtao Zu, Jinwei Zhang, Wei Lin, Haibin Gu

Abstract read
In one paragraph

Article in Small (Weinheim an der Bergstrasse, 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.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jialu ShenKey Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu, China.
Pengtao ZuKey Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu, China.
Jinwei ZhangKey Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu, China.
Wei LinKey Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu, China.
Haibin GuKey Laboratory of Leather Chemistry and Engineering of Ministry of Education, Sichuan University, Chengdu, China.

Funding

National Natural Science Foundation of China 22578293
6 · The paper itself

Abstract

In extreme scenarios such as emergency rescue operations, conventional hydrogel sensors face significant challenges in achieving real-time distress signal transmission due to their mechanical brittleness and stringent signal transmission requirements. This study introduced a multifunctional integrated carbon quantum dots (CCQDs)-empowered ultra-tough and stable leather-based gel multimodal wearable sensor (SMT-TL), aiming to achieve long-term stable distress signal transmission. SMT-TL utilized zeolite-tanned sheep leather (SMT-L) as a rigid substrate, with flexible polyacrylic acid (PAA) densely filling the gaps of collagen fibers within SMT-L and enhancing hydrogen bond interactions. This resulted in the fabrication of an ultra-tough, ultra-stretchable, multifunctional integrated, and transparent "rigid-flexible combination" SMT-TL. It retained over 70% of the breaking strength (>13.5 MPa) of original SMT-L, while achieving an elongation exceeding 590.0%, a toughness greater than 63.64 MJ/m

Indexed as

MotionWearable Electronic DevicesAcrylic ResinsAnimalsCarbon Quantum DotsGelsHumansMonitoring, PhysiologicAcrylic Resinscarbopol 940Gelscarbon quantum dotsfunctional integrationinformation transmissionleather‐derived wearable sensor

Identifiers

PMID42446087
PMCPMC13548922

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.