Evidence map›Paper›PMID 41499019›Full record

ArticleNano-micro letters2026

Strong and Tough MXene-Induced Bacterial Cellulose Macrofibers for AIoT Textile Electronics.

Yi Hao, Zixuan Zhang, Yajun Chen, Song Wang, Yingjia Tong, Pengfei Lv, Qufu Wei, Chengkuo Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano-micro letters, 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

8 authors.

Yi HaoKey Laboratory of Special Protective Textiles, Ministry of Education, College of Textile Science and Engineering, Jiangnan University, Wuxi, 214122, People's Republic of China.
Zixuan ZhangDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore.
Yajun ChenSuzhou Institute of Trade & Commerce, Suzhou, 215009, People's Republic of China.
Song WangState Key Laboratory for Mechanical Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China.
Yingjia TongSchool of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, People's Republic of China.
Pengfei LvKey Laboratory of Special Protective Textiles, Ministry of Education, College of Textile Science and Engineering, Jiangnan University, Wuxi, 214122, People's Republic of China. pengfeilv@jiangnan.edu.cn.
Qufu WeiKey Laboratory of Special Protective Textiles, Ministry of Education, College of Textile Science and Engineering, Jiangnan University, Wuxi, 214122, People's Republic of China. qfwei@jiangnan.edu.cn.
Chengkuo LeeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore. elelc@nus.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Textile electronics with extraordinary sensing capabilities holds significant potential in the Artificial Intelligence of Things (AIoT). However, little effort is paid to their mutual advantages of robust interfacial interactions, ultra-strong mechanical performance, and stability. Herein, we fabricate homogeneous and multifunctional core-shell macrofibers by integrating bridge-functionalized MXene/PEDOT:PSS conductive ink with aligned bacterial cellulose (BC). These resulting macrofibers feature mechanical properties (tensile strength of 433.2 MPa and the Young's modulus of 25.9 GPa), exceptional electrical conductivity (10.05 S cm

Indexed as

Bacterial cellulose macrofiberLiquid recognitionMXene/PEODT:PSS inkTextile electronicsTriboelectric nanogenerators

Identifiers

PMID41499019
PMCPMC12779806

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.