Evidence map›Paper›PMID 42549290›Full record

ArticleSmall science2026

Sustainable P3HB:ZnO Composite Piezoelectric Nanofibers for AI-Driven Gait Monitoring.

Milad Razbin, Kexin Ruan, Danish Tahir, Xuan Li, Qing Li, Hala Zreiqat, Fariba Dehghani, Shuying Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Small science, 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.

Milad RazbinSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.
Kexin RuanSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.
Danish TahirSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.ORCID https://orcid.org/0000-0001-6885-8650
Xuan LiSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.
Qing LiSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.
Hala ZreiqatSchool of Biomedical Engineering The University of Sydney Sydney New South Wales Australia.
Fariba DehghaniSchool of Chemical and Biomolecular Engineering The University of Sydney Sydney New South Wales Australia.ORCID https://orcid.org/0000-0002-7805-8101
Shuying WuSchool of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney New South Wales Australia.ORCID https://orcid.org/0000-0002-6585-8898

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Degrable, high-performance piezoelectric materials are critical for implantable and environmentally friendly devices capable of reliable sensing and energy harvesting while safely degrading after use. However, most degrable piezoelectric materials still suffer from limited electromechanical performance. Here, we report a high-performance poly(3-hydroxybutyrate):zinc oxide (P3HB:ZnO) composite piezoelectric nanofiber optimized using an integrated artificial neural network-genetic algorithm framework. The model identified an optimal ZnO content of 4.3 wt.%, resulting in significantly enhanced piezoelectric performance compared with pristine P3HB. The optimized nanofibers achieved a peak voltage sensitivity of 0.382 mV/kPa at 1 Hz and an effective piezoelectric voltage coefficient of 13.65 mV/m·N, with stable output over 1000 loading cycles and a response delay of 318 ms. Biodegradation studies showed approximately 38% mass loss after 6 weeks, while the nanofibers retained measurable piezoelectric functionality within this timeframe. Furthermore, integration with a multichannel sensing platform enabled a proof-of-concept AI-assisted gait-recognition demonstration. This work presents a scalable machine-learning-driven strategy for designing high-performance sustainable piezoelectric materials for self-powered transient bioelectronics.

Indexed as

artificial neural networkelectrospinninggenetic algorithmpiezoelectricitysustainable materials

Identifiers

PMID42549290
PMCPMC13431746

What OpenQuestion holds

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LicenceCC BY
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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.