Evidence map›Paper›PMID 39323418›Full record

ArticleNanoscale advances2024

Development of a laser induced graphene (LIG) and polylactic acid (PLA) shape memory polymer composite with simultaneous multi-stimuli response and deformation self-sensing characteristics.

Reza Gholami, Ibrahim Lawan, Panuwat Luengrojanakul, Sahar Ebrahimi, Cheol-Hee Ahn, Sarawut Rimdusit

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanoscale advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Reza GholamiCenter of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand sarawut.r@chula.ac.th.
Ibrahim LawanCenter of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand sarawut.r@chula.ac.th.
Panuwat LuengrojanakulCenter of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand sarawut.r@chula.ac.th.
Sahar EbrahimiCenter of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand sarawut.r@chula.ac.th.
Cheol-Hee AhnDepartment of Materials Science and Engineering, Seoul National University Seoul 08826 Korea.
Sarawut RimdusitCenter of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand sarawut.r@chula.ac.th.ORCID https://orcid.org/0000-0002-9531-2300

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents the integration of laser-induced graphene (LIG) on a polylactic acid (PLA) substrate to create a novel shape memory polymer composite (SMPC) with multi-stimuli response and deformation self-sensing characteristics. The LIG was initially engraved on a commercial polyimide film and subsequently transferred to the PLA substrate through hot compression. Raman spectra analysis confirmed the successful engraving of the LIG, exhibiting the typical characteristic peaks. Durability tests revealed that the transferred LIG adhered well to the PLA substrate. Additionally, the transferred LIG demonstrated a sheet resistance of 40.3 Ω sq

Identifiers

PMID39323418
PMCPMC11421537

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Registered trials

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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.