Evidence map›Paper›PMID 39669520›Full record

ArticleNanoscale advances2025

Laser writing of metal-oxide doped graphene films for tunable sensor applications.

Shasvat Rathod, Monika Snowdon, Matthew Peres Tino, Peng Peng

Abstract read
In one paragraph

Article in Nanoscale advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Shasvat RathodCentre for Advanced Materials Joining, Department of Mechanical and Mechatronics Engineering, University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada peng.peng@uwaterloo.ca.ORCID https://orcid.org/0000-0002-3313-0587
Monika SnowdonCentre for Advanced Materials Joining, Department of Mechanical and Mechatronics Engineering, University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada peng.peng@uwaterloo.ca.ORCID https://orcid.org/0000-0001-6020-4839
Matthew Peres TinoCentre for Advanced Materials Joining, Department of Mechanical and Mechatronics Engineering, University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada peng.peng@uwaterloo.ca.ORCID https://orcid.org/0009-0005-6832-1761
Peng PengCentre for Advanced Materials Joining, Department of Mechanical and Mechatronics Engineering, University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada peng.peng@uwaterloo.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flexible and wearable devices play a pivotal role in the realm of smart portable electronics due to their diverse applications in healthcare monitoring, soft robotics, human-machine interfaces, and artificial intelligence. Nonetheless, the extensive integration of intelligent wearable sensors into mass production faces challenges within a resource-limited environment, necessitating low-cost manufacturing, high reliability, stability, and multi-functionality. In this study, a cost-effective fiber laser direct writing method (fLDW) was illustrated to create highly responsive and robust flexible sensors. These sensors integrate laser-induced graphene (LiG) with mixed metal oxides on a flexible polyimide film. fLDW simplifies the synthesis of graphene, functionalization of carbon structures into graphene oxides and reduced graphene oxides, and deposition of metal-oxide nanoparticles within a single experimental laser writing setup. The preparation and surface modification of dense oxygenated graphene networks and semiconducting metal oxide nanoparticles (CuO

Identifiers

PMID39669520
PMCPMC11632522

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.