Evidence map›Paper›PMID 42889989›Full record

ArticleACS omega2026

Direct Laser Writing of Functional Material with Independently Tunable Electrical and Mechanical Properties.

Yifan Chen, Wing Ki Lo, Zeying Chen, Sen Yang

Abstract read
In one paragraph

Article in ACS omega, 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

4 authors.

Yifan ChenDepartment of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.ORCID https://orcid.org/0000-0002-9373-5633
Wing Ki LoDepartment of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.
Zeying ChenDepartment of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.
Sen YangDepartment of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional materials with tunable electrical and mechanical properties are essential for technologies such as flexible electronics and soft robotics, where the device performance must adapt to complex mechanical and electrical environments. Conventional approaches often rely on blending conductive metal nanoparticles (NPs) into a polymer matrix, where the pursuit of high conductivity can compromise mechanical integrity, and vice versa. We report a reagent-engineering strategy for direct laser writing (DLW) that decouples this fabrication challenge. By formulating a precursor that combines a metal salt or metalate with a suspension of NPs as a reducing agent, we achieve the single-step deposition of homogeneous metal-NP composites. Electrical characterization reveals that conductivity is primarily governed by the choice of metal salt or metalate, spanning several orders of magnitude, while changes in the NP filler induce only a moderate variation. Mechanical nanoindentation shows that both the elastic modulus and the hardness of the composite are controlled by the size and type of the NP suspension, indicating its potential role as a mechanical reinforcement phase. Energy-dispersive X-ray spectroscopy (EDS) confirmed a homogeneous distribution of both controlling phases within the deposit. This work demonstrates a direct-write platform where electrical and mechanical properties can be independently tuned through precursor design, offering a route to functionally graded materials.

Identifiers

PMID42889989
PMCPMC13670759

What OpenQuestion holds

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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.