Evidence map›Paper›PMID 38930352›Full record

ArticleMaterials (Basel, Switzerland)2024

Optimising Mg-Ca/PLA Composite Filaments for Additive Manufacturing: An Analysis of Particle Content, Size, and Morphology.

Hyeonseok Kim, Tom McKenna, Eoin O'Cearbhaill, Mert Celikin

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. 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.

Hyeonseok KimSchool of Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland.ORCID 0009-0009-8756-9753
Tom McKennaSchool of Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland.ORCID 0000-0002-4260-1993
Eoin O'CearbhaillSchool of Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland.ORCID 0000-0002-4666-5863
Mert CelikinSchool of Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland.ORCID 0000-0001-7807-4446

Funding

Science Foundation Ireland 20/FFP-P/8868
6 · The paper itself

Abstract

Low-temperature additive manufacturing of magnesium (Mg) alloy implants is considered a promising technique for biomedical applications due to Mg's inherent biocompatibility and 3D printing's capability for patient-specific design. This study explores the influence of powder volume content, size, and morphology on the mechanical properties and viscosity of polylactic acid (PLA) matrix composite filaments containing in-house-produced magnesium-calcium (Mg-Ca) particles, with a focus on their application towards low-temperature additive manufacturing. We investigated the effects of varying the Mg-Ca particle content in a PLA matrix, revealing a direct correlation between volume content and bending strength. Particle size analysis demonstrated that smaller particles (D50: 57 μm) achieved a bending strength of 63.7 MPa, whereas larger particles (D50: 105 μm) exhibited 49.6 MPa at 20 vol.%. Morphologically, the filament containing spherical particles at 20 vol.% showed a bending strength that was 11.5 MPa higher than that of the filament with irregular particles. These findings highlight the critical role of particle content, size, and shape in determining the mechanical and rheological properties of Mg-Ca/PLA composite filaments for use in material extrusion additive manufacturing.

Indexed as

additive manufacturingbiomaterialsfilamentmagnesium alloymaterial extrusionparticle contentparticle morphologyparticle sizeviscosity

Identifiers

PMID38930352
PMCPMC11205617

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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.