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Record W4413049156 · doi:10.1016/j.addma.2025.104914

Radiopaque filaments for fused filament fabrication (FFF) 3D printing

2025· article· en· W4413049156 on OpenAlexafffund
Souheib Zekraoui, Théophraste Lescot, Mahdokht Akbari Taemeh, Marc‐André Fortin

Bibliographic record

VenueAdditive manufacturing · 2025
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalCentre hospitalier universitaire de Québec
FundersInstitute of Cancer ResearchNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsMaterials scienceFused filament fabricationFabricationProtein filament3D printingComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Polyetheretherketone (PEEK) is a chemically resistant, high-performance polymer known for its excellent thermal and mechanical properties. It is increasingly employed in the development of 3D-printed medical implants and radiotherapeutic devices. While PEEK exhibits strong resistance to radiation-induced degradation, it has inherently poor radiation-blocking capacity. However, many tools and components used in radiology, radiotherapy, and nuclear medicine—such as brachytherapy implants, shielding devices, imaging screens, and personalized radioprotective equipment—require a certain degree of radiopacity. This study presents a novel methodology for integrating electron-dense particles (micro- or nano-sized) into high-performance polymer filaments suitable for fused filament fabrication (FFF) 3D printing. Specifically, a new pre-processing approach was developed to achieve optimal mixing of micrometer-sized tungsten (µW) particles with micrometer-sized PEEK particles (µPEEK), prior to their incorporation into millimeter-sized PEEK pellets. This tri-phased particle mixture enabled high-temperature extrusion, producing robust, continuous filaments with uniformly embedded metal particles within a protective polymer matrix. The resulting filaments were characterized using scanning electron microscopy (SEM) to assess morphology, X-ray fluorescence (XRF) imaging to evaluate particle distribution homogeneity, and elemental analysis to quantify the content of the radiation-blocking material. Filaments containing tungsten at concentrations below 10 vol.% were used to fabricate high-density, 3D-printed structures of varying thicknesses. Optimal printing parameters were identified, and the X-ray attenuation properties of the printed objects were evaluated through radiographic imaging. Additionally, mechanical properties of the 3D printed PEEK, and PEEK-W composites were evaluated through standardized flexural and Vickers hardness testing. Finally, biocompatibility testing using primary human choroid fibroblast (hCSF) cells demonstrated high cell viability after four days of exposure to the W-loaded PEEK prints. These findings highlight the potential of 3D-printed radiopaque polymers not only for medical implants but also for applications in aerospace and nuclear technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.222
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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