Evidence map›Paper›PMID 42280560›Full record

ArticlePolymers2026

Mechanical Enhancement and Fracture Mechanisms of SLA Photopolymer Composites Reinforced with Fish Bone Ash.

Cem Alparslan, Mert Minaz, Erhan Baysal, Muhammed Fatih Yentimur, Oğuz Koçar, Şenol Bayraktar

Abstract read
In one paragraph

Article in Polymers, 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

6 authors.

Cem AlparslanDepartment of Mechanical Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdoğan University, Rize 53100, Türkiye.ORCID 0000-0001-7316-360X
Mert MinazDepartment of Aquaculture, Recep Tayyip Erdoğan University, Rize 53100, Türkiye.
Erhan BaysalAlapli Vocational School, Zonguldak Bülent Ecevit University, Zonguldak 67850, Türkiye.ORCID 0000-0002-2767-8722
Muhammed Fatih YentimurDepartment of Civil Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, Rize 53100, Türkiye.ORCID 0000-0003-0496-9065
Oğuz KoçarDepartment of Mechanical Engineering, Faculty of Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67100, Türkiye.ORCID 0000-0002-1928-4301
Şenol BayraktarDepartment of Mechanical Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdoğan University, Rize 53100, Türkiye.ORCID 0000-0001-8226-0188

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, salmon fish bone waste from the fish processing industry was converted into an inorganic ash filler by calcination and incorporated into an SLA-compatible photopolymer resin at 4, 8, and 12 wt.%. To compensate for filler-induced optical scattering and rheological changes, the printing parameters were systematically optimized, and the optimum conditions were identified as a layer thickness of 30 µm and an exposure time of 12 s. Tensile tests performed in accordance with ASTM D638 Type IV showed that fish bone ash significantly enhanced the tensile strength of the photopolymer matrix, increasing it from 24.8 MPa for the neat resin to 37.95 MPa at 12 wt.% filler loading. In contrast, increasing filler content reduced elongation at break and promoted a more brittle fracture response. Statistical evaluation using Welch ANOVA and Games-Howell post hoc analysis confirmed that filler loading had a statistically significant effect on tensile strength (

Indexed as

additive manufacturingbio-wastephotopolymer compositesalmon fish bone ashstereolithographysustainable filler

Identifiers

PMID42280560
PMCPMC13259430

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.