Evidence map›Paper›PMID 42212639›Full record

ArticleClinical oral investigations2026

Influence of silica nanoparticle sedimentation on the mechanical, surface properties and trueness of DLP-printed dental resin.

Yifan Ma, Gan Jin, Di Chen, Zhihao Zhang, Yunqi Liu, Jung-Hwa Lim, Jong-Eun Kim

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Article in Clinical oral investigations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yifan Ma *Department of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Gan Jin *Department of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Di ChenDepartment of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Zhihao ZhangDepartment of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Yunqi LiuDepartment of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Jung-Hwa LimDepartment of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
Jong-Eun KimDepartment of Prosthodontics, College of Dentistry, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. gomyou@yuhs.ac.

Funding

Ministry of Science and ICT, South Korea No. RS-2024-00406281
6 · The paper itself

Abstract

objectivesThis study investigated the gravitational sedimentation of silica nanoparticles in DLP-printed dental resins and assessed its impact on filler distribution, DC, mechanical and surface properties and printing trueness. MATERIALS AND

methodsDental resin containing 1 wt% SiNPs was printed via DLP printing into 1 mm-thick disks at build heights ranging from 1 to 15 mm above the platform. SiNP distribution was analyzed by SEM-EDS. DC, Vickers hardness, contact angle, surface roughness (Ra), and ΔE were measured and correlated with local Si concentrations (n = 5). The printed specimens from each build height were randomly assigned to different tests. Printing trueness (n = 15) was measured using digital superimposition analysis of dental crowns by RMS values. Dental resin without SiNPs served as controls. One-way ANOVA with Tukey's post-hoc test, linear regression and t-test were used for statistical analysis.

resultsSEM-EDS confirmed a linear increase in Si concentration with build height in the 1 wt% group (R

conclusionsSedimentation during DLP induces predictable vertical nanoparticle gradients, which in turn systematically influence DC, mechanical strength, hydrophilicity, surface smoothness, and printing trueness. CLINICAL RELEVANCE: Nanoparticle sedimentation creates vertical property gradients within DLP-printed restorations. This causes heterogeneous mechanical and surface performance, potentially leading to localized weakness or marginal discrepancies. Clinicians should optimize print orientation to position functionally critical surfaces at appropriate build heights and implement mid-print homogenization to minimize sedimentation effects.

Indexed as

Composite ResinsNanoparticlesPrinting, Three-DimensionalSilicon DioxideHardnessMaterials TestingMicroscopy, Electron, ScanningSurface PropertiesComposite ResinsSilicon Dioxide3D PrintingComposite dental resinGradientMechanical testsNanoparticlesSedimentation

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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.