Effects of PEEK surface treatment using alumina blasting or concentrated sulfuric acid etching in combination with functional monomers on shear bond strength to adhesive cement after artificial aging.
{"title":"Effects of PEEK surface treatment using alumina blasting or concentrated sulfuric acid etching in combination with functional monomers on shear bond strength to adhesive cement after artificial aging.","authors":"Maowei Zhong, Ryuhei Kanda, Susumu Tsuda, Yoshiya Hashimoto, Ruonan Zhang, Takamasa Fujii, Kosuke Kashiwagi","doi":"10.4012/dmj.2024-233","DOIUrl":null,"url":null,"abstract":"<p><p>The purpose of this study is to investigate the effects of surface treatment methods using polyetheretherketone (PEEK) (with or without a functional monomer-containing primer following treatment with alumina blasting or concentrated sulfuric acid) on the shear bond strength (SBS) of resin luting material after artificial aging. The PEEK specimens were classified into five groups according to their treatment methods: untreated, alumina blasting (AB), concentrated sulfuric acid (SA), alumina blasting+primer (ABP), and concentrated SA+primer (SAP). The SBS score of each group was determined experimentally using a universal testing machine. The SBS tests revealed that the initial bond strengths of ABP and SAP were significantly higher than those of AB and SA. In addition, both SBS after 20,000 thermal cycles remained high (>15 MPa). These results suggest that the ABP and SAP groups are the best predictive methods for evaluating SBS with PEEK and resin cement.</p>","PeriodicalId":11065,"journal":{"name":"Dental materials journal","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dental materials journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.4012/dmj.2024-233","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
The purpose of this study is to investigate the effects of surface treatment methods using polyetheretherketone (PEEK) (with or without a functional monomer-containing primer following treatment with alumina blasting or concentrated sulfuric acid) on the shear bond strength (SBS) of resin luting material after artificial aging. The PEEK specimens were classified into five groups according to their treatment methods: untreated, alumina blasting (AB), concentrated sulfuric acid (SA), alumina blasting+primer (ABP), and concentrated SA+primer (SAP). The SBS score of each group was determined experimentally using a universal testing machine. The SBS tests revealed that the initial bond strengths of ABP and SAP were significantly higher than those of AB and SA. In addition, both SBS after 20,000 thermal cycles remained high (>15 MPa). These results suggest that the ABP and SAP groups are the best predictive methods for evaluating SBS with PEEK and resin cement.
本研究的目的是探讨聚醚醚酮(PEEK)表面处理方法(氧化铝喷射或浓硫酸处理后使用或不使用含功能单体的底漆)对人工老化后树脂敷层材料剪切结合强度(SBS)的影响。根据处理方法将 PEEK 试样分为五组:未处理组、氧化铝喷射组(AB)、浓硫酸组(SA)、氧化铝喷射+底漆组(ABP)和浓硫酸组+底漆组(SAP)。各组的 SBS 评分是通过万能试验机进行实验测定的。SBS 测试表明,ABP 和 SAP 的初始粘接强度明显高于 AB 和 SA。此外,经过 20,000 次热循环后,这两种材料的 SBS 仍保持较高水平(大于 15 兆帕)。这些结果表明,ABP 和 SAP 组是评估 PEEK 和树脂水泥 SBS 的最佳预测方法。
期刊介绍:
Dental Materials Journal is a peer review journal published by the Japanese Society for Dental Materials and Devises aiming to introduce the progress of the basic and applied sciences in dental materials and biomaterials. The dental materials-related clinical science and instrumental technologies are also within the scope of this journal. The materials dealt include synthetic polymers, ceramics, metals and tissue-derived biomaterials. Forefront dental materials and biomaterials used in developing filed, such as tissue engineering, bioengineering and artificial intelligence, are positively considered for the review as well. Recent acceptance rate of the submitted manuscript in the journal is around 30%.