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基于铝酸锶的机械发光材料的优化用于人工牙咬合检查。

Optimization of strontium aluminate-based mechanoluminescence materials for occlusal examination of artificial tooth.

机构信息

School of Stomatology, Lanzhou University, Lanzhou, Gansu 730000, China; State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.

State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China.

出版信息

Mater Sci Eng C Mater Biol Appl. 2018 Nov 1;92:374-380. doi: 10.1016/j.msec.2018.06.056. Epub 2018 Jul 20.

Abstract

This work presents a novel approach for evaluating the occlusal examination of artificial tooth based on the mechanoluminescence (ML) materials. The rare earth doped strontium aluminate (SrAlO: Eu, Dy; SAOED) was chosen as the ML material, which was further composited with the commercial denture base resin (DBR) to determine its feasibility for the mechanics analysis of artificial tooth occlusion. To eliminate negative factors for occlusal analysis, SAOED was first optimized to exhibit a rapid decay of afterglow and enhanced ML intensity. The luminescent characterizations of the SAOED/DBR composites suggest DBR is a desirable elastic-supporter for nondestructive ML generation. Furthermore, the introduction of SAOED improved the mechanical performance of DBR, and its biocompatibility was maintained at the same time. These results suggest the feasibility of the idea to detect the mechanics in occlusal examination of artificial tooth based on ML. The bright and sensitive ML from the constructed standard artificial tooth models could guide clinicians to purposefully adjust the occlusal surface until a balanced occlusion established.

摘要

本工作提出了一种基于光致发光(ML)材料评估人工牙咬合检查的新方法。选择掺 Eu、Dy 的 SrAlO 作为 ML 材料:Eu、Dy(SAOED),并进一步与商业义齿基托树脂(DBR)复合,以确定其在人工牙咬合力学分析中的可行性。为了消除咬合分析的负面影响,首先对 SAOED 进行了优化,以表现出后发光的快速衰减和增强的 ML 强度。SAOED/DBR 复合材料的发光特性表明 DBR 是一种理想的弹性支撑体,可用于无损 ML 产生。此外,SAOED 的引入提高了 DBR 的机械性能,同时保持了其生物相容性。这些结果表明了基于 ML 检测人工牙咬合力学的想法的可行性。从构建的标准人工牙模型中获得的明亮且灵敏的 ML 可以指导临床医生有目的地调整咬合面,直到建立平衡的咬合。

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