journal article·2012-06

Chemical and structural analysis of the bone-implant interface by TOF-SIMS, SEM, FIB and TEM: Experimental study in animal

Anders Palmquist, Lena Emanuelsson, Peter Sjövall

Applied Surface Science· Volume 258· Issue 17
Publisher page

Abstract

Although bone-anchored implants are widely used in reconstructive medicine, the mechanism of osseointegration is still not fully understood. Novel analytical tools are needed to further understand this process, where both the chemical and structural aspects of the bone-implant interface are important. The aim of this study was to evaluate the advantages of combining time-of-flight secondary ion mass spectroscopy (TOF-SIMS) with optical (LM), scanning (SEM) and transmission electron microscopy (TEM) techniques for studying the bone-implant interface of bone-anchored implants. Laser-modified titanium implants with surrounded bone retrieved after 8 weeks healing in rabbit were dehydrated and resin embedded. Three types of sample preparation were studied to evaluate the information gained by combining TOF-SIMS, SEM, FIB and TEM. The results show that imaging TOF-SIMS can provide detailed chemical information, which in combination with structural information from microscopy methods provide a more complete characterization of anatomical structures at the bone-implant interface. By investigating various sample preparation techniques, it is shown that grinded cross section samples can be used for chemical imaging using TOF-SIMS, if careful consideration of potential preparation artifacts is taken into account. TOF-SIMS analysis of FIB-prepared bone/implant cross section samples show distinct areas corresponding to bone tissue and implant with a sharp interface, although without chemical information about the organic components.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Ion-surface interactions and analysis · Bone Tissue Engineering Materials · Dental Implant Techniques and Outcomes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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