Assessment of biodegradable magnesium alloys for enhanced mechanical and biocompatible properties

Biomaterials have been used for more than a century in the human body to improve body functions and replace damaged tissues. Currently approved and commonly used metallic biomaterials such as, stainless steel, titanium, cobalt chromium and other alloys have been found to have adverse effects leading in some cases, to mechanical failure and rejection of the implant. The physical or chemical nature of the degradation products of some implants initiates an adverse foreign body reaction in the tissue. Some metallic implants remain as permanent fixtures, whereas others such as plates, screws and pins used to secure serious fractures are removed by a second surgical procedure after the tissue has healed sufficiently. However, repeat surgical procedures increase the cost of health care and the possibility of patient morbidity. This study focuses on the development of magnesium based biodegradable alloys/metal matrix composites (MMCs) for orthopedic and cardiovascular applications. The Mg alloys/MMCs possessed good mechanical properties and biocompatible properties. Nine different compositions of Mg alloys/MMCs were manufactured and surface treated. Their degradation behavior, ion leaching, wettability, morphology, cytotoxicity and mechanical properties were determined. Alloying with Zn, Ca, HA and Gd and surface treatment resulted in improved mechanical properties, corrosion resistance, reduced cytotoxicity, lower pH and hydrogen evolution. Anodization resulted in the formation of a distinct oxide layer (thickness 5-10 μm) as compared with that produced on mechanically polished samples (~20-50 nm) under ambient conditions. It is envisaged that the findings of this research will introduce a new class of Mg based biodegradable alloys/MMCs and the emergence of innovative cardiovascular and orthopedic implant devices.^

Recurrent Tibial Tunnel Cyst Formation Following Anterior Cruciate Ligament Reconstruction and Interference Screw Removal

A unique case of a collegiate athlete who suffered an anterior cruciate ligament injury leading to the formation of a synovial cyst is described. The cyst, localized over the tibial tunnel, resulted from irritation caused by the removal of interference screws.

Development of New Structural Health Monitoring Techniques

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

Development of New Structural Health Monitoring Techniques

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.