Effect of Inclusion Size and Distribution on the Corrosion Behavior of Medical-Device Grade Nitinol Tubing

Nonmetallic inclusions in Nitinol, such as carbides (TiC) and intermetallic oxides (Ti₄Ni₂O _x ), are known to be triggers for fatigue failure of Nitinol medical devices. These mechanically brittle inclusions are introduced during the melting process. As a result of hot and cold working in the production of Nitinol tubing inclusions are fractionalized due to the mechanical deformation imposed. While the role of inclusions regarding Nitinol fatigue performance has been studied extensively in the past, their effect on Nitinol corrosion behavior was investigated in only a limited number of studies. The focus of the present work was to understand the effect of inclusion size and distribution on the corrosion behavior of medical-device grade Nitinol tubing made from three different ingot sources during different manufacturing stages: (i) for the initial stage (hollow: round bar with centric hole), (ii) after hot drawing, and (iii) after the final drawing step (final tubing dimensions: outer diameter 0.3 mm, wall thickness 0.1 mm). For one ingot source, two different material qualities were investigated. Potentiodynamic polarization tests were performed for electropolished samples of the above-mentioned stages. Results indicate that inclusion size rather than inclusion quantity affects the susceptibility of electropolished Nitinol to pitting corrosion.     

Non-uniformity issues and workarounds in bounded-size sampling

A variety of schemes have been proposed in the literature to speed up query processing and analytics by incrementally maintaining a bounded-size uniform sample from a dataset in the presence of a sequence of insertion, deletion, and update transactions. These algorithms vary according to whether the dataset is an ordinary set or a multiset and whether the transaction sequence consists only of insertions or can include deletions and updates. We report on subtle non-uniformity issues that we found in a number of these prior bounded-size sampling schemes, including some of our own. We provide workarounds that can avoid the non-uniformity problem; these workarounds are easy to implement and incur negligible additional cost. We also consider the impact of non-uniformity in practice and describe simple statistical tests that can help detect non-uniformity in new algorithms.     

Analysis of New Nitinol Ingot Qualities

New ingot qualities, processed by optimized vacuum arc remelting (VAR), optimized vacuum induction melting followed by VAR and VAR followed by electron beam remelting, were compared with standard quality. Finished components as well as diamond-shaped samples representing a typical dimension of self-expanding stents were produced using Nitinol tubing drawn from the new ingot qualities. Metallographic longitudinal sections were prepared and analyzed to determine inclusion size and distributions of the various ingot qualities. Radial force and uniaxial tensile tests were used to determine the mechanical properties of fully processed material and tubing, respectively. Transformation temperatures of tubing as delivered from supplier and processed stents were measured by differential scanning calorimetry and deformation-and-free-recovery testing. Finally, fatigue tests were performed on diamond-shaped samples to evaluate the strain-life characteristics of the new ingot qualities. Results of this study are compared to ADMEDES historical data from standard Nitinol materials to gain an assessment of the new improved ingot qualities with regard to the production of Nitinol vascular implants. The latest developments in Nitinol ingot quality are highlighted and the results of the comparison from technical point of view are shown.     

Frequency and phase estimation in time series with quasi periodic components

In this article, we consider frequency and phase estimation in a noisy oscillation with potentially non‐constant phase increments resulting from an underlying non‐constant frequency. A maximum periodogram method on segments is used to estimate the time‐varying frequency and a subsequent least squares approach to estimate the phase. A key problem addressed in this article is the question how to set up a meaningful concept of asymptotic statistics for this model. This problem is solved by a special infill asymptotics concept. We use this concept to prove consistency and asymptotic normality of the estimates. Furthermore, the phase estimate is compared to the Hilbert transform in a simulation.