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Ultra-smooth nanostructured diamond (USND) can be applied to greatly increase the wear resistance of orthopaedic implants over conventional designs. Herein we describe surface modification techniques and cytocompatibility studies performed on this new material. We report that hydrogen (H)-terminated USND surfaces supported robust mesenchymal stem cell (MSC) adhesion and survival, while oxygen- (O) and fluorine (F)-terminated surfaces resisted cell adhesion, indicating that USND can be modified to either promote or prevent cell/biomaterial interactions. Given the favorable cell response to H-terminated USND, this material was further compared with two commonly used biocompatible metals, titanium alloy (Ti-6Al-4V) and cobalt chrome (CoCrMo). MSC adhesion and proliferation were significantly improved on USND compared with CoCrMo, although cell adhesion was greatest on Ti-6Al-4V. Comparable amounts of the pro-adhesive protein, fibronectin, were deposited from serum on the three substrates. Finally, MSCs were induced to undergo osteoblastic differentiation on the three materials, and deposition of a mineralized matrix was quantified. Similar amounts of mineral were deposited onto USND and CoCrMo, whereas mineral deposition was slightly higher on Ti-6Al-4V. When coupled with recently published wear studies, these in vitro results suggest that USND has the potential to reduce debris particle release from orthopaedic implants without compromising osseointegration.

Measuring the spatial variation of residual stresses often requires the solution of an elastic inverse problem such as a Volterra equation. Using a maximum likelihood estimate (least squares fit), a series expansion for the spatial distribution of stress or underlying eigenstrain can be an effective solution. Measurement techniques that use a series expansion inverse include incremental slitting (crack compliance), incremental hole drilling, a modified Sach’s method, and others. This paper presents a comprehensive uncertainty analysis and order selection methodology, with detailed development for the slitting method. For the uncertainties in the calculated stresses caused by errors in the measured data, an analytical formulation is presented which includes the usually ignored but important contribution of covariances between the fit parameters. Using Monte Carlo numerical simulations, it is additionally demonstrated that accurate uncertainty estimates require the estimation of model error, the ability of the chosen series expansion to fit the actual stress variation. An original method for estimating model error for a series expansion inverse solution is presented. Finally, it is demonstrated that an optimal order for the series expansion can usually be chosen by minimizing the estimated uncertainty in the calculated stresses.

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