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Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology.

ABSTRACT: Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process.

SUBMITTER: Huang JC

PROVIDER: S-EPMC5458819 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

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Project description:An aging global population is accelerating the need for better, longer-lasting orthopaedic and dental implants. Additive manufacturing can provide patient-specific, titanium-alloy-based implants with tailored, three-dimensional, bone-like architecture. Studies using two-dimensional substrates have demonstrated that osteoblastic differentiation of bone marrow stromal cells (MSCs) is enhanced on surfaces possessing hierarchical macro/micro/nano-scale roughness that mimics the topography of osteoclast resorption pits on the bone surface. Conventional machined implants with these surfaces exhibit successful osseointegration, but the complex architectures produced by 3D printing make consistent nanoscale surface texturing difficult to achieve, and current line-of-sight methods used to roughen titanium alloy surfaces cannot reach all internal surfaces. Here, we demonstrate a new, non-line-of-sight, gas/solid-reaction-based process capable of generating well-controlled nanotopographies on all open (gas-exposed) surfaces of titanium alloy implants. Dense 3D-printed titanium-aluminum-vanadium (TiAl6V4) substrates were used to evaluate the evolution of surface nanostructure for development of this process. Substrates were either polished to be smooth (for easier evaluation of surface nanostructure evolution) or grit-blasted and acid-etched to present a microrough biomimetic topography. An ultrathin (90 ± 16 nm) conformal, titania-based surface layer was first formed by thermal oxidation (600 °C, 6 h, air). A calciothermic reduction (CaR) reaction (700 °C, 1 h) was then used to convert the surface titania (TiO2) into thin layers of calcia (CaO, 77 ± 16 nm) and titanium (Ti, 51 ± 20 nm). Selective dissolution of the CaO layer (3 M acetic acid, 40 min) then yielded a thin nanoporous/nanorough Ti-based surface layer. The changes in surface nanostructure/chemistry after each step were confirmed by scanning and transmission electron microscopies with energy-dispersive X-ray analysis, X-ray diffraction, selected area electron diffraction, atomic force microscopy, and mass change analyses. In vitro studies indicated that human MSCs on CaR-modified microrough surfaces exhibited increased protein expression associated with osteoblast differentiation and promoted osteogenesis compared to unmodified microrough surfaces (increases of 387% in osteopontin, 210% in osteocalcin, 282% in bone morphogenic protein 2, 150% in bone morphogenic protein 4, 265% in osteoprotegerin, and 191% in vascular endothelial growth factor). This work suggests that this CaR-based technique can provide biomimetic topography on all biologically facing surfaces of complex, porous, additively manufactured TiAl6V4 implants.

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Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology.

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